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\chapter{Results and Analysis}
\label{yyyyy}

\section{Evaluation Metrics}
\label{Ch7: Background}

The effectiveness and reliability of steganographic systems are commonly evaluated based on four main criteria: imperceptibility, security, capacity, and robustness. Evaluating the proposed approach using these factors provides a comprehensive understanding of its efficiency, reliability, and suitability for practical applications.

\begin{itemize}
    \item \textbf{Imperceptibility}: This metric indicates how effectively a steganographic technique can conceal data within a cover medium without degrading its visual quality. The objective is to embed data in a manner that remains undetectable to human perception and automated detection algorithms, making it difficult for potential attackers to identify the presence of hidden information. Imperceptibility is essential for maintaining the natural appearance of the cover medium and ensuring that the steganographic communication remains discreet.
    
    \item \textbf{Security}: This criterion focuses on protecting the presence and content of concealed data within the stego medium from unauthorized detection or disclosure. The primary goal is to preserve the confidentiality of the embedded information and prevent extraction by adversaries. Security is crucial for safeguarding privacy and resisting malicious analysis or manipulation attempts.
    
    \item \textbf{Capacity}: Capacity denotes the amount of data (in bits) that can be embedded within a cover medium while preserving imperceptibility. The objective is to maximize the hidden payload without introducing noticeable artifacts or raising suspicion.
    
    \item \textbf{Robustness}: Robustness refers to the system’s ability to accurately extract the hidden data when the stego medium undergoes intentional or unintentional modifications. The goal is to ensure reliable data recovery despite common image processing operations or attacks that may alter the stego medium.
\end{itemize}

\subsection{Imperceptibility Metrics}

Imperceptibility is typically evaluated by comparing pixel values of the cover image with those of the corresponding stego image. Several commonly used metrics for assessing imperceptibility are described below.

\subsubsection{Mean Square Error (MSE)}
\label{}

\textbf{MSE} is a metric that indicates the average squared difference between the pixel values of the original cover image and the stego image. It measures the degree to which the stego image deviates from the original image. A lower MSE value implies that the modifications introduced by the steganographic embedding are less noticeable and have minimal impact on visual appearance. Therefore, lower MSE values are preferred when evaluating imperceptibility. The MSE is computed using Equation~5.1 for color images and Equation~5.2 for grayscale images. The value of MSE typically lies between 0 and 1, where values closer to zero indicate better imperceptibility~\cite{wani2023,duan2020}. Here, $x$, $y$, and $z$ represent pixel indices and color channels for color images, while $i$ and $j$ represent pixel indices for grayscale images.

\noindent MSE for a color image with $L$ layers:
\begin{equation}
\text{MSE} = \frac{1}{WH} \sum_{x=1}^{W} \sum_{y=1}^{H} \sum_{z=1}^{L} (I_{xyz} - I'_{xyz})^2
\end{equation}

where:
\begin{align*}
    & \text{MSE} : \text{Mean Squared Error} \\
    & W, H, L : \text{Image dimensions} \\
    & I(x,y,z) : \text{Pixel value of the original image} \\
    & I'(x,y,z) : \text{Pixel value of the stego image}
\end{align*}

\noindent MSE for a grayscale image:
\begin{equation}
    \text{MSE} = \frac{1}{mn} \sum_{i=1}^{m} \sum_{j=1}^{n} (I(i,j) - K(i,j))^2
\end{equation}

where:
\begin{align*}
    & \text{MSE} : \text{Mean Squared Error} \\
    & m, n : \text{Image dimensions} \\
    & I(i,j) : \text{Pixel value of the original image} \\
    & K(i,j) : \text{Pixel value of the stego image}
\end{align*}

\subsubsection{Peak Signal-to-Noise Ratio (PSNR)}
\label{}

\textbf{RMSE} is a derivative of MSE in which the square root of the MSE is computed to measure the average magnitude of errors between the original and stego images. Lower RMSE values indicate smaller deviations between the two images and, consequently, better imperceptibility~\cite{plachta2022,Himthani2022}. RMSE is calculated as shown in Equation~5.3:
\begin{equation}
    \text{RMSE} = \sqrt{\text{MSE}}
\end{equation}
The RMSE value typically ranges between 0 and 1, with values closer to zero indicating better image quality.

\textbf{PSNR} evaluates how closely the stego image resembles the cover image and is expressed in decibels (dB). It quantifies the level of noise introduced by the embedding process. As the embedding payload increases, PSNR generally decreases due to increased distortion. PSNR values above 40~dB indicate high image quality, whereas values below 30~dB suggest noticeable degradation~\cite{plachta2022,Himthani2022}. PSNR is computed using the logarithmic relationship with MSE, as shown in Equation~4.5:
\begin{equation}
\text{PSNR} = 10 \log_{10}\left(\frac{I_{\text{max}}^2}{\text{MSE}}\right)
\end{equation}

Here, $I_{\text{max}}$ denotes the maximum possible pixel value of the image. For 8-bit grayscale images, $I_{\text{max}}$ is typically 255. If a lower maximum value is used, PSNR accuracy may be affected.

\subsubsection{Structural Similarity Index (SSIM)}
\label{}

\textbf{SSIM} is a perceptually motivated metric that provides a more comprehensive assessment of image fidelity than MSE and PSNR. It evaluates image similarity based on luminance, contrast, and structural information, which are closely aligned with human visual perception. SSIM is particularly effective at detecting structural changes that may not be apparent through PSNR alone. SSIM values close to 1 indicate high similarity between images, whereas values close to $-1$ indicate significant dissimilarity~\cite{chinniyan2022}. High PSNR values do not necessarily imply high SSIM, as structural distortions may still exist. SSIM is computed using Equation~5.5:
\begin{equation}
    \text{SSIM}(I, K) = \frac{{(2\mu_I\mu_K + C_1)(2\sigma_{IK} + C_2)}}{{(\mu_I^2 + \mu_K^2 + C_1)(\sigma_I^2 + \sigma_K^2 + C_2)}}
\end{equation}

where:
\begin{align*}
    & I, K : \text{Two images under comparison} \\
    & \mu_I, \mu_K : \text{Mean intensities of images $I$ and $K$} \\
    & \sigma_I, \sigma_K : \text{Standard deviations of $I$ and $K$} \\
    & \sigma_{IK} : \text{Covariance between $I$ and $K$} \\
    & C_1, C_2 : \text{Stabilization constants}
\end{align*}

\subsubsection{Image Fidelity (IF)}

\textbf{IF} measures the degree of similarity between two images by quantifying pixel-level deviations. Values closer to 1 indicate higher similarity between the cover and stego images~\cite{chinniyan_image_2022,zou2019}. IF is computed as shown in Equation~5.6:
\begin{equation}
    \text{IF}(I, K) = \frac{1}{N} \sum_{i=1}^{N} \left(1 - \frac{|I_i - K_i|}{\max(I_i, K_i)}\right)
\end{equation}

where:
\begin{align*}
    & N : \text{Total number of pixels} \\
    & I_i, K_i : \text{Pixel values at position $i$ in images $I$ and $K$}
\end{align*}

\subsection{Capacity Metrics}

\subsubsection{Payload Capacity (PC)}
\label{}

\textbf{PC} measures the total amount of data that can be embedded within a cover image using the proposed steganographic method and depends primarily on the cover image size and embedding strategy~\cite{chakraborty2020,SETIADI2023}. Payload capacity is calculated using Equation~5.7:
\begin{equation}
    \text{Payload Capacity} = {\text{T} \times N \times 8}
\end{equation}

where:
\begin{align*}
    & \text{T} : \text{Total number of pixels in the cover image} \\
    & N : \text{Bytes per pixel (1 for grayscale, 3 for color images)}
\end{align*}

\subsubsection{Bits Per Pixel (BPP)}
\label{}

\textbf{BPP} represents the average number of bits embedded per pixel across the entire image~\cite{KUMAR2023,sabah_abdulazeez2023}. It is computed by dividing the payload capacity (in bits) by the total number of pixels, as shown in Equation~5.8:
\begin{equation}
    \text{BPP} = \frac{\text{Payload Capacity (bits)}}{N}
\end{equation}

where:
\begin{align*}
    & \text{BPP} : \text{Bits Per Pixel} \\
    & \text{Payload Capacity (bits)} : \text{Total embedded payload in bits} \\
    & N : \text{Total number of pixels in the image}
\end{align*}


\subsection{Security Metrics}

\subsubsection{Xu-Net Model}
The convolutional neural network architecture Xu-Net serves as a strong baseline for image steganalysis, as it was originally designed for this purpose. Its design emphasizes suppressing dominant image content while enhancing the detection of weak embedding traces. In the preprocessing stage, Xu-Net applies fixed residual filters or learnable shallow filters to attenuate low-frequency image components, thereby revealing noise-like patterns introduced by data hiding. Subsequent convolutional stages perform sequential feature extraction using normalization and nonlinear activation functions to capture statistical differences between cover images and their steganographic counterparts. Practical Xu-Net variants typically incorporate steganalysis-specific operations, including restricted convolutions with small receptive fields, absolute-value or truncation layers to obtain sign-invariant residuals, batch normalization to improve training stability, and pooling/striding to aggregate local residual statistics before a compact classifier produces the final decision. Overall, Xu-Net-style models achieve strong end-to-end performance when trained on paired cover--stego datasets and demonstrate both effective steganalytic feature learning and practical detection capability in comparative evaluations~\cite{sedighi2016,Kumar2024}.

\subsubsection{Ye-Net Model}
The convolutional neural network architecture Ye-Net is designed to detect embedded residual patterns for image steganalysis. The model begins with high-pass preprocessing using fixed or learnable residual filters to highlight noise-like artifacts, followed by multiple convolutional blocks with small receptive fields, activation functions, and normalization layers to extract local residual features. Ye-Net commonly employs absolute-value operations and truncation mechanisms to achieve sign-invariant representations while limiting the influence of extreme values. Pooling operations and strided convolutions are then used to aggregate spatial information across the image. Finally, a small set of fully connected layers combines the learned features to perform binary classification between cover and stego images. Ye-Net-style architectures have been widely adopted in comparative evaluation studies and demonstrate effective steganalytic representation learning through end-to-end training on matched cover--stego pairs~\cite{Lin2024,Patwari2025}.

\subsection{Robustness Metrics}
To measure the robustness of the proposed system against attacks, each attacked stego image is passed to the revealing network to recover the hidden information after the attack is applied. The revealing network is responsible for decoding the embedded payload from the attacked stego image. Its performance is then evaluated to determine how reliably it can extract the hidden information under different attack conditions. Therefore, the revealing network serves as an indicator of the overall robustness of the steganographic system.

\subsubsection{Error Rate of Extraction (Re)}
\textbf{Re} is a metric used to evaluate extraction reliability in robust steganography by quantifying the number of bit errors in the extracted message. As the error rate approaches 0, extraction accuracy increases; when $Re = 0$, extraction is perfect~\cite{Sharma2024,Verma2024}. This metric is computed using Equation~5.9:
\begin{equation}
Re = \frac{Ne}{Lmessage}
\end{equation}
In the above equation, $Ne$ denotes the number of erroneous message bits, and $Lmessage$ denotes the total number of message bits. Several studies~\cite{Sharma2024,Verma2024} also use additional metrics (e.g., PSNR, MSE, and SSIM) to assess robustness under distortion.

\subsubsection{BER}
BER measures the ratio of incorrectly received bits to the total number of transmitted (or extracted) bits. It reflects the average disagreement rate between the embedded bitstream and the recovered bitstream, indicating the reliability and accuracy of communication and steganographic extraction processes~\cite{Panigrahi2025,Ray2021}. BER is computed using Equation~5.10:
\begin{equation}
\mathrm{BER} = \frac{1}{N}\sum_{i=1}^{N} \mathbf{1}\{b_i \neq \hat{b}_i\}
\label{eq:BER}
\end{equation}
The indicator function $\mathbf{1}\{\cdot\}$ evaluates to 1 when the transmitted bit $b_i$ differs from the recovered bit $\hat{b}_i$, and $N$ denotes the total number of bits.

\subsection{Robustness Attacks}
Digital media platforms are subject to various perturbations and geometric distortions; therefore, image steganography systems require a high extraction rate to remain effective under such conditions. Robustness is assessed by the ability to recover embedded data from attacked stego images with minimal degradation. The attacks considered in this work include JPEG compression (quality factor $Q$ from 10 to 90), Gaussian noise (variance $\sigma$ of 0.001 and 0.005), and salt-and-pepper noise (density parameter reported as $\sigma = 0.001$).

\subsubsection{JPEG Compression Attack}
JPEG compression is applied to reduce image size by encoding the image using quality factors ranging from 10 to 90. As JPEG compression increases, image quality decreases because information is lost during quantization, leading to a loss of fine details. To assess robustness against JPEG compression, varying quality factors are used to examine how well the hidden information can be recovered. In typical JPEG settings, the quality factor ranges from 10 to 100: higher values correspond to lower compression and better visual quality, while lower values correspond to higher compression and greater distortion. Thus, the quality factor controls the trade-off between file size reduction and image fidelity~\cite{Setiadi2024,Liu2024}.

\subsubsection{Gaussian Noise Attack}
Gaussian noise is applied by adding random values sampled from a Gaussian distribution with variance in the range 0.001 to 0.005. This type of noise is commonly used to model random sensor and transmission perturbations and can introduce a grainy appearance that degrades image quality and fidelity. In Gaussian noise, the variance parameter controls the noise strength: higher variance produces noisier images with larger intensity fluctuations, whereas lower variance yields milder perturbations~\cite{Wang2025,Abdulhammed2024}.

\subsubsection{Salt-and-Pepper Noise Attack}
Salt-and-pepper noise is applied by randomly selecting pixels and setting their intensities to either 0 (pepper, the minimum intensity) or 255 (salt, the maximum intensity). In this work, the noise level is reported using a parameter $\sigma = 0.001$. Increasing salt density introduces more bright pixels, whereas increasing pepper density introduces more dark pixels. By adjusting these densities, the visual impact and spatial distribution of the noise can be controlled. Unlike Gaussian noise, salt-and-pepper noise does not follow a Gaussian distribution; therefore, variance is not an intrinsic parameter of this noise model. Instead, it is typically characterized by salt and pepper densities, which determine the proportion of pixels corrupted by the noise~\cite{Jasim2024,Tahbaz2024}.


\section{Edge Detectors Results Analysis}

In this section, we analyze and compare the performance of different edge detection algorithms employed within the proposed steganographic framework. The primary objective is to evaluate how each edge detector—particularly HED and RCF—performs in identifying accurate object boundaries while preserving image structure and texture during both embedding and extraction. To ensure a fair comparison, the experimental design maintains all components of the steganography system constant, varying only the edge detection model. The same datasets (BOSSbase and USC-SIPI) and embedding configurations (fixed-threshold and GA-optimized modes) are used across all experiments.

For each edge detector, payloads are embedded and extracted at predefined target BPP values, and multiple performance indicators are measured. These include PSNR, SSIM, MSE, and IF to assess image quality, as well as TimeE (embedding time) and TimeX (extraction time) to evaluate computational efficiency. When applicable, the achieved embedding rate (BPP\textsubscript{used}) is also reported. This evaluation directly supports the main research objective of identifying the edge detection model that provides the optimal balance among visual quality, robustness, and processing efficiency for integration into the proposed steganographic system.

\noindent
The experimental setup presented in the following tables follows a full factorial design that examines the influence of two binary factors—attention-map guidance (e.g., RCF, HED, VGG16, ResNet50) and Genetic Algorithm (GA) optimization—on steganographic performance under three target embedding rates, $\mathrm{BPP} \in \{0.100,\,0.200,\,0.400\}$. Each factor has two states: \texttt{attention map = True} indicates that the system employs edge- or texture-guided embedding based on the corresponding attention maps, whereas \texttt{attention map = False} denotes the use of a fallback attention strategy. Similarly, \texttt{GA = True} indicates that genetic optimization is applied to tune embedding parameters, while \texttt{GA = False} denotes embedding without optimization.

This design results in four experimental conditions for each BPP level:  
(1) attention map enabled with GA enabled (\textit{True}, \textit{True});  
(2) attention map enabled with GA disabled (\textit{True}, \textit{False});  
(3) attention map disabled with GA enabled (\textit{False}, \textit{True}); and  
(4) both attention map and GA disabled (\textit{False}, \textit{False}).  

For each configuration, the system records quantitative measures of embedding and extraction quality (PSNR, SSIM, MSE, IF), the effective embedding rate ($\mathrm{BPP}_{\mathrm{used}}$), and computational performance (TimeE and TimeX). Repeating these configurations across the three target BPP values allows the study to isolate and compare the individual and combined effects of attention guidance, evolutionary optimization, and payload capacity on system behavior. In subsequent subsections, after analyzing imperceptibility and capacity, security performance is evaluated using Xu-Net and Ye-Net steganalysis models.

\subsection{Imperceptibility and Capacity Results with RCF and CNN}

In this experiment, the objective is to investigate how the use of \textbf{RCF} (edge-based feature extraction) and \textbf{GA} (Genetic Algorithm optimization) affects the performance of the steganographic system. The experimental design presented in Table~\ref{tab:rcf_secret64_bpp_grid20} follows a \textit{factorial experimental setup}, in which two binary factors (RCF and GA) are varied under three target payload levels expressed in bits per pixel (BPP) = 0.100, 0.200, and 0.400.

\smallskip
\noindent
The table columns represent both the \textbf{experimental factors} and the \textbf{measured performance metrics}. The first three columns (\textit{RCF}, \textit{GA}, and BPP\textsubscript{tg}) correspond to the independent variables that are intentionally varied during the experiments. The remaining columns report the measured outcomes, including:

\begin{itemize}
    \item \textbf{BPP\textsubscript{used}} – the actual embedding rate achieved by the system;
    \item \textbf{PSNR\textsubscript{c→s}}, \textbf{SSIM\textsubscript{c→s}}, \textbf{MSE\textsubscript{c→s}}, and \textbf{IF\textsubscript{c→s}} – quality metrics comparing the cover and stego images, reflecting the degree of visual distortion introduced by embedding;
    \item \textbf{PSNR\textsubscript{s→x}} and \textbf{SSIM\textsubscript{s→x}} – metrics evaluating the accuracy and fidelity of secret image recovery;
    \item \textbf{TimeE} and \textbf{TimeX} – embedding and extraction times, respectively, used to assess computational efficiency.
\end{itemize}

\noindent
By varying the two binary factors (\texttt{RCF = True/False} and \texttt{GA = True/False}) across the three target BPP levels, the experiment systematically examines how edge-based guidance and genetic optimization influence embedding fidelity, capacity utilization, and extraction performance. In essence, the table summarizes how different combinations of feature guidance and optimization affect the quality, capacity, and efficiency of the steganographic process. The primary distinction between this table and subsequent tables lies in the specific edge or texture detector used to generate the attention maps in each case.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % تقليل الفراغ الأفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Result Analysis Using RCF and CNN for secret image size $64\times64$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:rcf_secret64_bpp_grid20}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{RCF} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.096 & 0.058 \\ \hline
True  & False & 0.100 & 0.0992 & 57.43 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.101 & 0.036 \\ \hline
False & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.042 & 0.027 \\ \hline
False & False & 0.100 & 0.0992 & 57.49 & 0.9987 & 0.000002 & 0.9960 & $\infty$ & 1.0000 & 0.052 & 0.016 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.049 & 0.030 \\ \hline
True  & False & 0.200 & 0.1250 & 56.51 & 0.9983 & 0.000002 & 0.9955 & 156.28 & 1.0000 & 0.030 & 0.019 \\ \hline
False & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.132 & 0.075 \\ \hline
False & False & 0.200 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.078 & 0.047 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.076 & 0.030 \\ \hline
True  & False & 0.400 & 0.1250 & 56.51 & 0.9983 & 0.000002 & 0.9955 & 156.28 & 1.0000 & 0.029 & 0.021 \\ \hline
False & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28  & 1.0000 & 0.077 & 0.031 \\ \hline
False & False & 0.400 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.029 & 0.018 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup

\noindent Table~\ref{tab:rcf_secret64_bpp_grid20} indicates that the Genetic Algorithm (GA) is the primary factor responsible for maintaining high cover-image fidelity, as evidenced by the quantitative results, whereas RCF mainly contributes to increased embedding capacity without introducing additional distortion when GA is enabled.At a target embedding rate of $\mathrm{BPP}{\mathrm{tg}} = 0.1$ (with $\mathrm{BPP}{\mathrm{used}} = 0.0992$), enabling GA increases $\mathrm{PSNR}_{\text{cover}\rightarrow\text{stego}}$ from approximately $57.43$–$57.49,\mathrm{dB}$ (GA disabled, RCF enabled or disabled) to $61.17,\mathrm{dB}$ (GA enabled, regardless of RCF). At the same time, $\mathrm{SSIM}$ increases to $0.9993$, $\mathrm{IF}$ rises to $0.9974$, and $\mathrm{MSE}$ is reduced to approximately $10^{-6}$.Similar performance trends are observed at higher payload targets. When GA is enabled, $\mathrm{PSNR}$ reaches approximately $60,\mathrm{dB}$, corresponding to a gain of $3.6$–$3.8,\mathrm{dB}$ compared to configurations without GA, along with gradual improvements in $\mathrm{SSIM}$ and $\mathrm{IF}$. The system achieves a maximum effective embedding rate of $\mathrm{BPP}{\mathrm{used}} = 0.125$ once the target rate $\mathrm{BPP}{\mathrm{tg}}$ reaches $0.2$ or higher, as the size of the secret image limits the achievable payload.Secret recovery remains perfect under all experimental conditions, with $\mathrm{SSIM}{\text{secret}\rightarrow\text{extracted}} = 1.0000$ and $\mathrm{PSNR}{\text{secret}\rightarrow\text{extracted}} = \infty$ at $\mathrm{BPP}{\mathrm{tg}} = 0.1$, and approximately $156.3,\mathrm{dB}$ at $\mathrm{BPP}{\mathrm{tg}} \in {0.2, 0.4}$. All operations are completed within one second: embedding requires $0.03$–$0.13,\mathrm{s}$, while extraction requires $0.02$–$0.08,\mathrm{s}$.Overall, these results demonstrate that GA significantly enhances cover-image fidelity, whereas RCF primarily provides additional embedding capacity, which remains largely unused in this secret-size–constrained experimental setting.



% في الـ preamble يُفضّل:
% \usepackage{graphicx}
% \usepackage{array}

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and CNN for secret image size $128\times128$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:rcf_secret128_bpp_grid21}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{RCF} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.040 & 0.023 \\ \hline
True  & False & 0.100 & 0.0992 & 57.39 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.024 & 0.016 \\ \hline
False & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.040 & 0.024 \\ \hline
False & False & 0.100 & 0.0992 & 57.37 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.023 & 0.015 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.074 & 0.043 \\ \hline
True  & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.045 & 0.025 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.073 & 0.044 \\ \hline
False & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.044 & 0.027 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.406 & 0.217 \\ \hline
True  & False & 0.400 & 0.3966 & 51.29 & 0.9942 & 0.000007 & 0.9917 & $\infty$ & 1.0000 & 0.190 & 0.119 \\ \hline
False & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.148 & 0.091 \\ \hline
False & False & 0.400 & 0.3966 & 51.49 & 0.9944 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.084 & 0.052 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:rcf_secret128_bpp_grid21} demonstrates that the Genetic Algorithm (GA) has the most significant impact on maintaining cover-image fidelity, as evidenced by the ablation results, whereas RCF exhibits minimal influence once GA is enabled. This table reports experimental results obtained using a secret image of size $128 \times 128$.At a target embedding rate of $\mathrm{BPP}_{\mathrm{tg}} = 0.1$, activating GA yields a cover-to-stego PSNR of $61.23\,\mathrm{dB}$, representing an improvement of approximately $3.8\,\mathrm{dB}$ over the baseline range of $57.37$–$57.39\,\mathrm{dB}$ observed without GA. Enabling GA also results in an increase of $0.0004$ in SSIM and $0.0005$ in IF. As the target embedding rate increases, the cover-to-stego PSNR reaches $58.25\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.2$ and $55.15\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.4$.The increase in the effective embedding rate $\mathrm{BPP}_{\mathrm{used}}$ from $0.0992$ to $0.1953$ and $0.3966$ results in an approximate $3\,\mathrm{dB}$ reduction in cover-image quality at each doubling step. Nevertheless, GA consistently maintains a $3.6$–$3.9\,\mathrm{dB}$ advantage over fixed bit-allocation strategies across all payload levels.Secret image recovery is exact at the byte level for all tested configurations, as indicated by $\mathrm{SSIM}_{\text{secret}\rightarrow\text{extracted}} = 1.0000$ and $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}} = \infty$. The embedding and extraction times range from $0.02$ to $0.41\,\mathrm{s}$ and from $0.02$ to $0.22\,\mathrm{s}$, respectively, with processing duration primarily determined by payload size.Overall, these results confirm that the GA-based allocator provides consistent and reliable improvements in cover-image quality across all payload sizes, whereas the Random Capacity Filler (RCF) mainly contributes unused capacity when GA governs the allocation process.



% يفضّل في الـ preamble:
% \usepackage{graphicx}
% \usepackage{array}

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and CNN for secret image size $512\times512$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:rcf_secret512_bpp_grid12}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{RCF} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.147 & 0.071 \\ \hline
True  & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.039 & 0.018 \\ \hline
False & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.039 & 0.023 \\ \hline
False & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.026 & 0.035 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.079 & 0.045 \\ \hline
True  & False & 0.200 & 0.1953 & 54.53 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.045 & 0.027 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.075 & 0.043 \\ \hline
False & False & 0.200 & 0.1953 & 54.56 & 0.9973 & 0.000003 & 0.9943 & $\infty$ & 1.0000 & 0.044 & 0.055 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.152 & 0.087 \\ \hline
True  & False & 0.400 & 0.3966 & 51.27 & 0.9942 & 0.000007 & 0.9917 & $\infty$ & 1.0000 & 0.079 & 0.052 \\ \hline
False & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.150 & 0.088 \\ \hline
False & False & 0.400 & 0.3966 & 51.47 & 0.9943 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.081 & 0.049 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup



Table~\ref{tab:rcf_secret512_bpp_grid12} shows that the Genetic Algorithm (GA) is the dominant factor influencing cover-image fidelity for a $512 \times 512$ secret image, whereas RCF has no noticeable impact when GA is enabled. Activating GA yields a cover-to-stego PSNR of $61.20\,\mathrm{dB}$ at a target embedding rate of $\mathrm{BPP}_{\mathrm{tg}} = 0.1$ with $\mathrm{BPP}_{\mathrm{used}} = 0.0992$, while the PSNR remains at approximately $57.46\,\mathrm{dB}$ when both GA and RCF are disabled. Enabling GA also results in improved $\mathrm{SSIM}$ and $\mathrm{IF}$ values, along with reduced $\mathrm{MSE}$, indicating enhanced visual fidelity.Across all three evaluated payload levels, the PSNR values exhibit consistent improvements when GA is applied, regardless of whether RCF is enabled or disabled. Specifically, the application of GA increases PSNR by approximately $3.6$–$3.9\,\mathrm{dB}$ across the tested BPP levels. As the effective embedding rate doubles from $0.0992$ to $0.1953$ and subsequently to $0.3966$, the cover-image PSNR decreases by approximately $3\,\mathrm{dB}$ at each step. Nevertheless, the $\mathrm{SSIM}$ remains high, exceeding $0.9974$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.1$ and remaining above $0.9970$ at higher payload levels.The system operates at sub-second execution times for both embedding and extraction, with processing time increasing slightly as payload size grows. Overall, these results confirm that the GA-based allocator consistently delivers measurable improvements in cover-image quality across all payload levels, whereas the RCF algorithm contributes minimal additional capacity when GA governs the embedding process.





\subsection{Imperceptibility \& Capacity Results With HED \& CNN}

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and CNN for secret image size $64\times64$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:hed_secret64_bpp_grid1}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.255 & 0.142 \\ \hline
True  & False & 0.100 & 0.0992 & 56.96 & 0.9985 & 0.000002 & 0.9957 & $\infty$ & 1.0000 & 0.133 & 0.097 \\ \hline
False & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.097 & 0.056 \\ \hline
False & False & 0.100 & 0.0992 & 57.49 & 0.9987 & 0.000002 & 0.9960 & $\infty$ & 1.0000 & 0.059 & 0.034 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.117 & 0.070 \\ \hline
True  & False & 0.200 & 0.1250 & 55.88 & 0.9981 & 0.000003 & 0.9951 & 156.28 & 1.0000 & 0.074 & 0.042 \\ \hline
False & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.048 & 0.028 \\ \hline
False & False & 0.200 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.028 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.116 & 0.070 \\ \hline
True  & False & 0.400 & 0.1250 & 55.88 & 0.9981 & 0.000003 & 0.9951 & 156.28 & 1.0000 & 0.070 & 0.042 \\ \hline
False & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.049 & 0.030 \\ \hline
False & False & 0.400 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.030 & 0.019 \\ \hline
\end{tabular}
}
\end{table}
\endgroup

In this section, the same experimental design described in the previous section is employed; however, imperceptibility and capacity are evaluated using HED-based attention in combination with a CNN. Table~\ref{tab:hed_secret64_bpp_grid1} shows that the HED-based attention system combined with the Genetic Algorithm (GA) achieves the highest cover-image fidelity for a $64 \times 64$ secret image. Nevertheless, the results clearly indicate that GA is the dominant contributor to performance improvements, rather than HED alone.At a target embedding rate of $\mathrm{BPP}_{\mathrm{tg}} = 0.1$ (with $\mathrm{BPP}_{\mathrm{used}} = 0.0992$), enabling GA yields a $\mathrm{PSNR}_{\text{cover}\rightarrow\text{stego}}$ value of $61.17\,\mathrm{dB}$, along with $\mathrm{SSIM}$ and $\mathrm{IF}$ values of $0.9993$ and $0.9974$, respectively, while the $\mathrm{MSE}$ is reduced by approximately half. A similar PSNR advantage of $3.6$–$3.8\,\mathrm{dB}$ persists at higher target embedding rates of $\mathrm{BPP}_{\mathrm{tg}} = 0.2$ and $0.4$, where $\mathrm{BPP}_{\mathrm{used}}$ reaches a saturation value of $0.1250$. In these cases, GA improves $\mathrm{PSNR}_{\text{cover}\rightarrow\text{stego}}$ from approximately $55.88$–$56.37\,\mathrm{dB}$ to $60.15\,\mathrm{dB}$, with corresponding increases in $\mathrm{SSIM}$ and $\mathrm{IF}$.The effective embedding rate $\mathrm{BPP}_{\mathrm{used}}$ saturates at $0.125$ once $\mathrm{BPP}_{\mathrm{tg}}$ reaches $0.2$ or higher because a $64 \times 64$ grayscale secret image contains $32{,}768$ bits, which corresponds to $0.125$ bits per pixel when embedded into a $512 \times 512$ cover image. Consequently, the secret image size becomes the limiting factor, and increasing the target BPP from $0.2$ to $0.4$ does not lead to further degradation in cover-image quality. Although HED provides additional embedding capacity, this extra capacity remains unused under the secret-size constraint.When GA is disabled, the PSNR values decrease by approximately $0.4$–$0.5\,\mathrm{dB}$ across the tested configurations. Secret reconstruction remains perfectly lossless in all scenarios, as indicated by $\mathrm{SSIM}_{\text{secret}\rightarrow\text{extracted}} = 1.0000$ and $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}} = \infty$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.1$, and approximately $156.28\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}} \geq 0.2$.In inclusion of HED in conjunction with GA results in longer embedding and extraction times. At $\mathrm{BPP}_{\mathrm{tg}} = 0.1$, embedding and extraction require approximately $0.255\,\mathrm{s}$ and $0.142\,\mathrm{s}$, respectively, compared to $0.097\,\mathrm{s}$ and $0.056\,\mathrm{s}$ when HED is not used. Despite this increase, the total processing time remains below one second. Overall, these results demonstrate that GA serves as the primary mechanism for controlling image quality, while HED mainly contributes additional embedding capacity at the cost of increased computational time, without significantly affecting image quality in secret-size--limited scenarios.



\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and CNN for secret image size $128\times128$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:hed_secret128_bpp_grid2}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.097 & 0.061 \\ \hline
True  & False & 0.100 & 0.0992 & 56.94 & 0.9985 & 0.000002 & 0.9957 & $\infty$ & 1.0000 & 0.055 & 0.036 \\ \hline
False & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.038 & 0.023 \\ \hline
False & False & 0.100 & 0.0992 & 57.37 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.024 & 0.015 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.079 & 0.046 \\ \hline
True  & False & 0.200 & 0.1953 & 53.81 & 0.9969 & 0.000004 & 0.9938 & $\infty$ & 1.0000 & 0.041 & 0.025 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.228 & 0.105 \\ \hline
False & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.096 & 0.064 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.176 & 0.085 \\ \hline
True  & False & 0.400 & 0.3966 & 49.73 & 0.9930 & 0.000011 & 0.9901 & $\infty$ & 1.0000 & 0.076 & 0.045 \\ \hline
False & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.158 & 0.094 \\ \hline
False & False & 0.400 & 0.3966 & 51.49 & 0.9944 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.081 & 0.050 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup

Table~\ref{tab:hed_secret128_bpp_grid2} shows that HED-based attention achieves the highest cover-image fidelity for a $128 \times 128$ secret image when combined with the Genetic Algorithm (GA). The results indicate that GA is the primary factor determining performance outcomes, whereas HED mainly influences the available capacity headroom.At a target embedding rate of $\mathrm{BPP}_{\mathrm{tg}} = 0.1$ (with $\mathrm{BPP}_{\mathrm{used}} = 0.0992$), enabling GA increases the $\mathrm{PSNR}_{\text{cover}\rightarrow\text{stego}}$ from $56.94\,\mathrm{dB}$ to $61.23\,\mathrm{dB}$, while simultaneously raising $\mathrm{SSIM}$ to $0.9994$, increasing $\mathrm{IF}$ to $0.9974$, and reducing $\mathrm{MSE}$. Comparable performance improvements are observed at higher target embedding rates of $\mathrm{BPP}_{\mathrm{tg}} = 0.2$ and $\mathrm{BPP}_{\mathrm{tg}} = 0.4$, with $\mathrm{BPP}_{\mathrm{used}} = 0.1953$ and $0.3966$, respectively. Under these conditions, PSNR reaches $58.25\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.2$ and $55.15\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.4$, corresponding to gains of approximately $3.7$–$5.4\,\mathrm{dB}$ when GA is applied.When GA is disabled, PSNR values remain unchanged across configurations, with $61.23\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.1$, and $58.25\,\mathrm{dB}$ and $55.15\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.2$ and $0.4$, respectively. Although the nominal embedding capacity increases when HED is used without GA, the effective embedding rate remains unchanged once GA governs the allocation process.Secret image reconstruction remains lossless in all tested scenarios, as the pipeline adjusts the secret size to match the allocated bit budget, resulting in $\mathrm{SSIM}_{\text{secret}\rightarrow\text{extracted}} = 1.0000$ and $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}} = \infty$. The system operates at sub-second execution times, with processing time increasing slightly as payload size grows (e.g., GA+HED requires approximately $0.097/0.061\,\mathrm{s}$ for embedding/extraction at $\mathrm{BPP}_{\mathrm{tg}} = 0.1$, increasing to $0.176/0.085\,\mathrm{s}$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.4$).Overall, these results demonstrate that perceptual quality improvements are driven primarily by GA in a consistent and predictable manner, while HED mainly contributes additional embedding capacity that remains largely unused under the secret-size constraints.




\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and CNN for secret image size $512\times512$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:hed_secret512_bpp_grid3}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.044 & 0.025 \\ \hline
True  & False & 0.100 & 0.0992 & 56.93 & 0.9985 & 0.000002 & 0.9957 & $\infty$ & 1.0000 & 0.024 & 0.015 \\ \hline
False & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.039 & 0.023 \\ \hline
False & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.025 & 0.014 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.076 & 0.047 \\ \hline
True  & False & 0.200 & 0.1953 & 53.81 & 0.9969 & 0.000004 & 0.9938 & $\infty$ & 1.0000 & 0.086 & 0.062 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.071 & 0.042 \\ \hline
False & False & 0.200 & 0.1953 & 54.56 & 0.9973 & 0.000003 & 0.9943 & $\infty$ & 1.0000 & 0.045 & 0.025 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.142 & 0.084 \\ \hline
True  & False & 0.400 & 0.3966 & 49.73 & 0.9930 & 0.000011 & 0.9901 & $\infty$ & 1.0000 & 0.077 & 0.044 \\ \hline
False & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.322 & 0.082 \\ \hline
False & False & 0.400 & 0.3966 & 51.47 & 0.9943 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.077 & 0.046 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup

Table~\ref{tab:hed_secret512_bpp_grid3} presents the evaluation results for a $512 \times 512$ secret image under three target payload rates of $0.10$, $0.20$, and $0.40$ bits per pixel (BPP), using a $2 \times 2$ ablation design that varies HED-based attention preselection and Genetic Algorithm (GA) optimization. The results clearly indicate that GA is the primary factor influencing cover-image fidelity.The highest cover-to-stego quality is achieved when GA is enabled, with PSNR values of $61.20$, $58.25$, and $55.17\,\mathrm{dB}$ at target payloads of $0.10$, $0.20$, and $0.40$~BPP, respectively. These results correspond to SSIM values of $0.9993$, $0.9987$, and $0.9974$, and MSE values ranging from $10^{-6}$ to $3 \times 10^{-6}$. When GA is disabled, the best PSNR values decrease to $57.46$, $54.56$, and $51.47\,\mathrm{dB}$ at the same payload rates, with corresponding SSIM values of $0.9987$, $0.9973$, and $0.9943$.The PSNR and SSIM values remain nearly unchanged regardless of whether HED is enabled when GA is active, indicating that HED has minimal impact on fidelity under GA-controlled embedding. In contrast, disabling GA results in an increase in distortion of approximately $0.5$–$1.7\,\mathrm{dB}$.Secret image recovery remains perfectly lossless across all tested configurations, as evidenced by $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}} = \infty$ and $\mathrm{SSIM} = 1.0000$. The achieved data transmission rates closely match the specified targets, with minimal underutilization ($\mathrm{BPP}_{\mathrm{used}} = 0.0992$, $0.1953$, and $0.3966$, corresponding to $-0.8\%$, $-2.35\%$, and $-0.85\%$, respectively).Extraction is consistently faster than embedding, with runtime primarily dependent on payload size. HED introduces negligible overhead at low embedding rates but begins to affect runtime at higher rates. For example, at $0.40$~BPP under GA control, extraction requires approximately $0.142\,\mathrm{s}$ with HED enabled, compared to $0.322\,\mathrm{s}$ without HED.Overall, the ablation results confirm that GA is the dominant contributor to improvements in cover--stego fidelity, while all evaluated configurations maintain flawless secret recovery across all payload levels.



\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and GAN for secret image size $64\times64$ at BPP targets 0.100, 0.200, and 0.400 }
\label{tab:hed_secret64_bpp_targets5}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.031 & 0.018 \\ \hline
True  & False & 0.100 & 0.0992 & 56.46 & 0.9986 & 0.000002 & 0.9954 & $\infty$ & 1.0000 & 0.021 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.017 \\ \hline
False & False & 0.100 & 0.0992 & 57.49 & 0.9987 & 0.000002 & 0.9960 & $\infty$ & 1.0000 & 0.019 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.037 & 0.021 \\ \hline
True  & False & 0.200 & 0.1250 & 55.40 & 0.9981 & 0.000003 & 0.9949 & 156.28 & 1.0000 & 0.026 & 0.027 \\ \hline
False & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.039 & 0.023 \\ \hline
False & False & 0.200 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.021 & 0.012 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.037 & 0.021 \\ \hline
True  & False & 0.400 & 0.1250 & 55.40 & 0.9981 & 0.000003 & 0.9949 & 156.28 & 1.0000 & 0.025 & 0.015 \\ \hline
False & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.039 & 0.023 \\ \hline
False & False & 0.400 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.032 & 0.017 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup




\subsection{Imperceptibility \& Capacity Results With RCF \& GAN}
In this section, we perform the same experiment design as with the previous section, but we will measure the imperceptibility and capacity using the RCF and GAN.
Table~\ref{tab:hed_secret64_bpp_targets5} shows that the evaluation of a 64x64 secret key with three payload targets at 0.10, 0.20, and 0.40 bits per pixel demonstrates that the genetic algorithm (GA) produces the best cover-to-stego quality. The best cover-to-stego quality reaches 61.17 dB PSNR at 0.10 bpp and 60.15 dB at the effective 0.125 bpp when using the 0.20/0.40 targets while achieving SSIM values between 0.9992 and 0.9993 and MSE values of approximately $10^{-6}$. The absence of GA results in a 4-5 dB decrease in PSNR values (56.46-56.37 dB) while SSIM values decrease to 0.9981-0.9987. The RCF preselection method produces no difference in PSNR or SSIM values when the GA is active, but it results in a 0.9-1.1 in every scenario because PSNRsecret→extracted equals infinity at 0.10 bpp and reaches a dB increase in distortion when the GA is disabled. The secret reconstruction process maintains perfect lossless data extraction at 156 dB with SSIM=1.0000 at higher targets. The actual payload reaches its maximum at 0.125 BPP for both the 0.20 and 0.40 targets because of capacity and bit-allocation constraints, which prevent further distortion growth. The GA-enhanced configuration maintains stable and low extraction and embedding times between 0.02 and 0.04 seconds, which shows that the genetic algorithm does not introduce significant runtime delays.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and GAN for secret image size $128\times128$ at BPP targets 0.100, 0.200, and 0.400}
\label{tab:hed_secret128_bpp_targets6}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.017 \\ \hline
True  & False & 0.100 & 0.0992 & 56.41 & 0.9986 & 0.000002 & 0.9954 & $\infty$ & 1.0000 & 0.021 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.029 & 0.017 \\ \hline
False & False & 0.100 & 0.0992 & 57.37 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.027 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.059 & 0.033 \\ \hline
True  & False & 0.200 & 0.1953 & 53.03 & 0.9967 & 0.000005 & 0.9932 & $\infty$ & 1.0000 & 0.035 & 0.020 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.060 & 0.032 \\ \hline
False & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.050 & 0.037 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.127 & 0.063 \\ \hline
True  & False & 0.400 & 0.3966 & 48.28 & 0.9914 & 0.000015 & 0.9883 & $\infty$ & 1.0000 & 0.063 & 0.035 \\ \hline
False & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.121 & 0.076 \\ \hline
False & False & 0.400 & 0.3966 & 51.49 & 0.9944 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.061 & 0.036 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:hed_secret128_bpp_targets6} presents the evaluation results for a $128 \times 128$ secret image embedded at three target payload rates of $0.10$, $0.20$, and $0.40$ bits per pixel (BPP). The results demonstrate that the Genetic Algorithm (GA) has a substantially greater influence on controlling cover-to-stego distortion than the $2 \times 2$ ablation of RCF preselection.When GA is enabled, PSNR values remain high at $61.23\,\mathrm{dB}$ for $0.10$~BPP, $58.25\,\mathrm{dB}$ for $\mathrm{BPP}_{\mathrm{used}} = 0.1953$, and $55.15\,\mathrm{dB}$ for $\mathrm{BPP}_{\mathrm{used}} = 0.3966$. Correspondingly, SSIM values range from $0.9974$ to $0.9994$, and MSE values remain between $10^{-6}$ and $3 \times 10^{-6}$.

When GA is disabled, stego-image quality decreases by approximately $4$–$7\,\mathrm{dB}$ across all payload levels ($0.10$~BPP: $56.41\,\mathrm{dB}$; $0.1953$~BPP: $53.03$–$54.50\,\mathrm{dB}$; $0.3966$~BPP: $48.28$–$51.49\,\mathrm{dB}$), accompanied by slight reductions in SSIM. The distortion metrics remain essentially unchanged when RCF is enabled or disabled while GA is active, indicating that RCF has negligible impact under GA-controlled allocation. In contrast, when GA is disabled, enabling RCF results in poorer performance compared to configurations without RCF.Secret image reconstruction remains perfectly lossless in all evaluated scenarios, as indicated by $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}} = \infty$ and $\mathrm{SSIM} = 1.0000$. The effective embedding rates achieved by the system closely match the specified targets, with $\mathrm{BPP}_{\mathrm{used}} = 0.0992$, $0.1953$, and $0.3966$. The GA-enhanced configuration maintains practical computational efficiency, with embedding times increasing gradually from approximately $0.03\,\mathrm{s}$ to $0.12\,\mathrm{s}$ and extraction times from $0.01\,\mathrm{s}$ to $0.08\,\mathrm{s}$ as the payload rate increases.



\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and GAN for secret image size $512\times512$ at BPP targets 0.100, 0.200, and 0.400}
\label{tab:hed_secret512_bpp_targets7}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.017 \\ \hline
True  & False & 0.100 & 0.0992 & 56.40 & 0.9986 & 0.000002 & 0.9954 & $\infty$ & 1.0000 & 0.021 & 0.012 \\ \hline
False & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.017 \\ \hline
False & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.017 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.060 & 0.032 \\ \hline
True  & False & 0.200 & 0.1953 & 53.06 & 0.9967 & 0.000005 & 0.9933 & $\infty$ & 1.0000 & 0.035 & 0.020 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.100 & 0.055 \\ \hline
False & False & 0.200 & 0.1953 & 54.56 & 0.9973 & 0.000003 & 0.9943 & $\infty$ & 1.0000 & 0.055 & 0.031 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.202 & 0.114 \\ \hline
True  & False & 0.400 & 0.3966 & 48.30 & 0.9914 & 0.000015 & 0.9884 & $\infty$ & 1.0000 & 0.124 & 0.069 \\ \hline
False & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.114 & 0.061 \\ \hline
False & False & 0.400 & 0.3966 & 51.47 & 0.9943 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.064 & 0.035 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:hed_secret512_bpp_targets7} presents the evaluation results for a $512 \times 512$ secret image embedded at three target payload rates of $0.10$, $0.20$, and $0.40$ bits per pixel (BPP). The results indicate that the Genetic Algorithm (GA) is the most influential factor in determining cover-to-stego distortion.When GA is enabled, the PSNR reaches $61.20\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.0992$, decreases to $58.25\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.1953$, and further declines to $55.17\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.3966$. Across these payload levels, SSIM values remain high, ranging from $0.9974$ to $0.9993$, while MSE values stay within the range of $10^{-6}$ to $3 \times 10^{-6}$.

When GA is disabled, stego-image quality deteriorates, with PSNR decreasing by approximately $1$--$3\,\mathrm{dB}$ at lower payload rates and by up to $3\,\mathrm{dB}$ at $\mathrm{BPP} = 0.40$ (e.g., from $56.40\,\mathrm{dB}$ to $51.47$--$48.30\,\mathrm{dB}$). Correspondingly, SSIM values decrease and MSE values increase, reaching approximately $1.5 \times 10^{-5}$.RCF has no noticeable effect on distortion when GA is active; however, when GA is disabled, RCF further degrades performance, particularly at higher payload levels (e.g., $48.30\,\mathrm{dB}$ with RCF versus $51.47\,\mathrm{dB}$ without RCF at $\mathrm{BPP}_{\mathrm{used}} = 0.3966$). Secret image reconstruction remains perfectly lossless in all evaluated scenarios, as indicated by $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}} = \infty$ and $\mathrm{SSIM} = 1.0000$.The achieved embedding rates closely match the specified targets at $0.0992$, $0.1953$, and $0.3966\,\mathrm{BPP}$. Embedding and extraction operations maintain short execution times that increase proportionally with the embedding rate, ranging from approximately $0.02$--$0.20\,\mathrm{s}$ for embedding and $0.01$--$0.11\,\mathrm{s}$ for extraction. Overall, the GA-enhanced configuration demonstrates practical applicability through its combination of high visual quality, accurate payload control, and efficient processing times.



\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and GAN for secret image size $64\times64$ at BPP targets 0.100, 0.200, and 0.400}
\label{tab:hed_secret64_bpp_targets_grid8}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 60.54 & 0.9992 & 0.000001 & 0.9972 & $\infty$ & 1.0000 & 0.029 & 0.017 \\ \hline
True  & False & 0.100 & 0.0992 & 57.23 & 0.9987 & 0.000002 & 0.9958 & $\infty$ & 1.0000 & 0.021 & 0.012 \\ \hline
False & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.041 & 0.020 \\ \hline
False & False & 0.100 & 0.0992 & 57.49 & 0.9987 & 0.000002 & 0.9960 & $\infty$ & 1.0000 & 0.017 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 59.75 & 0.9991 & 0.000001 & 0.9969 & 156.28 & 1.0000 & 0.036 & 0.020 \\ \hline
True  & False & 0.200 & 0.1250 & 56.12 & 0.9983 & 0.000002 & 0.9953 & 156.28 & 1.0000 & 0.033 & 0.036 \\ \hline
False & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.037 & 0.020 \\ \hline
False & False & 0.200 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.025 & 0.012 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 59.75 & 0.9991 & 0.000001 & 0.9969 & 156.28 & 1.0000 & 0.071 & 0.020 \\ \hline
True  & False & 0.400 & 0.1250 & 56.12 & 0.9983 & 0.000002 & 0.9953 & 156.28 & 1.0000 & 0.025 & 0.014 \\ \hline
False & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.037 & 0.020 \\ \hline
False & False & 0.400 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.020 & 0.012 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


% \subsection{Imperceptibility \& Capacity Results With HED \& GAN}
\subsection{Imperceptibility \& Capacity Results With HED \& GAN}

In this section, the same experimental design described in the previous section is employed; however, imperceptibility and capacity are evaluated using RCF in combination with a GAN-based attention mechanism. Table~\ref{tab:hed_secret64_bpp_targets_grid8} presents the evaluation results for a $64 \times 64$ secret image embedded at three target payload rates ($0.10$, $0.20$, and $0.40$ bits per pixel).The results demonstrate that the Genetic Algorithm (GA) is the primary factor influencing cover-to-stego distortion. When GA is enabled, the system achieves high cover-to-stego quality, with $\mathrm{PSNR}$ values ranging from $60.54$ to $61.17\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.0992$ and from $59.75$ to $60.15\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.125$. At the same time, $\mathrm{SSIM}$ remains between $0.9991$ and $0.9993$, $\mathrm{IF}$ ranges from $0.9969$ to $0.9974$, and $\mathrm{MSE}$ stays on the order of $10^{-6}$.

When GA is disabled, the system exhibits lower fidelity, with $\mathrm{PSNR}$ values of $57.23$–$57.49\,\mathrm{dB}$ at $0.0992\,\mathrm{BPP}$ and $56.12$–$56.37\,\mathrm{dB}$ at $0.125\,\mathrm{BPP}$. Correspondingly, $\mathrm{SSIM}$ decreases to the range $0.9983$–$0.9987$, and $\mathrm{MSE}$ increases to approximately $2 \times 10^{-6}$. When GA is active, performance remains essentially unchanged with respect to the attention mechanism, indicating that RCF/GAN-based guidance has minimal influence under GA-controlled allocation. In contrast, a slight degradation in performance is observed when GA is disabled.Secret reconstruction remains perfectly lossless in all tested scenarios, with $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}} = \infty$ at $0.10\,\mathrm{BPP}$ and approximately $156\,\mathrm{dB}$ with $\mathrm{SSIM} = 1.0000$ at higher target rates. The system reaches an effective rate limit at $\mathrm{BPP}_{\mathrm{used}} = 0.125$ for the $0.20$ and $0.40$ targets, resulting in stable distortion levels rather than further degradation as the nominal payload increases.The GA-enhanced pipeline also operates efficiently, with embedding times ranging from $0.02$ to $0.07\,\mathrm{s}$ and extraction times from $0.01$ to $0.04\,\mathrm{s}$.




\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and GAN for secret image size $128\times128$ at BPP targets 0.100, 0.200, and 0.400}
\label{tab:hed_secret128_bpp_targets_grid10}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 60.90 & 0.9993 & 0.000001 & 0.9973 & $\infty$ & 1.0000 & 0.029 & 0.017 \\ \hline
True  & False & 0.100 & 0.0992 & 57.15 & 0.9987 & 0.000002 & 0.9958 & $\infty$ & 1.0000 & 0.020 & 0.012 \\ \hline
False & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.050 & 0.030 \\ \hline
False & False & 0.100 & 0.0992 & 57.37 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.029 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 57.95 & 0.9986 & 0.000002 & 0.9962 & $\infty$ & 1.0000 & 0.056 & 0.033 \\ \hline
True  & False & 0.200 & 0.1953 & 54.14 & 0.9973 & 0.000004 & 0.9941 & $\infty$ & 1.0000 & 0.036 & 0.021 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.059 & 0.033 \\ \hline
False & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.031 & 0.018 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 54.85 & 0.9972 & 0.000003 & 0.9945 & $\infty$ & 1.0000 & 0.209 & 0.110 \\ \hline
True  & False & 0.400 & 0.3966 & 49.24 & 0.9932 & 0.000012 & 0.9895 & $\infty$ & 1.0000 & 0.109 & 0.064 \\ \hline
False & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.114 & 0.064 \\ \hline
False & False & 0.400 & 0.3966 & 51.49 & 0.9944 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.062 & 0.035 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:hed_secret128_bpp_targets_grid10} presents the evaluation results for a $128 \times 128$ secret image embedded at three target payload rates of $0.10$, $0.20$, and $0.40$ bits per pixel (BPP). The results indicate that the Genetic Algorithm (GA) plays a more dominant role in controlling cover-to-stego distortion than HED-based preselection.When GA is enabled, the cover-to-stego PSNR values remain high, ranging from $60.90$ to $61.23\,\mathrm{dB}$, while the effective embedding rate $\mathrm{BPP}_{\mathrm{used}}$ spans from $0.0992$ to $0.3966$. Under this configuration, SSIM values remain within the range $0.9972$–$0.9994$, and MSE values stay low, between $10^{-6}$ and $3 \times 10^{-6}$ across all three payload levels ($0.0992$, $0.1953$, and $0.3966$).When GA is disabled, stego-image quality degrades noticeably, with PSNR decreasing by approximately $3$–$6\,\mathrm{dB}$ across all payload targets. Correspondingly, SSIM values decrease to the range $0.9932$–$0.9987$, while MSE values increase to approximately $1.2 \times 10^{-5}$. Under these conditions, HED-based preselection has little impact when GA is active but exacerbates distortion at higher payload rates when GA is disabled (e.g., $49.24\,\mathrm{dB}$ with HED versus $51.49\,\mathrm{dB}$ without HED at $0.3966\,\mathrm{BPP}$).Secret image reconstruction remains perfectly lossless in all evaluated scenarios, as indicated by $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}} = \infty$ and $\mathrm{SSIM} = 1.0000$. The achieved embedding rates closely match the target values at $0.0992$, $0.1953$, and $0.3966\,\mathrm{BPP}$. Computational efficiency remains practical, with processing time increasing gradually as the payload rate rises (embedding time from $0.03$ to $0.21\,\mathrm{s}$ and extraction time from $0.01$ to $0.11\,\mathrm{s}$), supporting the real-world applicability of the GA-enhanced pipeline.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and GAN for secret image size $512\times512$ at BPP targets 0.100, 0.200, and 0.400 }
\label{tab:hed_secret512_bpp_targets_grid22}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.018 \\ \hline
True  & False & 0.100 & 0.0992 & 57.14 & 0.9987 & 0.000002 & 0.9958 & $\infty$ & 1.0000 & 0.021 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.045 & 0.017 \\ \hline
False & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.018 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.060 & 0.032 \\ \hline
True  & False & 0.200 & 0.1953 & 54.13 & 0.9973 & 0.000004 & 0.9940 & $\infty$ & 1.0000 & 0.036 & 0.021 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.060 & 0.032 \\ \hline
False & False & 0.200 & 0.1953 & 54.56 & 0.9973 & 0.000003 & 0.9943 & $\infty$ & 1.0000 & 0.031 & 0.018 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.115 & 0.062 \\ \hline
True  & False & 0.400 & 0.3966 & 49.26 & 0.9932 & 0.000012 & 0.9896 & $\infty$ & 1.0000 & 0.136 & 0.044 \\ \hline
False & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.113 & 0.061 \\ \hline
False & False & 0.400 & 0.3966 & 51.47 & 0.9943 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.061 & 0.036 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:hed_secret512_bpp_targets_grid22} presents the evaluation results for a $512 \times 512$ secret image embedded at three target payload rates of $0.10$, $0.20$, and $0.40$ bits per pixel (BPP). The results indicate that the Genetic Algorithm (GA) is the dominant factor in determining cover-to-stego fidelity, whereas HED-based preselection has a negligible impact when GA is enabled.When GA is active, PSNR values decrease gradually as the embedding rate increases, with PSNR reaching $61.20\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.0992$, $58.25\,\mathrm{dB}$ at $0.1953$, and $55.17\,\mathrm{dB}$ at $0.3966$. Across these payload levels, SSIM values remain high, ranging from $0.9974$ to $0.9993$, while MSE values stay within the range of $10^{-6}$ to $3 \times 10^{-6}$.When GA is disabled, PSNR values decrease by several decibels at all embedding rates, falling to $57.14$–$57.46\,\mathrm{dB}$ at $0.0992$, $54.13$–$54.56\,\mathrm{dB}$ at $0.1953$, and $49.26$–$51.47\,\mathrm{dB}$ at $0.3966$. Correspondingly, SSIM decreases to approximately $0.9932$, and MSE increases to about $1.2 \times 10^{-5}$ when HED operates without GA at the highest payload rate ($0.3966\,\mathrm{BPP}$). The most pronounced degradation in performance is therefore observed at the highest embedding rate.Secret image reconstruction remains perfectly lossless in all evaluated scenarios, as indicated by $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}} = \infty$ and $\mathrm{SSIM} = 1.0000$. The effective embedding rates closely match the specified targets at $0.0992$, $0.1953$, and $0.3966\,\mathrm{BPP}$. The system operates with practical computational efficiency, with execution time increasing proportionally to payload size; embedding time ranges from $0.03$ to $0.12\,\mathrm{s}$, while extraction time ranges from $0.01$ to $0.06\,\mathrm{s}$.






\subsection{Security Analysis Using Edge Maps}

%In this work, we assume a passive steganalyst with no access to cover–stego pairs and no internal keys or selection masks. The algorithm is public (Kerckhoffs’ principle), but seeds/masks—if any—remain private. Consequently, the adversary cannot train matched-pair supervised detectors nor replicate the exact embedding channel, which reduces detection power compared to stronger threat models (e.g., known-cover or chosen-cover attacks.)

We consider a steganographic system with a cover image $I \in \{0,\dots,255\}^{H \times W}$, a secret payload $S$ (bitstream or media), and an attention map $A \in [0,1]^{H \times W}$ that guides where embedding is preferable. The encoder and decoder are defined as
\[
E_{\theta}:\,(I,S,A;\,K_{\text{emb}},K_{\text{white}},\mathrm{nonce}) \mapsto \hat I,
\qquad
D_{\phi}:\,(\hat I;\,K_{\text{emb}},K_{\text{white}},\mathrm{nonce}) \mapsto \hat S,
\]
and operate at a fixed payload rate $r$ bits per pixel (BPP). The evaluation objectives include high visual fidelity and imperceptibility—maximizing $\mathrm{PSNR}(I,\hat I)$ and $\mathrm{SSIM}(I,\hat I)$—low reconstruction error—minimizing $\mathrm{BER}(S,\hat S)$—and low detectability by standard steganalysis tools, complemented by basic robustness sanity checks (e.g., JPEG compression, additive noise, and median filtering).According to the adopted threat model, we assume a passive adversary in the Kerckhoffs sense: the algorithm and hyperparameters are public, but the attacker has no access to the per-image keys or seeds, no paired cover--stego images of the same scene, and no ability to modify the communication channel. Consequently, security is achieved without relying on heavyweight encryption mechanisms. Instead, secrecy is provided through the following components:
(i) an \textbf{embedding key} $K_{\text{emb}}$ that seeds a pseudo-random number generator (PRNG) to produce a keyed permutation over candidate pixels or blocks selected from $A$ (via thresholding or sampling proportional to $A$), thereby determining which positions carry payload bits at rate $r$;
(ii) an optional \textbf{whitening/permutation key} $K_{\text{white}}$ that scrambles or XOR-whitens the payload bits so that any partially recovered information appears statistically structureless; and
(iii) a fresh per-image \textbf{nonce} that guarantees unique permutations and keystreams across different images.

The nonce is stored in a short, redundantly encoded header to ensure reliable recovery. While error-correcting codes (ECC) enhance robustness against noise and distortions, they do not provide secrecy. Under this security model, recovering $S$ from $\hat I$ without the secret keys is computationally infeasible: the keyed placement space is combinatorial, and the whitened payload is indistinguishable from noise. As a result, a passive adversary’s most viable strategy is detection rather than extraction, while payload secrecy is preserved at a fixed BPP.

In the following sections, security performance is evaluated using two deep learning--based steganalysis models, Xu-Net and Ye-Net. Detection accuracy and area under the ROC curve (AUC) are reported, supported by confusion matrices and ROC analyses.


\subsubsection{Security Analysis Using Xu-Net, HED and CNN }

In this subsection, a security analysis is conducted to assess how effectively the proposed HED+CNN-based embedding scheme—optimized using the Genetic Algorithm (GA) allocator—can evade detection by Xu-Net under different embedding rates (0.1, 0.2, and 0.4~bpp). The objective is to evaluate whether the system preserves secrecy and imperceptibility not only at the visual level but also at the statistical level when confronted with a state-of-the-art CNN-based steganalysis detector.Figure~\ref{fig:xu_hed_cnn_conf_all1} presents the confusion matrices, which indicate that the HED+CNN embedding combined with GA-based optimization is operationally difficult for Xu-Net to detect across embedding rates ranging from 0.1 to 0.4~bpp. At 0.1~bpp, the detector performs only marginally better than random guessing, producing a substantial number of false alarms. At higher embedding rates (0.2 and 0.4~bpp), Xu-Net fails to discriminate between cover and stego images and degenerates into predicting all samples as “stego,” resulting in random accuracy on a balanced dataset.The confusion matrices further show that the detector is unable to identify any meaningful distinction between cover and stego images. Specifically, the overall classification accuracy reaches only 52.5\% at BPP = 0.1, which is close to chance-level performance. At BPP = 0.2 and BPP = 0.4, the classifier exhibits a degenerate behavior by labeling all samples as stego images, yielding a true positive rate (TPR) of 1.0 and a true negative rate (TNR) of 0.0. Consequently, the corresponding receiver operating characteristic (ROC) area is approximately 0.5, confirming chance-level detection performance.


\begin{figure}
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (HED+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_roc_all2} shows that the ROC curves of Xu-Net at \( \mathrm{BPP}\in\{0.1,\,0.2,\,0.4\} \) presents minimal variation from the chance diagonal because their AUC values remain at 0.52. The ROC curves for Xu-Net (HED+CNN) appear in the figure at three different payload rates of BPP = 0.1, 0.2 and 0.4. The ROC curves maintain a close position to the diagonal line while the AUC values reach approximately 0.52 for all decision threshold points. The results indicate that the system maintains a 50\% equal-error rate and lacks any decision threshold which provides both high true-positive detection and low false-positive results. The steganographic method demonstrates resistance to detection through this particular detector system when using this specific dataset configuration.


\begin{figure}
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_roc_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (HED+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_roc_all2}
\end{figure}
%\end{document}

\subsubsection{Security Analysis Using Xu-Net, HED, and GAN}

In this subsection, a comprehensive security analysis is conducted to evaluate the ability of the proposed HED+GAN-based embedding framework to resist detection by Xu-Net at different embedding capacities (0.1, 0.2, and 0.4~bpp). The objective of this evaluation is to determine whether the combination of HED-based edge guidance and GAN-based feature learning enhances concealment to a degree that prevents even an advanced CNN-based steganalyzer, such as Xu-Net, from reliably distinguishing between cover and stego images.Figure~\ref{fig:xu_hed_gan_conf_all3} presents the Xu-Net confusion matrices for the \textbf{HED+GAN} configuration. At an embedding rate of 0.1~bpp, Xu-Net achieves an overall accuracy of 52.5\% on a balanced test set consisting of 20 cover images and 20 stego images. The corresponding confusion matrix reports 6 true negatives, 14 false positives, 5 false negatives, and 15 true positives. These results indicate near–chance-level performance, with a pronounced bias toward predicting the \emph{stego} class. Specifically, at $\mathrm{BPP} = 0.1$, the model correctly identifies 6 out of 20 cover images and 15 out of 20 stego images, yielding an accuracy of approximately 52.5\%, a true positive rate (TPR) of 0.75, and a true negative rate (TNR) of 0.30.Model performance further degrades at higher embedding rates of $\mathrm{BPP} = 0.2$ and $\mathrm{BPP} = 0.4$. In these cases, Xu-Net correctly classifies only 2 out of 20 cover images while detecting 18 out of 20 stego images, resulting in an overall accuracy of 50\%, with $\mathrm{TPR} = 0.90$ and $\mathrm{TNR} = 0.10$. Consequently, the balanced accuracy values are approximately 0.525 at 0.1~BPP and approximately 0.50 at both 0.2 and 0.4~BPP.Overall, these results demonstrate that Xu-Net exhibits no meaningful ability to discriminate between cover and stego images under the HED+GAN configuration, effectively operating at chance-level performance. While this indicates strong resistance of the proposed embedding strategy against this specific Xu-Net setup and dataset, it does not, by itself, constitute proof of universal security and should be interpreted in conjunction with evaluations using additional detectors, datasets, and attack scenarios.

\begin{figure}[H]
  \centering

  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_gan_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_gan_conf_0_4}
  \end{subfigure}

  \caption{XuNet Confusion (HED+GAN) at different BPP.}
  \label{fig:xu_hed_gan_conf_all3}
\end{figure}

Figure~\ref{fig:xu_hed_gan_roc_all4} illustrates the ROC curves obtained by \textbf{Xu-Net} when evaluating \emph{HED+GAN} stego images at embedding rates of 
\( \mathrm{BPP} \in \{0.1,\,0.2,\,0.4\} \).The curves exhibit minimal deviation from the chance diagonal, with corresponding area-under-the-curve (AUC) values of approximately $0.53$, $0.52$, and $0.52$, respectively.The test set consists of a relatively small and balanced number of samples—approximately 20 cover images and 20 stego images—which results in a characteristic staircase-shaped ROC curve. The ROC curves for Xu-Net under the HED+GAN configuration, shown for BPP values of $0.1$, $0.2$, and $0.4$, remain close to the diagonal reference line.The computed AUC values indicate near-chance-level discrimination, with an AUC of approximately $0.53$ at $\mathrm{BPP}=0.1$ and approximately $0.52$ at $\mathrm{BPP}=0.2$ and $\mathrm{BPP}=0.4$. The equal-error rate is close to $50\%$, and no operating point achieves both a high true positive rate (TPR) and a low false positive rate (FPR). These results indicate that changes in BPP have only a marginal effect on detectability and that increasing the embedding rate does not significantly improve detection performance under this system configuration.Overall, the results demonstrate that the proposed embedding method remains effectively undetectable when evaluated using Xu-Net with the HED+GAN configuration on the considered dataset.




\begin{figure}[H]
  \centering

  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_gan_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_gan_roc_0_4}
  \end{subfigure}

  \caption{XuNet ROC (HED+GAN) at different BPP.}
  \label{fig:xu_hed_gan_roc_all4}
\end{figure}



\subsubsection{ Security Analysis Using Xu-Net, RCF, and CNN}

In this subsection, the security evaluation focuses on the proposed RCF+CNN embedding framework, where RCF (Richer Convolutional Features) provides edge-based feature guidance during the embedding process. The objective is to analyze how effectively this hybrid model conceals hidden information and resists detection by Xu-Net across different embedding payloads (0.1, 0.2, and 0.4~bpp). This analysis provides insight into the detectability and resilience of the RCF-guided embedding strategy when evaluated against a state-of-the-art steganalysis model.

Figure~\ref{fig:xu_rcf_cnn_conf_all5} shows the confusion matrices produced by the Xu-Net steganalyzer when evaluating the RCF+CNN embedding method on a balanced test set consisting of 20 cover images and 20 stego images per payload level. At an embedding rate of 0.1~bpp, the classifier reports 8 true negatives, 12 false positives, 8 false negatives, and 12 true positives. This results in an overall accuracy of 50\%, with a true positive rate (TPR) of 0.60, a true negative rate (TNR) of 0.40, a precision of 0.50, an F1-score of approximately 0.55, and a Matthews correlation coefficient (MCC) of 0. These metrics indicate chance-level detection performance.

The confusion matrices for \textbf{Xu-Net (RCF+CNN)} at $\mathrm{BPP} = 0.1$, $0.2$, and $0.4$ consistently exhibit near–chance-level behavior, with a slight bias toward predicting the \emph{stego} class. At 0.1~bpp, Xu-Net achieves 50\% accuracy by correctly identifying 8 out of 20 cover images and 12 out of 20 stego images. At higher payload rates of 0.2 and 0.4~bpp, the classifier maintains the same 50\% accuracy, with TPR increasing to 0.65 and TNR decreasing to 0.35, corresponding to 7 correct cover detections and 13 correct stego detections out of 20 samples each. As a result, the balanced accuracy remains fixed at 0.50 across all evaluated payload levels, indicating that the classifier fails to meaningfully distinguish between cover and stego images.Overall, these results demonstrate that the specific Xu-Net (RCF+CNN) configuration exhibits no effective discriminative capability on the evaluated dataset, suggesting that the proposed RCF-guided embedding framework provides strong resistance to detection under this steganalysis setting.

%this result does not provide universal security protection because ROC/AUC values near 0.5 need verification across multiple detectors and different datasets.

\begin{figure}[H]
  \centering

  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_cnn_conf_0_4}
  \end{subfigure}

  \caption{XuNet Confusion (RCF+CNN) at different BPP.}
  \label{fig:xu_rcf_cnn_conf_all5}
\end{figure}

Figure~\ref{fig:xu_rcf_cnn_roc_all6} shows that the the ROC curves for \textbf{XuNet (RCF+CNN)} at \(\mathrm{BPP}=0.1,\,0.2,\,0.4\) show a close proximity to the diagonal line with \(\mathrm{AUC}\approx 0.52\) in all three panels. The equal–error rate reaches 50\% because the operating points fail to achieve both high TPR and low FPR across all thresholds. The AUC values remain nearly constant when BPP increases which indicates that this configuration makes stego images less detectable. The results indicate that the embedding method shows resistance to this particular Xu-Net (RCF+CNN) detector when applied to this dataset.
%although they do not confirm complete security without additional evaluation tests.


\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_cnn_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_cnn_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_cnn_roc_0_4}
  \end{subfigure}

  \caption{XuNet ROC (RCF+CNN)at different BPP.}
  \label{fig:xu_rcf_cnn_roc_all6}
\end{figure}

\subsubsection{Security Analysis Using Xu-Net, RCF, and GAN}
In this subsection, the RCF+GAN embedding framework is evaluated using the Xu-Net detector to assess its resistance to statistical and spatial steganalysis. The RCF (Richer Convolutional Features) module provides edge-aware guidance during embedding, while the GAN component enhances the realism of the modified pixel distributions. Together, these mechanisms aim to generate stego images that are visually indistinguishable from cover images and statistically resilient to detection. The objective of this analysis is to determine whether Xu-Net—a state-of-the-art CNN-based steganalyzer—can effectively differentiate between cover and stego images across varying payload capacities (0.1, 0.2, and 0.4~bpp), thereby quantifying the detectability and robustness of the RCF+GAN-based embedding scheme.Figure~\ref{fig:xu_rcf_gan_conf_all7} shows that the resulting confusion matrices exhibit near–chance-level performance across all evaluated BPP values from 0.1 to 0.4, with the model displaying a consistent bias toward predicting the \emph{stego} class. At BPP = 0.1, the classifier achieves an overall accuracy of 50\% by correctly identifying 7 out of 20 cover images and 13 out of 20 stego images, corresponding to a true positive rate (TPR) of 0.65 and a true negative rate (TNR) of 0.35. At higher payload rates of BPP = 0.2 and BPP = 0.4, the model correctly detects 16 out of 20 stego images but identifies only 4 out of 20 cover images, resulting in the same overall accuracy of 50\% with TPR = 0.80 and TNR = 0.20. Consequently, the balanced accuracy remains fixed at 0.50 across all payload levels, indicating that the classifier fails to meaningfully distinguish between cover and stego images.Overall, these results demonstrate that the specific Xu-Net (RCF+GAN) configuration is unable to detect the steganographic embedding on the evaluated dataset. However, this outcome alone does not constitute proof of universal security, as comprehensive validation requires corroborating ROC/AUC results approaching 0.5 across multiple detectors and evaluations on diverse datasets.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_gan_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_gan_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_gan_conf_0_4}
  \end{subfigure}
  \caption{XuNet Confusion (RCF+GAN) at different BPP.}
  \label{fig:xu_rcf_gan_conf_all7}
\end{figure}

Figure~\ref{fig:xu_rcf_gan_roc_all8} presents the ROC curves obtained for the Xu-Net steganalyzer when evaluating the \textbf{RCF+GAN} embedding configuration at BPP values of 0.1, 0.2, and 0.4. The corresponding area-under-the-curve (AUC) values are approximately 0.53 at 0.1~BPP and 0.52 at both 0.2 and 0.4~BPP. In all cases, the ROC curves remain close to the diagonal reference line, indicating near–chance-level detection performance.The AUC values close to 0.5 demonstrate that the detector fails to identify any operating point that simultaneously achieves a high true positive rate (TPR) and a low false positive rate (FPR), with the equal-error rate remaining around 50\%. The minimal variation in detection performance across different payload capacities further indicates that increasing the embedding rate does not significantly improve detectability under this configuration. Overall, these results suggest that stego images generated using the RCF+GAN framework remain difficult to detect by Xu-Net on the evaluated dataset.However, the inability of the Xu-Net (RCF+GAN) detector to successfully identify embedded content in this experiment does not, by itself, constitute proof of overall security. Comprehensive validation requires additional evidence obtained from evaluations on multiple datasets and against stronger steganalysis models, ideally supported by confidence intervals and statistical significance analysis.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_gan_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_gan_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_gan_roc_0_4}
  \end{subfigure}
  \caption{XuNet ROC (RCF+GAN) at different BPP.}
  \label{fig:xu_rcf_gan_roc_all8}
\end{figure}

% يتطلب الحزمة booktabs لتحسين تنسيق الجداو

The results reported in Table~\ref{tab:hed12} show that Xu-Net achieves classification accuracies in the range of 50–52.5\% and AUC values between 0.515 and 0.525 when evaluated across three payload sizes (0.1, 0.2, and 0.4~bpp) and two decoder configurations (CNN and GAN). Overall, the accuracy remains close to chance level, while the area under the ROC curve remains stable within the same narrow range.On a balanced test set, random guessing corresponds to 50\% accuracy and an AUC of 0.5. The marginal improvements observed above chance level—approximately 2.5\% in accuracy and 0.025 in AUC—indicate that the detector fails to meaningfully distinguish between cover and stego images. Moreover, the detection results for the HED+CNN and HED+GAN configurations exhibit no significant differences, suggesting that the Xu-Net detector, under the evaluated settings, is largely insensitive to the choice of decoder as well as to increases in payload size from 0.1 to 0.4~bpp.These findings indicate that the evaluated adversary—Xu-Net operating with a fixed decision threshold—fails to reliably detect HED-based embedding across the tested payload rates. Consequently, the proposed embedding strategy demonstrates strong resistance to this steganalysis configuration and is suitable for stealth-oriented applications under the considered threat model.
\begin{table}[htbp]
\centering
\caption{Xu-Net Security Analysis using HED Accuracy and AUC}
\label{tab:hed12}
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\ \hline
BPP\_0.1\_HED\_CNN_GA & 52.50\% & 0.5225 \\ \hline
BPP\_0.2\_HED\_CNN_GA & 50.00\% & 0.5150 \\ \hline
BPP\_0.4\_HED\_CNN_GA & 50.00\% & 0.5150 \\ \hline
BPP\_0.1\_HED\_GAN_GA & 52.50\% & 0.5250 \\ \hline
BPP\_0.2\_HED\_GAN_GA & 50.00\% & 0.5175 \\ \hline
BPP\_0.4\_HED\_GAN & 50.00\% & 0.5175 \\ \hline
\end{tabular}
\end{table}

{\color{blue}
It can be observed from Table~\ref{tab:hed12} that most of the reported accuracies are close to 50\%, with AUC values around 0.5. 
Since the steganalysis task is a balanced binary classification problem (cover versus stego), an accuracy of approximately 50\% corresponds to chance-level performance. 
This indicates that the Xu-Net classifier is unable to reliably distinguish between cover and stego images generated by the proposed method.
Such behavior reflects a high level of security, as the embedding strategy effectively suppresses detectable artifacts even at higher payloads (BPP = 0.2 and 0.4).
The reported AUC values further confirm this observation, as values close to 0.5 indicate random guessing rather than successful discrimination.Although the reported accuracy values remain close to 50\% in most cases, the corresponding AUC values exhibit slight variations.This behavior can be explained by the fundamental difference between the two metrics.
Accuracy is computed based on a fixed decision threshold and reflects the final classification outcome, which in a balanced binary steganalysis setting results in chance-level performance when the classifier fails to make confident decisions.
In contrast, the AUC metric evaluates the ranking ability of the classifier across all possible thresholds.
Therefore, even weak discriminative signals that are insufficient to alter the final classification accuracy may still lead to small variations in AUC values.
This observation indicates that while the Xu-Net classifier captures minor statistical differences, these differences are not strong enough to enable reliable detection, thus confirming the robustness of the proposed steganographic method.

}

Table~\ref{tab:rcf22} shows that the evaluation results for cover and stego images indicate that \textbf{accuracy} remains constant at 50.00\% across all payload sizes and for both decoding methods, while the area under the ROC curve (AUC) stays close to chance level, with values ranging between 0.52 and 0.53. These results demonstrate that Xu-Net operates at near–random guessing performance when attempting to distinguish between cover and stego images produced by RCF-based embedding.
The slight increase of AUC above 0.50 (by only 0.02 to 0.03) shows no consistent trend across different payload rates (0.1 to 0.4~bpp) or decoder types (CNN and GAN). Consequently, the detector fails to exploit payload size or decoder variation to improve detection performance. Overall, these findings indicate that RCF-based embedding remains difficult to detect when Xu-Net is used as the adversary at the evaluated payload levels, thereby demonstrating effective stealth capabilities under the considered steganalysis setting.
\begin{table}[htbp]
\centering
\caption{Xu-Net Security Analysis using Accuracy and AUC for RCF}
\label{tab:rcf22}
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\ \hline
BPP\_0.1\_RCF\_CNN_GA & 50.00\% & 0.5250 \\ \hline
BPP\_0.2\_RCF\_CNN_GA & 50.00\% & 0.5225 \\ \hline
BPP\_0.4\_RCF\_CNN_GA & 50.00\% & 0.5225 \\ \hline
BPP\_0.1\_RCF\_GAN_GA & 50.00\% & 0.5300 \\ \hline
BPP\_0.2\_RCF\_GAN_GA & 50.00\% & 0.5200 \\ \hline
BPP\_0.4\_RCF\_GAN_GA & 50.00\% & 0.5200 \\ \hline
\end{tabular}
\end{table}


\subsubsection{Security Analysis Using Ye-Net, HED, and CNN}
In this subsection, the performance of the Ye-Net detector is evaluated when analyzing stego images generated using the HED+CNN embedding method. Ye-Net is a convolutional neural network specifically designed to detect subtle artifacts introduced during the embedding process, making it a suitable benchmark for assessing the resilience of modern steganographic schemes. The objective of this analysis is to examine whether the combination of HED (Holistically-Nested Edge Detection) and CNN-based embedding can effectively resist detection by Ye-Net across multiple payload capacities (0.1, 0.2, and 0.4~bpp). This evaluation provides insight into the extent to which the proposed embedding framework maintains imperceptibility and statistical security under deep learning–based steganalysis.Figure~\ref{fig:xu_hed_cnn_conf_all9} shows the confusion matrices produced by \textbf{Ye-Net} when evaluated on \emph{HED+CNN} stego images using a balanced test set (20 cover images and 20 stego images per payload). Across all tested payload levels ($\mathrm{BPP} = 0.1, 0.2, 0.4$), Ye-Net exhibits a degenerate decision behavior by classifying all samples as cover images. As a result, the model achieves 100\% accuracy for cover images but fails to correctly identify any stego images. This leads to an overall classification accuracy of 50\%, a true positive rate (TPR) of 0\%, a true negative rate (TNR) of 100\%, and a balanced accuracy of 50\% across all payload sizes.These results indicate that Ye-Net is unable to extract discriminative features for detecting the HED+CNN embedding under the evaluated conditions, yielding no effective detection threshold or operating point. While this outcome demonstrates resistance of the proposed embedding method against Ye-Net on the tested dataset, it does not constitute a proof of overall security and should be interpreted alongside evaluations using additional detectors, datasets, and threat model.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (HED+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all9}
\end{figure}
Figure~\ref{fig:xu_hed_cnn_roc_all10} shows that the ROC curves for Ye-Net (HED+CNN) at BPP = 0.1, 0.2, 0.4 . The ROC curves maintain a close position to the diagonal line with AUC values of 0.50 at 0.1 and 0.511 at 0.2 and 0.4 in all panels. The near-chance detection results indicate that the system maintains a 50\% error rate while lacking any point that would achieve high true positive rates and low false positive rates. The small changes in detectability when BPP increases indicate that this specific configuration does not benefit from higher BPP values. The specific Ye-Net (HED+CNN) detector fails to detect this steganographic method on this particular dataset.
%but this result does not prove overall security.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_roc_0_4}
  \end{subfigure}
  \caption{YeNet ROC (HED+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_roc_all10}
\end{figure}


\subsubsection{Security Analysis Using Ye-Net,HED and GAN}
In this subsection, the focus is on evaluating the performance of the Ye-Net detector when applied to stego images generated by the HED+GAN embedding framework. By combining HED (Holistically-Nested Edge Detection) with GAN-based adaptive embedding, the proposed method distributes secret information along complex edge regions and data-driven texture patterns, thereby increasing the difficulty for CNN-based steganalyzers to detect embedding artifacts. The objective of this analysis is to assess whether Ye-Net can effectively distinguish between cover and stego images across different payload levels (0.1, 0.2, and 0.4~bpp), thus measuring the robustness and detectability of the HED+GAN embedding approach.Figure~\ref{fig:xu_hed_gan_conf_all11} shows the confusion matrices obtained by Ye-Net for the \textbf{HED+GAN} configuration at BPP values of 0.1, 0.2, and 0.4. Across all payload levels, Ye-Net exhibits a degenerate decision behavior by classifying all samples as stego,but this finding does not prove overall security.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_gan_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_gan_conf_0_4}
  \end{subfigure}
  \caption{YeNet Confusion (HED+GAN) at different BPP.}
  \label{fig:xu_hed_gan_conf_all11}
\end{figure}

Figure~\ref{fig:xu_hed_gan_roc_all12} presents that the ROC curves show Ye-Net (HED+GAN) performance at three different payload levels (\(\mathrm{BPP}\)) with the third panel showing 0.2 instead of 0.4.The ROC curves in all panels maintain a close position to the diagonal line which results in AUC values of 0.5075 at 0.1 and 0.5025 at 0.2 and 0.2 in the third panel. The AUC values demonstrate that the system performs at chance level with an equal error rate of 50\%. The minimal change in detectability occurs when BPP increases under this specific configuration. The embedding shows resistance to detection by Ye-Net (HED+GAN) on this particular dataset.
%but this result does not prove overall security since it needs verification with additional detectors and separate datasets..

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_gan_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
   \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_ROC_0.4.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_roc_0_2}
  \end{subfigure}\hfill
  \caption{XuNet ROC (HED+GAN) at different BPP.}
  \label{fig:xu_hed_gan_roc_all12}
\end{figure}


\subsubsection{Security Analysis Using Ye-Net, RCF and CNN}

In this subsection, the Ye-Net detector is employed to evaluate the resistance of the proposed RCF+CNN embedding framework. The RCF (Richer Convolutional Features) module enhances the embedding process by leveraging detailed edge and texture information, while the CNN component refines feature representations to maintain high visual fidelity. The objective of this analysis is to determine whether Ye-Net can effectively distinguish between cover and stego images at varying payload capacities (0.1, 0.2, and 0.4~bpp), thereby assessing the detectability and robustness of the RCF-guided embedding approach under neural network–based steganalysis.Figure~\ref{fig:xu_hed_cnn_conf_all13} presents the confusion matrices obtained by Ye-Net when evaluating the \textbf{RCF+CNN} configuration at $\mathrm{BPP} = 0.1,,0.2,,0.4$. Across all payload levels, Ye-Net exhibits a degenerate classification behavior by labeling all samples as \emph{cover}. Consequently, each confusion matrix reports $\mathrm{TN}=20$, $\mathrm{FP}=0$, $\mathrm{FN}=20$, and $\mathrm{TP}=0$, resulting in an overall accuracy of 50\%, a true positive rate (TPR) of 0.0, a true negative rate (TNR) of 1.0, and a balanced accuracy of 0.50 for all payload values.These results indicate that the classifier fails to distinguish between cover and stego images under this configuration. Accordingly, the expected area under the ROC curve (AUC) approaches 0.5, and the equal error rate (EER) approaches 50\%, confirming chance-level detection performance. Overall, these findings demonstrate that the proposed RCF+CNN embedding framework exhibits strong resistance to this particular Ye-Net detector on the evaluated dataset.

%but this finding does not prove overall security because it needs additional evidence from multiple detectors and different.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (RCF+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all13}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_roc_all14} shows that the the ROC curves for Ye-Net (HED+CNN) at BPP values of 0.1, 0.2 and 0.4. The ROC curves match the diagonal line in all panels while showing an AUC of 0.5000 which indicates chance-level discrimination at all thresholds and a 50\% equal-error rate. The results show no change in detectability when BPP increases under this configuration. The embedding shows resistance to this particular detector on this dataset.
%but this result does not prove overall security because it needs verification through low AUC values from multiple detectors and separate datasets with confidence intervals.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_roc_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (RCF+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_roc_all14}
\end{figure}



\subsubsection{Security Analysis Using Ye-Net, RCF and GAN}
In this subsection, the Ye-Net detector is employed to assess the detectability of stego images generated by the proposed RCF+GAN embedding framework. The RCF (Richer Convolutional Features) component provides detailed edge and texture information to guide the embedding process, while the GAN (Generative Adversarial Network) enhances the realism and statistical consistency of the modified images. Together, these components aim to produce stego images that are visually imperceptible and difficult for deep learning–based detectors to identify. This analysis examines whether Ye-Net can effectively distinguish between cover and stego images across different payload capacities (0.1, 0.2, and 0.4~bpp), thereby providing insight into the detectability and robustness of the RCF+GAN–based steganographic framework under deep learning–based security evaluation.Figure~\ref{fig:xu_rcf_gan_conf_all15} shows the confusion matrices obtained by Ye-Net for the \textbf{RCF+GAN} configuration at $\mathrm{BPP} = 0.1,,0.2,,0.4$. Across all payload levels, Ye-Net exhibits a degenerate decision behavior, classifying all samples as \emph{cover}. In each case, the confusion matrices report $\mathrm{TN} = 20$, $\mathrm{FP} = 0$, $\mathrm{FN} = 20$, and $\mathrm{TP} = 0$, resulting in an overall accuracy of 50\%, a true positive rate (TPR) of 0.0, a true negative rate (TNR) of 1.0, and a balanced accuracy of 0.50. These results indicate that the model lacks discriminative capability and does not provide a meaningful operating threshold under the evaluated conditions.Overall, the findings demonstrate that the proposed RCF+GAN embedding approach exhibits strong resistance to detection by this particular Ye-Net configuration on the evaluated dataset.
%but this finding does not prove overall security because it needs additional testing with different detectors and separate datasets.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_gan_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_gan_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_gan_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (RCF+GAN) at different BPP.}
  \label{fig:xu_rcf_gan_conf_all15}
\end{figure}

Figure~\ref{fig:xu_rcf_gan_roc_all16} shows that the the ROC curves for Ye-Net (RCF+GAN) at BPP = 0.1, 0.2, 0.4 are shown in Figure~7.16. The curves exist on or very close to the diagonal line with AUC values of 0.4812 at 0.1 (below chance level or inverted ranking) and 0.5000 at 0.2 and 0.4. The results show that the system performs at chance level with 50\% equal error rate while lacking any point that would achieve both high true positive rate and low false positive rate. The results show that higher BPP values do not enhance detectability under this configuration because the system maintains its current performance level. The specific detector fails to detect the embedding in this dataset.
%but this result does not prove overall security because AUC performance needs evaluation with multiple detectors and different datasets..


\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_gan_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_gan_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_gan_roc_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (RCF+GAN) at different BPP.}
  \label{fig:xu_rcf_gan_roc_all16}
\end{figure}
Table~\ref{tab:hed_performance} shows that the detector maintains a consistent performance level across all HED-conditioned datasets and payload capacities, with BPP values of $0.1$, $0.2$, and $0.4$. In all evaluated cases, the detector achieves an accuracy of 50\%, while the area under the ROC curve (AUC) remains at chance level, with values ranging between $0.50$ and $0.5112$ for both the HED+CNN and HED+GAN configurations. These results indicate random detection behavior, as the slight increase above 0.5 (less than $0.0112$) is statistically insignificant and does not exhibit any consistent trend with respect to payload size or decoder selection.Furthermore, the operating point of the detector remains at an equal-error rate of 50\% across all configurations. Overall, the analysis demonstrates that HED-based embedding is operationally difficult to detect when evaluated using this adversary and dataset at embedding rates between $0.1$ and $0.4$~bpp. Detection performance does not improve with increasing payload size, and the choice between CNN and GAN decoders has a negligible impact on the detector’s effectiveness.

\begin{table}[H]
\centering
\caption{Performance on HED Datasets}
\label{tab:hed_performance}
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\ \hline
BPP\_0.1\_HED\_CNN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.2\_HED\_CNN_GA & 50.00\% & 0.5112 \\ \hline
BPP\_0.4\_HED\_CNN_GA & 50.00\% & 0.5112 \\ \hline
BPP\_0.1\_HED\_GAN_GA & 50.00\% & 0.5075 \\ \hline
BPP\_0.2\_HED\_GAN_GA & 50.00\% & 0.5025 \\ \hline
BPP\_0.4\_HED\_GAN_GA & 50.00\% & 0.5025 \\ \hline
\end{tabular}
\end{table}



The RCF-based datasets reported in Table~\ref{tab:rcf_performance2} exhibit chance-level detection performance across all experimental conditions. The \textbf{RCF+CNN} configuration achieves an accuracy of 50.00\% and an AUC of 0.5000 at BPP values of 0.1, 0.2, and 0.4, indicating performance equivalent to random guessing on a balanced test set.Similarly, the \textbf{RCF+GAN} configuration demonstrates identical chance-level performance at 0.2 and 0.4~bpp. At 0.1~bpp, it performs slightly below chance, with an accuracy of 48.12\% and an AUC of 0.4812. This behavior reflects a weak negative correlation, suggesting that the detector relies on misleading features; even when the AUC is inverted (yielding 0.5188), performance remains close to chance level.Overall, detection performance remains centered around a 50\% error rate across all payload levels and decoder types, as no operating point achieves a meaningful trade-off between true positive and false positive rates. These results indicate that RCF-guided embedding is operationally difficult to detect under the evaluated adversary and dataset, across embedding rates ranging from 0.1 to 0.4~bpp, for both CNN- and GAN-based decoders.

\begin{table}[H]
\centering
\caption{Performance on RCF Datasets}
\label{tab:rcf_performance2}
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\ \hline
BPP\_0.1\_RCF\_CNN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.2\_RCF\_CNN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.4\_RCF\_CNN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.1\_RCF\_GAN_GA & 48.12\% & 0.4812 \\ \hline
BPP\_0.2\_RCF\_GAN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.4\_RCF\_GAN_GA & 50.00\% & 0.5000 \\ \hline
\end{tabular}
\end{table}

\subsection{Robustness Analysis Using Edge Maps}

In this subsection, the robustness of the proposed method is analyzed using different edge map detectors as guiding mechanisms for embedding and extraction. The objective is to evaluate how effectively the system withstands common image processing operations—such as JPEG compression, Gaussian noise, and salt-and-pepper noise—which typically degrade image quality and challenge reliable data recovery. By examining the bit error rate (BER) and reconstruction accuracy (Re) of the extracted secret images, this analysis aims to determine whether edge-based embedding strategies improve resilience or whether inconsistencies in embedding--extraction synchronization adversely affect the overall robustness of the system.

Table~\ref{tab:mini-ber-re1} shows that, in Mini mode, both \textit{BER\_secret} and \textit{Re\_secret} remain in saturated states across baseline and attack scenarios. Specifically, the results indicate $\mathrm{BER} \approx 0.5$ and $\mathrm{Re} \approx 0.74\!-\!0.75$, with no discernible trend as attack strength increases (JPEG compression, Gaussian noise, or salt-and-pepper noise). This behavior suggests that the extraction process does not properly align with the embedding process. Possible causes include key or threshold mismatches, header decoding failures, or inaccuracies in mini attention map reconstruction. As a result, the recovered data appear effectively random, preventing a meaningful assessment of robustness under the tested conditions.

\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using HED and CNN}
  \label{tab:mini-ber-re1}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none      & -                      & 0.500031 & 0.749064 \\
    Mini & JPEG      & q=90                   & 0.499207 & 0.738150 \\
    Mini & JPEG      & q=70                   & 0.506622 & 0.747780 \\
    Mini & JPEG      & q=50                   & 0.499817 & 0.731722 \\
    Mini & JPEG      & q=30                   & 0.503113 & 0.746924 \\
    Mini & Gaussian  & sigma=0.005            & 0.499084 & 0.747151 \\
    Mini & Gaussian  & sigma=0.01             & 0.500580 & 0.740908 \\
    Mini & Gaussian  & sigma=0.02             & 0.504547 & 0.751235 \\
    Mini & SaltPepper & amount=0.005, svp=0.5 & 0.500305 & 0.749631 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5 & 0.495209 & 0.746226 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5 & 0.501312 & 0.749188 \\
    \bottomrule
  \end{tabular}
\end{table}

The robustness assessment of the Mini embedding mode for the proposed HED--GAN hybrid model is presented in Table~\ref{tab:mini-ber-re-2}. The two robustness indicators show no variation in their values when the system is subjected to different attack types and varying attack intensities. The baseline evaluation, conducted without any attack, yields $\mathrm{BER} \approx 0.50$ and $\mathrm{Re} \approx 0.75$, and all subsequent attack scenarios produce identical results.This apparent stability does not indicate true robustness. Instead, it reflects a failure in the decoding process to correctly recover meaningful secret data, resulting in output statistics that resemble random noise. In particular, a BER value of approximately 0.5 corresponds to random guessing. Therefore, the observed resistance to attacks arises from synchronization or key mismatches between the embedding and extraction stages, rather than from genuine robustness of the Mini embedding strategy itself.
 %The HED--GAN architecture requires two stages of improvement: first achieve accurate baseline recovery with $\mathrm{BER}\ll0.5$ and low $\mathrm{Re}$ values then perform statistical tests on multiple samples to verify its actual robustness.

\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using HED and GAN}
  \label{tab:mini-ber-re-2}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.501709 & 0.750947 \\
    Mini & JPEG       & q=90                   & 0.496399 & 0.744457 \\
    Mini & JPEG       & q=70                   & 0.498627 & 0.750294 \\
    Mini & JPEG       & q=50                   & 0.496704 & 0.738242 \\
    Mini & JPEG       & q=30                   & 0.494202 & 0.745596 \\
    Mini & Gaussian   & sigma=0.005            & 0.498413 & 0.747821 \\
    Mini & Gaussian   & sigma=0.01             & 0.500366 & 0.727148 \\
    Mini & Gaussian   & sigma=0.02             & 0.499023 & 0.751670 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.501984 & 0.750027 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.501678 & 0.750629 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.502747 & 0.754499 \\
    \bottomrule
  \end{tabular}
\end{table}

The proposed RCF--CNN model exhibits its robustness behavior under various image distortion scenarios in the Mini embedding configuration, as reported in Table~\ref{tab:mini-ber-re-3}. The bit error rate (\textit{BER\_secret}) and relative error (\textit{Re\_secret}) remain constant across all evaluated conditions, with BER confined to the range $0.497 \pm 0.003$ and Re consistently between 0.74 and 0.75.

The extraction results show no variation in BER or Re values when the system operates without attacks or when subjected to JPEG compression ($q = 90$--$30$), Gaussian noise ($\sigma = 0.005$--$0.02$), or salt-and-pepper noise (amount $= 0.005$--$0.02$). The absence of observable changes under these attack conditions indicates that the applied distortions do not influence the measured extraction metrics.However, BER values close to 0.5 indicate random data recovery, suggesting decoding failure rather than genuine robustness. Consequently, the system’s apparent stability under attack reflects persistent extraction errors instead of effective resistance to image distortions. Thus, the Mini embedding configuration demonstrates extraction failure manifested as invariant performance metrics, rather than true robustness against the evaluated attacks.

%The evaluation of robustness requires two essential steps: first achieve high extraction accuracy with BER below 0.5 and Re near zero and then establish proper embedding-extraction synchronization before performing perturbation tests for comparison.
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using RCF and CNN}
  \label{tab:mini-ber-re-3}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.496613 & 0.741609 \\
    Mini & JPEG       & q=90                   & 0.497803 & 0.744655 \\
    Mini & JPEG       & q=70                   & 0.497253 & 0.750494 \\
    Mini & JPEG       & q=50                   & 0.501587 & 0.749983 \\
    Mini & JPEG       & q=30                   & 0.501373 & 0.746055 \\
    Mini & Gaussian   & sigma=0.005            & 0.499115 & 0.743936 \\
    Mini & Gaussian   & sigma=0.01             & 0.500488 & 0.748001 \\
    Mini & Gaussian   & sigma=0.02             & 0.499512 & 0.749053 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.496582 & 0.741397 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.496796 & 0.740624 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.496704 & 0.742853 \\
    \bottomrule
  \end{tabular}
\end{table}

 The RCF--GAN hybrid steganographic model exhibits its robustness behavior under various image distortion scenarios in the Mini embedding configuration, as summarized in Table~\ref{tab:mini-ber-re-4}. The bit error rate (\textit{BER\_secret}) and relative reconstruction error (\textit{Re\_secret}) remain stable across all evaluated conditions, with BER values in the range $0.495$--$0.505$ and Re values between $0.73$ and $0.75$.The baseline evaluation without any attack yields $\mathrm{BER} = 0.4939$ and $\mathrm{Re} = 0.7415$, while all attack scenarios produce nearly identical results. No increase in either metric is observed as attack severity increases, indicating that the measured extraction statistics are unaffected by the applied distortions. However, BER values close to 0.5 indicate random data recovery, suggesting that the recovered secret information lacks meaningful structure. This behavior implies a loss of synchronization between the embedding and extraction stages rather than genuine robustness against image distortions.Although the system appears stable under attack, this apparent stability arises from persistent decoding failure rather than effective resistance to compression or noise. Consequently, the framework must first establish reliable baseline recovery—characterized by $\mathrm{BER} \ll 0.5$ and low \textit{Re} values—before meaningful robustness evaluation against compression and noise attacks can be performed.

\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using RCF and GAN}
  \label{tab:mini-ber-re-4}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.493988 & 0.741467 \\
    Mini & JPEG       & q=90                   & 0.504272 & 0.739156 \\
    Mini & JPEG       & q=70                   & 0.498016 & 0.747490 \\
    Mini & JPEG       & q=50                   & 0.496857 & 0.743401 \\
    Mini & JPEG       & q=30                   & 0.499878 & 0.746629 \\
    Mini & Gaussian   & sigma=0.005            & 0.501404 & 0.751044 \\
    Mini & Gaussian   & sigma=0.01             & 0.498749 & 0.731864 \\
    Mini & Gaussian   & sigma=0.02             & 0.502136 & 0.736265 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.494537 & 0.740553 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.494415 & 0.740937 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.494751 & 0.742224 \\
    \bottomrule
  \end{tabular}
\end{table}


\section{Textures Features Detectors Results Analysis}

Analyzing texture feature detectors plays a vital role in understanding how effectively a steganographic or image analysis system can identify and preserve fine image structures. This section presents an experimental evaluation of texture feature extraction models, with a focus on their ability to capture spatial and structural information that enhances image representation. Specifically, deep learning–based architectures such as VGG16 and ResNet50 are examined for their effectiveness in detecting texture features and maintaining image quality under different embedding conditions.The evaluation employs standard quantitative metrics—including PSNR, SSIM, MSE, and IF—as well as embedding and extraction times (TimeE and TimeX) to assess reconstruction fidelity, stability, and computational efficiency. Through these measures, the analysis investigates how well each texture-based feature detector preserves visual quality while supporting reliable and efficient embedding.Overall, this section evaluates the performance of texture feature detectors to determine their suitability for steganographic applications. By comparing VGG16- and ResNet50-based approaches, the analysis identifies which model achieves the most favorable balance between high-quality image reconstruction, embedding robustness, and processing efficiency, thereby guiding model selection for the proposed steganographic framework.

\subsection{Imperceptibility and Capacity Results Using VGG16 and CNN}
Evaluating the imperceptibility and capacity of a steganographic system is essential for assessing its ability to embed information while preserving high visual quality and maintaining an effective payload rate. In this subsection, the performance of the proposed framework is analyzed using VGG16 as a texture feature extractor and a CNN as the embedding network. The objective is to examine how effectively this configuration balances image fidelity, embedding efficiency, and extraction accuracy across different target payloads (0.100, 0.200, and 0.400). The analysis emphasizes the impact of activating the Genetic Algorithm (GA) optimizer, which aims to enhance embedding adaptivity and perceptual quality. By evaluating quantitative measures such as PSNR, SSIM, MSE, and IF, along with embedding and extraction times, this subsection highlights the system’s capacity limits and its ability to maintain high imperceptibility as the embedding payload increases.

In this experiment, imperceptibility and capacity are evaluated using the VGG16 texture extractor in combination with a CNN-based embedding network. Table~\ref{tab:texture_ablation_bpp_grid1} presents the texture-attention ablation results (\(\mathrm{VGG16} \times \mathrm{GA}\)) using a \(64 \times 64\) secret image and target payloads \(\mathrm{BPP}_{\mathrm{tg}} \in \{0.100, 0.200, 0.400\}\). The effective embedding rate \(\mathrm{BPP}_{\mathrm{used}}\) is maintained at 0.0992 for \(\mathrm{BPP}_{\mathrm{tg}} = 0.100\) and saturates at 0.1250 for \(\mathrm{BPP}_{\mathrm{tg}} \in \{0.200, 0.400\}\), indicating a fixed capacity limit that is independent of the target payload once this threshold is reached.

\noindent
Activation of the Genetic Algorithm (GA) is the primary factor responsible for improving cover-image fidelity. Specifically, the $\mathrm{PSNR}_{c \rightarrow s}$ values increase from approximately $51$--$55\,\mathrm{dB}$ (without GA) to $60$--$61\,\mathrm{dB}$ (with GA) when $\mathrm{BPP}_{\mathrm{used}}$ is matched across configurations. Correspondingly, $\mathrm{SSIM}_{c \rightarrow s}$ reaches values between $0.9995$ and $0.9996$, $\mathrm{MSE}_{c \rightarrow s}$ approaches $10^{-6}$, and $\mathrm{IF}_{c \rightarrow s}$ increases to the range $0.998$--$0.999$.In contrast, incorporating the ResNet50 block does not yield any measurable improvement when GA is enabled and, in fact, leads to a decrease in $\mathrm{PSNR}_{c \rightarrow s}$ when both GA and texture guidance are disabled. This observation indicates that the ResNet50-based texture extractor is not required for this configuration.Extraction quality remains perfectly lossless across all experimental conditions. Specifically, $\mathrm{PSNR}_{s \rightarrow x}$ reaches $\infty$ at \(\mathrm{BPP}_{\mathrm{tg}} = 0.100\) and approximately $156\,\mathrm{dB}$ at \(\mathrm{BPP}_{\mathrm{tg}} = 0.200\) and $0.400$, while $\mathrm{SSIM}_{s \rightarrow x}$ remains equal to $1.0000$. Overall, enabling GA significantly enhances perceptual quality at fixed payload rates. However, further increases in \(\mathrm{BPP}_{\mathrm{tg}}\) beyond the observed saturation threshold do not increase \(\mathrm{BPP}_{\mathrm{used}}\) nor affect visual quality, indicating that the system has reached its maximum payload capacity.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 64*64 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and CNN}
\label{tab:texture_ablation_bpp_grid1}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{Resnet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 52.26 & 0.9982 & 0.000006 & 0.9965 & $\infty$ & 1.0000 & 0.023 & 0.016 \\ \hline
False & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.026 \\ \hline
False & False & 0.100 & 0.0992 & 55.71 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.057 & 0.046 \\ \hline
True  & False & 0.200 & 0.1250 & 51.40 & 0.9979 & 0.000007 & 0.9961 & 156.28 & 1.0000 & 0.027 & 0.018 \\ \hline
False & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.097 & 0.061 \\ \hline
False & False & 0.200 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.055 & 0.037 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.071 & 0.034 \\ \hline
True  & False & 0.400 & 0.1250 & 51.40 & 0.9979 & 0.000007 & 0.9961 & 156.28 & 1.0000 & 0.028 & 0.018 \\ \hline
False & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.089 & 0.032 \\ \hline
False & False & 0.400 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.031 & 0.020 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:texture_ablation_bpp_grid_v22} presents a $2 \times 2$ ablation study involving VGG16 and the Genetic Algorithm (GA) using a $128 \times 128$ secret image and target payload rates of $0.100$, $0.200$, and $0.400$ bits per pixel (BPP). In this configuration, no early saturation is observed because the effective payload rates match the specified targets ($\mathrm{BPP}_{\mathrm{used}} = 0.0992$, $0.1953$, and $0.3966$). Across all experiments, GA consistently produces the highest cover-image fidelity.Specifically, the $\mathrm{PSNR}_{c \rightarrow s}$ values improve from approximately $47$–$52\,\mathrm{dB}$ without GA to $55$–$61\,\mathrm{dB}$ with GA at matched payload levels. Correspondingly, $\mathrm{SSIM}_{c \rightarrow s}$ reaches approximately $0.9984$ under GA, compared to a range of $0.9943$–$0.9982$ without GA, while $\mathrm{MSE}_{c \rightarrow s}$ decreases to the order of $10^{-6}$. As expected, cover-image quality gradually decreases as the payload increases, even when GA is enabled. In particular, $\mathrm{PSNR}_{c \rightarrow s}$ reaches approximately $61\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.0992$, decreases to $58\,\mathrm{dB}$ at $0.1953$, and further declines to $55\,\mathrm{dB}$ at $0.3966$, while $\mathrm{IF}_{c \rightarrow s}$ decreases slightly from $0.9987$ to $0.9975$.

The ResNet50 model does not provide any measurable improvement when GA is active, as configurations with and without ResNet50 under GA yield nearly identical results ($\mathrm{True}/\mathrm{True} \approx \mathrm{False}/\mathrm{True}$). Moreover, when used without GA, ResNet50 produces the lowest $\mathrm{PSNR}_{c \rightarrow s}$ values across all payload levels, indicating that its role is supplementary rather than essential in this context.Secret extraction remains perfectly lossless across all testing scenarios, with $\mathrm{PSNR}_{s \rightarrow x} = \infty$ and $\mathrm{SSIM}_{s \rightarrow x} = 1.0000$. Execution time increases when both GA and payload size increase. In particular, embedding and extraction processes require approximately twice the execution time when GA is enabled at each BPP setting, and higher payload levels introduce additional computational overhead.
 %The capacity field (Cap(bits)) maintained a fixed value for each configuration at 262144 bits regardless of target variations which indicates it stores the total number of available embedding locations instead of actual embedded bits that BPP-used measures.
\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 128*128 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and CNN}
\label{tab:texture_ablation_bpp_grid_v22}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{Resnet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.027 \\ \hline
True  & False & 0.100 & 0.0992 & 52.30 & 0.9982 & 0.000006 & 0.9965 & $\infty$ & 1.0000 & 0.023 & 0.016 \\ \hline
False & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.029 \\ \hline
False & False & 0.100 & 0.0992 & 55.69 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.026 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.161 & 0.088 \\ \hline
True  & False & 0.200 & 0.1953 & 50.02 & 0.9971 & 0.000010 & 0.9955 & $\infty$ & 1.0000 & 0.078 & 0.048 \\ \hline
False & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.156 & 0.090 \\ \hline
False & False & 0.200 & 0.1953 & 52.71 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.086 & 0.052 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.167 & 0.111 \\ \hline
True  & False & 0.400 & 0.3966 & 47.35 & 0.9943 & 0.000018 & 0.9938 & $\infty$ & 1.0000 & 0.081 & 0.050 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.161 & 0.097 \\ \hline
False & False & 0.400 & 0.3966 & 49.65 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.088 & 0.055 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:texture_ablation_bpp_grid_v33} presents a $2 \times 2$ ablation study (\(\mathrm{VGG16} \times \mathrm{GA}\)) using a $512 \times 512$ secret image and target payload rates \(\mathrm{BPP}_{\mathrm{tg}} \in \{0.100, 0.200, 0.400\}\). In this setting, the effective embedding rates exactly match the specified targets (\(\mathrm{BPP}_{\mathrm{used}} = 0.0992,\, 0.1953,\, 0.3966\)), indicating no early saturation effects. Across all evaluated conditions, the Genetic Algorithm (GA) consistently produces the highest cover-image fidelity.Specifically, the $\mathrm{PSNR}_{c \rightarrow s}$ values improve from approximately $47$–$55\,\mathrm{dB}$ without GA to $55$–$61\,\mathrm{dB}$ with GA at matched payload levels. Correspondingly, the $\mathrm{SSIM}_{c \rightarrow s}$ values reach the range $0.9984$–$0.9996$, while $\mathrm{MSE}_{c \rightarrow s}$ decreases to the order of $10^{-6}$ and $\mathrm{IF}_{c \rightarrow s}$ remains between $0.997$ and $0.999$. As expected, cover-image quality gradually decreases as the payload increases from $0.0992$ to $0.1953$ and then to $0.3966$, even when GA is enabled.

The ResNet50 model does not provide any measurable improvement when GA is active, as configurations with both VGG16 and GA enabled (\(\mathrm{True}/\mathrm{True}\)) yield results comparable to those without ResNet50 but with GA enabled (\(\mathrm{False}/\mathrm{True}\)). When operating independently (i.e., without GA), ResNet50 produces the lowest $\mathrm{PSNR}_{c \rightarrow s}$ values across all payload levels, indicating that its role is supplementary in this context.
Secret extraction remains perfectly lossless across all evaluated scenarios, as evidenced by $\mathrm{PSNR}_{s \rightarrow x} = \infty$ and $\mathrm{SSIM}_{s \rightarrow x} = 1.0000$. Execution time increases when both GA and payload size increase. In particular, activating GA during embedding and extraction approximately doubles the execution time compared to non-GA configurations, while higher payload sizes further extend the overall processing duration.
 
\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 512*512 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and CNN}
\label{tab:texture_ablation_bpp_grid_v33}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{Resnet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 52.28 & 0.9982 & 0.000006 & 0.9965 & $\infty$ & 1.0000 & 0.023 & 0.016 \\ \hline
False & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.083 & 0.048 \\ \hline
False & False & 0.100 & 0.0992 & 55.72 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.050 & 0.031 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.159 & 0.088 \\ \hline
True  & False & 0.200 & 0.1953 & 50.01 & 0.9971 & 0.000010 & 0.9955 & $\infty$ & 1.0000 & 0.080 & 0.047 \\ \hline
False & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.085 & 0.049 \\ \hline
False & False & 0.200 & 0.1953 & 52.73 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.047 & 0.029 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.163 & 0.095 \\ \hline
True  & False & 0.400 & 0.3966 & 47.32 & 0.9943 & 0.000019 & 0.9938 & $\infty$ & 1.0000 & 0.082 & 0.051 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.179 & 0.097 \\ \hline
False & False & 0.400 & 0.3966 & 49.64 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.089 & 0.055 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


\subsection{Impercibtibility and Capacity Results Using VGG16 and GAN}

In this subsection, the performance of the proposed framework integrating VGG16 and GAN architectures is analyzed to assess its ability to balance high visual fidelity with efficient payload utilization. The VGG16 network serves as a texture-based feature extractor that captures fine image structures, while the GAN component contributes to learning an optimal embedding distribution that preserves realism and imperceptibility. The analysis further investigates how the inclusion of the Genetic Algorithm (GA) refines this process by optimizing embedding parameters to improve cover-image quality and reconstruction performance. Quantitative evaluation is conducted using metrics such as PSNR, SSIM, MSE, IF, as well as embedding and extraction times (TimeE and TimeX).Table~\ref{tab:texture_ablation_bpp_grid_v44} presents a $2 \times 2$ ablation study of the VGG16--GA configuration using a $64 \times 64$ secret image and target payload rates $\mathrm{BPP}_{\mathrm{tg}} \in \{0.100, 0.200, 0.400\}$. The effective payload rate $\mathrm{BPP}_{\mathrm{used}}$ remains at 0.0992 when the target is 0.100 and saturates at 0.1250 for target rates of 0.200 and 0.400.The Genetic Algorithm (GA) emerges as the primary factor improving cover-image fidelity. When GA is enabled, the $\mathrm{PSNR}_{c \rightarrow s}$ increases from approximately $52$–$55\,\mathrm{dB}$ to $60$–$61\,\mathrm{dB}$. At the same time, $\mathrm{SSIM}_{c \rightarrow s}$ reaches the range $0.9991$–$0.9996$, $\mathrm{MSE}_{c \rightarrow s}$ approaches $10^{-6}$, and $\mathrm{IF}_{c \rightarrow s}$ increases to $0.9976$–$0.9987$. The ResNet50 model provides no measurable improvement when GA is active, as the results for configurations with both components enabled ($\mathrm{True}/\mathrm{True}$) are effectively equivalent to those with GA enabled alone ($\mathrm{False}/\mathrm{True}$). Moreover, when used independently, ResNet50 yields lower $\mathrm{PSNR}_{c \rightarrow s}$ values than configurations where both modules are disabled, indicating its supplementary role in this setting.
Secret extraction remains perfectly lossless across all experiments, with $\mathrm{PSNR}_{s \rightarrow x} = \infty$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.100$ and approximately $156\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.200$ and $0.400$, while $\mathrm{SSIM}_{s \rightarrow x}$ remains equal to $1.0000$. The inclusion of GA introduces additional computational overhead, resulting in embedding and extraction times that are approximately 1.5 to 1.8 times longer than those observed without GA.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 64*64 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and GAN}
\label{tab:texture_ablation_bpp_grid_v44}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.029 \\ \hline
True  & False & 0.100 & 0.0992 & 52.94 & 0.9985 & 0.000005 & 0.9968 & $\infty$ & 1.0000 & 0.029 & 0.019 \\ \hline
False & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.084 & 0.050 \\ \hline
False & False & 0.100 & 0.0992 & 55.71 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.049 & 0.031 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.056 & 0.034 \\ \hline
True  & False & 0.200 & 0.1250 & 52.13 & 0.9983 & 0.000006 & 0.9964 & 156.28 & 1.0000 & 0.035 & 0.023 \\ \hline
False & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.054 & 0.033 \\ \hline
False & False & 0.200 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.031 & 0.020 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.056 & 0.035 \\ \hline
True  & False & 0.400 & 0.1250 & 52.13 & 0.9983 & 0.000006 & 0.9964 & 156.28 & 1.0000 & 0.035 & 0.022 \\ \hline
False & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.054 & 0.036 \\ \hline
False & False & 0.400 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.031 & 0.020 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:texture_ablation_bpp_grid_v55} presents a $2 \times 2$ ablation study (\(\mathrm{VGG16} \times \mathrm{GA}\)) using a $128 \times 128$ secret image, which achieves effective payload rates of \((0.0992, 0.1953, 0.3966)\) bits per pixel. The Genetic Algorithm (GA) provides the primary improvement in cover-image fidelity. With GA enabled, the peak signal-to-noise ratio from cover to stego (\(\mathrm{PSNR}_{c \rightarrow s}\)) ranges approximately from \(55\) to \(61\,\mathrm{dB}\), compared to \(47\) to \(53\,\mathrm{dB}\) when GA is disabled. At the same time, the structural similarity index (\(\mathrm{SSIM}_{c \rightarrow s}\)) remains between \(0.9984\) and \(0.9996\), the mean squared error (\(\mathrm{MSE}_{c \rightarrow s}\)) stays on the order of \(10^{-6}\), and the image fidelity measure (\(\mathrm{IF}_{c \rightarrow s}\)) remains close to \(0.998\). As expected, cover-image quality decreases monotonically with increasing payload, even when GA is active (approximately \(61\,\mathrm{dB}\) at \(0.0992\), decreasing to \(58\,\mathrm{dB}\) at \(0.1953\), and further to \(55\,\mathrm{dB}\) at \(0.3966\)).

The ResNet50 block does not provide any measurable improvement under GA optimization, as the results for the \textit{True/True} and \textit{False/True} configurations are essentially equivalent. Moreover, when used without GA, ResNet50 yields the lowest \(\mathrm{PSNR}_{c \rightarrow s}\) values, indicating that its role in this configuration is supplementary rather than essential.Secret extraction remains perfectly lossless across all evaluated conditions, as indicated by \(\mathrm{PSNR}_{s \rightarrow x} = \infty\) and \(\mathrm{SSIM}_{s \rightarrow x} = 1.0000\). Finally, execution time increases with higher BPP values, as the optimization process becomes more complex and the system processes a larger number of embedded bits.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 128*128 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and GAN}
\label{tab:texture_ablation_bpp_grid_v55}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 52.96 & 0.9985 & 0.000005 & 0.9968 & $\infty$ & 1.0000 & 0.028 & 0.019 \\ \hline
False & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.041 & 0.026 \\ \hline
False & False & 0.100 & 0.0992 & 55.69 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.040 & 0.016 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.082 & 0.049 \\ \hline
True  & False & 0.200 & 0.1953 & 50.58 & 0.9974 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.055 & 0.033 \\ \hline
False & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.079 & 0.047 \\ \hline
False & False & 0.200 & 0.1953 & 52.71 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.043 & 0.028 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.164 & 0.103 \\ \hline
True  & False & 0.400 & 0.3966 & 47.05 & 0.9939 & 0.000020 & 0.9936 & $\infty$ & 1.0000 & 0.097 & 0.058 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.311 & 0.169 \\ \hline
False & False & 0.400 & 0.3966 & 49.65 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.159 & 0.116 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup



Table~\ref{tab:texture_ablation_bpp_grid_v66} reports a $2 \times 2$ ablation study using VGG16 with GA on a $512 \times 512$ secret image and demonstrates accurate tracking of the target payloads 
\(\mathrm{BPP}_{\mathrm{used}} = 0.0992,\, 0.1953,\, 0.3966\).The Genetic Algorithm (GA) proves essential for maintaining cover-image fidelity. Specifically, the peak signal-to-noise ratio from cover to stego (\(\mathrm{PSNR}_{c \rightarrow s}\)) increases from approximately \(47\)–\(56\,\mathrm{dB}\) without GA to \(55\)–\(61\,\mathrm{dB}\) when GA is enabled at equivalent payload levels. Meanwhile, the structural similarity index (\(\mathrm{SSIM}_{c \rightarrow s}\)) remains high at \(0.9996\), \(0.9992\), and \(0.9984\) across increasing payloads. The mean squared error (\(\mathrm{MSE}_{c \rightarrow s}\)) stays within the range of \(10^{-6}\) to \(3 \times 10^{-6}\), while the image fidelity measure (\(\mathrm{IF}_{c \rightarrow s}\)) decreases slightly with increasing payload, from \(0.9987\) to \(0.9974\).

The ResNet50 block provides no measurable benefit when GA is active, as the results for the \textit{True/True} and \textit{False/True} configurations are nearly identical. Moreover, when used independently, ResNet50 yields the lowest \(\mathrm{PSNR}_{c \rightarrow s}\) values, indicating that its role is supplementary rather than essential in this configuration.As expected, cover-image quality decreases as the payload increases, with \(\mathrm{PSNR}_{c \rightarrow s}\) values declining from approximately \(61\) to \(58\) and then to \(55\,\mathrm{dB}\) across the three target payload levels. Secret extraction remains perfectly lossless under all tested conditions, as indicated by \(\mathrm{PSNR}_{s \rightarrow x} = \infty\) and \(\mathrm{SSIM}_{s \rightarrow x} = 1.0000\).Execution time increases with both GA activation and payload size. For GA-based embedding and extraction at \(\mathrm{BPP}_{\mathrm{used}} = 0.3966\), the embedding and extraction times range from \(0.165\)–\(0.179\,\mathrm{s}\) and \(0.095\)–\(0.098\,\mathrm{s}\), respectively. In contrast, without GA, the corresponding times are reduced to approximately \(0.088\)–\(0.094\,\mathrm{s}\) for embedding and \(0.054\)–\(0.058\,\mathrm{s}\) for extraction.



\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 512*512 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and GAN}
\label{tab:texture_ablation_bpp_grid_v66}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 52.92 & 0.9985 & 0.000005 & 0.9968 & $\infty$ & 1.0000 & 0.046 & 0.019 \\ \hline
False & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.027 \\ \hline
False & False & 0.100 & 0.0992 & 55.72 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.018 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.085 & 0.051 \\ \hline
True  & False & 0.200 & 0.1953 & 50.57 & 0.9974 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.055 & 0.035 \\ \hline
False & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.086 & 0.050 \\ \hline
False & False & 0.200 & 0.1953 & 52.73 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.049 & 0.029 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.165 & 0.095 \\ \hline
True  & False & 0.400 & 0.3966 & 47.02 & 0.9939 & 0.000020 & 0.9936 & $\infty$ & 1.0000 & 0.094 & 0.058 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.165 & 0.098 \\ \hline
False & False & 0.400 & 0.3966 & 49.64 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.088 & 0.054 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup



\subsection{Imperceptibility and Capacity Results Using ResNet50 and CNN}

In this subsection, the evaluation focuses on the performance of the ResNet50 feature extractor integrated with a CNN-based embedding network, both with and without the application of the Genetic Algorithm (GA) optimizer. The objective is to analyze how these components influence cover-image fidelity, embedding efficiency, and extraction accuracy across various target payloads (0.100, 0.200, and 0.400). The ResNet50 architecture is employed to capture deep texture and structural representations that may enhance embedding precision, while the GA component is expected to refine parameter optimization to improve image quality and stability. By examining quantitative measures such as PSNR, SSIM, MSE, IF, and computational times (TimeE and TimeX), this analysis provides insight into how the ResNet50+CNN configuration maintains visual imperceptibility, achieves lossless recovery, and manages computational efficiency under different embedding capacities.


Table~\ref{tab:texture_ablation_bpp_grid_v77} presents a $2 \times 2$ ablation study involving ResNet50 and GA using a $64 \times 64$ secret image. The system achieves an effective payload of $\mathrm{BPP}_{\mathrm{used}} = 0.0992$ when $\mathrm{BPP}_{\mathrm{tg}} = 0.100$ and reaches a maximum of $\mathrm{BPP}_{\mathrm{used}} = 0.1250$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.200$ and $0.400$. This capacity limit remains unchanged across target payloads within each configuration.Activation of the Genetic Algorithm (GA) is essential for maintaining cover-image fidelity. Specifically, the $\mathrm{PSNR}_{c \rightarrow s}$ values increase from approximately $49$–$56\,\mathrm{dB}$ without GA to $60$–$61\,\mathrm{dB}$ with GA at equivalent payload sizes. At the same time, $\mathrm{SSIM}_{c \rightarrow s}$ remains high, ranging from $0.9991$ to $0.9996$, $\mathrm{MSE}_{c \rightarrow s}$ approaches $10^{-6}$, and $\mathrm{IF}_{c \rightarrow s}$ remains within $0.9986$–$0.9987$.The ResNet50 block provides no additional improvement when GA is active, as the results for the \textit{True/True} and \textit{False/True} configurations are nearly identical. Moreover, operating ResNet50 independently yields the lowest $\mathrm{PSNR}_{c \rightarrow s}$ values (e.g., $50.46\,\mathrm{dB}$ and $49.42\,\mathrm{dB}$) compared to configurations in which both modules are disabled ($55.71\,\mathrm{dB}$ and $54.68\,\mathrm{dB}$), indicating that ResNet50 serves only a supplementary role in this setup.Secret extraction remains perfectly lossless under all evaluated conditions, with $\mathrm{PSNR}_{s \rightarrow x} = \infty$ at $\mathrm{BPP}_{\mathrm{used}} = 0.0992$ and approximately $156.28\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.1250$, while $\mathrm{SSIM}_{s \rightarrow x} = 1.0000$. Execution time increases with both GA activation and payload size, reflecting the additional optimization overhead (e.g., embedding time increases from $0.044\,\mathrm{s}$ to $0.057\,\mathrm{s}$ with GA, compared to $0.020\,\mathrm{s}$ to $0.025\,\mathrm{s}$ without GA).


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 64*64 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and CNN}
\label{tab:texture_ablation_bpp_grid_v77}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.027 \\ \hline
True  & False & 0.100 & 0.0992 & 50.46 & 0.9975 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.020 & 0.014 \\ \hline
False & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.027 \\ \hline
False & False & 0.100 & 0.0992 & 55.71 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.057 & 0.034 \\ \hline
True  & False & 0.200 & 0.1250 & 49.42 & 0.9969 & 0.000011 & 0.9951 & 156.28 & 1.0000 & 0.025 & 0.017 \\ \hline
False & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.055 & 0.033 \\ \hline
False & False & 0.200 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.032 & 0.034 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.055 & 0.034 \\ \hline
True  & False & 0.400 & 0.1250 & 49.42 & 0.9969 & 0.000011 & 0.9951 & 156.28 & 1.0000 & 0.024 & 0.016 \\ \hline
False & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.054 & 0.033 \\ \hline
False & False & 0.400 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.033 & 0.020 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


\noindent
Table~\ref{tab:texture_ablation_bpp_grid_v88} presents a $2 \times 2$ ablation study of ResNet50 with GA using a $128 \times 128$ secret image. The results show that the effective embedding rates exactly match the target payloads (\(\mathrm{BPP}_{\mathrm{used}} = 0.0992,\, 0.1953,\, 0.3966\)), with no indication of early saturation. The Genetic Algorithm (GA) is the primary factor preserving cover-image fidelity. Specifically, GA achieves $\mathrm{PSNR}_{c \rightarrow s}$ values of approximately $61$, $58$, and $55\,\mathrm{dB}$ at the three matched payload levels, whereas ResNet50 alone yields $55.69$, $52.71$, and $49.65\,\mathrm{dB}$, and disabling both modules results in $50.45$, $47.55$, and $45.17\,\mathrm{dB}$, respectively.When GA is enabled, $\mathrm{SSIM}_{c \rightarrow s}$ remains high at $0.9996$, $0.9992$, and $0.9984$, $\mathrm{MSE}_{c \rightarrow s}$ stays within the range of $10^{-6}$ to $3 \times 10^{-6}$, and $\mathrm{IF}_{c \rightarrow s}$ decreases only slightly from $0.9987$ to $0.9975$ as the payload increases. ResNet50 provides no measurable benefit when GA is active, as the results for the \textit{True/True} and \textit{False/True} configurations are effectively equivalent. Moreover, operating ResNet50 independently yields the lowest $\mathrm{PSNR}_{c \rightarrow s}$ values across all payload levels.Secret extraction remains perfectly lossless under all evaluated conditions, as indicated by $\mathrm{PSNR}_{s \rightarrow x} = \infty$ and $\mathrm{SSIM}_{s \rightarrow x} = 1.0000$. Execution time increases with both payload size and GA activation. In particular, combining GA with ResNet50 incurs a substantial computational cost at medium to high payloads (total execution time of approximately $0.331\,\mathrm{s}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.1953$) compared to GA alone (approximately $0.084\,\mathrm{s}$), without yielding any additional improvement in image quality.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 128*128 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and CNN}
\label{tab:texture_ablation_bpp_grid_v88}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 50.45 & 0.9975 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.020 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.026 \\ \hline
False & False & 0.100 & 0.0992 & 55.69 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.331 & 0.231 \\ \hline
True  & False & 0.200 & 0.1953 & 47.55 & 0.9952 & 0.000018 & 0.9940 & $\infty$ & 1.0000 & 0.159 & 0.077 \\ \hline
False & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.084 & 0.049 \\ \hline
False & False & 0.200 & 0.1953 & 52.71 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.047 & 0.029 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.295 & 0.120 \\ \hline
True  & False & 0.400 & 0.3966 & 45.17 & 0.9915 & 0.000030 & 0.9921 & $\infty$ & 1.0000 & 0.073 & 0.046 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.163 & 0.096 \\ \hline
False & False & 0.400 & 0.3966 & 49.65 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.090 & 0.056 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup

Table~\ref{tab:texture_ablation_bpp_grid_v99} presents a $2 \times 2$ ablation study involving ResNet50 and the Genetic Algorithm (GA) using a $512 \times 512$ secret image. The results show that the effective embedding rates exactly match the target payloads (\(\mathrm{BPP}_{\mathrm{used}} = 0.0992,\, 0.1953,\, 0.3966\)), indicating the absence of early saturation effects.The Genetic Algorithm (GA) is the dominant factor determining cover-image fidelity. With GA enabled, the peak signal-to-noise ratio from cover to stego (\(\mathrm{PSNR}_{c \rightarrow s}\)) reaches approximately $61$, $58$, and $55\,\mathrm{dB}$ at the three payload levels. In contrast, disabling both GA and ResNet50 reduces PSNR to $55.72$, $52.73$, and $49.64\,\mathrm{dB}$, while using ResNet50 alone yields the lowest values of $50.48$, $47.57$, and $45.15\,\mathrm{dB}$.When GA is active, the structural similarity index (\(\mathrm{SSIM}_{c \rightarrow s}\)) remains high at $0.9996$, $0.9992$, and $0.9984$, the mean squared error (\(\mathrm{MSE}_{c \rightarrow s}\)) stays within the range of $10^{-6}$ to $3 \times 10^{-6}$, and the image fidelity measure (\(\mathrm{IF}_{c \rightarrow s}\)) decreases only slightly with increasing payload, from $0.9987$ to $0.9974$. ResNet50 provides no measurable improvement when GA is enabled, as the results for the \textit{True/True} and \textit{False/True} configurations are effectively identical. Moreover, operating ResNet50 independently produces the lowest \(\mathrm{PSNR}_{c \rightarrow s}\) values across all payload levels.Secret extraction remains perfectly lossless under all evaluated configurations, as indicated by \(\mathrm{PSNR}_{s \rightarrow x} = \infty\) and \(\mathrm{SSIM}_{s \rightarrow x} = 1.0000\). Execution time increases with both GA activation and payload size. For example, at \(\mathrm{BPP}_{\mathrm{used}} = 0.3966\), the embedding and extraction times are approximately $0.161\,\mathrm{s}$ and $0.095\,\mathrm{s}$ with GA enabled, whereas without GA they range from $0.069$–$0.088\,\mathrm{s}$ and $0.053$–$0.059\,\mathrm{s}$, respectively.


%The value of Cap(bits) depends only on the target but not on the target value which indicates it shows available embedding locations. The actual embedded payload amount appears through the BPP_used measurement.
\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 512*512 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and CNN}
\label{tab:texture_ablation_bpp_grid_v99}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 50.48 & 0.9975 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.021 & 0.014 \\ \hline
False & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.048 & 0.027 \\ \hline
False & False & 0.100 & 0.0992 & 55.72 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.026 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.085 & 0.051 \\ \hline
True  & False & 0.200 & 0.1953 & 47.57 & 0.9953 & 0.000017 & 0.9940 & $\infty$ & 1.0000 & 0.035 & 0.023 \\ \hline
False & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.083 & 0.049 \\ \hline
False & False & 0.200 & 0.1953 & 52.73 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.046 & 0.029 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.161 & 0.095 \\ \hline
True  & False & 0.400 & 0.3966 & 45.15 & 0.9915 & 0.000031 & 0.9920 & $\infty$ & 1.0000 & 0.069 & 0.059 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.161 & 0.095 \\ \hline
False & False & 0.400 & 0.3966 & 49.64 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.088 & 0.053 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


\subsection{Imperceptibility and Capacity Results Using ResNet50 and GAN}

In this subsection, the performance of the ResNet50--GAN configuration is analyzed to assess how effectively deep texture extraction (via ResNet50) and adversarial learning (via GAN) contribute to improved image fidelity and embedding efficiency. The Genetic Algorithm (GA) is incorporated as an optimization component to enhance embedding adaptivity and achieve a better balance between imperceptibility and capacity. The analysis examines how these modules influence quantitative indicators such as PSNR, SSIM, MSE, and IF, as well as embedding and extraction times (TimeE and TimeX), across different target payloads ($0.100$, $0.200$, and $0.400$). The purpose of this evaluation is to highlight the impact of integrating GAN-driven feature enhancement and GA-based optimization on the overall quality, stability, and performance of the steganographic framework.Table~\ref{tab:texture_ablation_bpp_grid_v1010} presents a $2 \times 2$ ablation study of ResNet50 with GA using a $64 \times 64$ secret image. The system achieves an effective payload of $0.0992$ at $\mathrm{BPP}_{\mathrm{tg}} = 0.100$ and reaches a saturation point of $0.1250$ at $\mathrm{BPP}_{\mathrm{tg}} \in \{0.200, 0.400\}$, indicating that the maximum payload capacity is independent of the target setting beyond this threshold.The Genetic Algorithm (GA) proves essential for maintaining cover-image fidelity. Specifically, with GA enabled, the $\mathrm{PSNR}_{c \rightarrow s}$ values reach $60.9$--$61.1\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.0992$ and $59.9$--$60.0\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.1250$. Correspondingly, $\mathrm{SSIM}_{c \rightarrow s}$ remains high in the range $0.9995$--$0.9996$, $\mathrm{MSE}_{c \rightarrow s}$ approaches $10^{-6}$, and $\mathrm{IF}_{c \rightarrow s}$ lies between $0.9985$ and $0.9987$.In contrast, when GA is disabled, the $\mathrm{PSNR}_{c \rightarrow s}$ values decrease to $54.7$--$55.7\,\mathrm{dB}$ when ResNet50 is also disabled. The ResNet50-only configuration yields the lowest performance, with $\mathrm{PSNR}_{c \rightarrow s}$ values in the range $46$--$47\,\mathrm{dB}$, demonstrating that GA contributes more significantly to cover-image quality than ResNet50 when used independently.Secret extraction remains perfectly lossless across all tested scenarios, as indicated by $\mathrm{PSNR}_{s \rightarrow x} = \infty$ at $\mathrm{BPP}_{\mathrm{used}} = 0.0992$ and approximately $156.28\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.1250$, while $\mathrm{SSIM}_{s \rightarrow x}$ consistently remains at $1.0000$. Execution time increases when GA is enabled and also rises slightly with increasing payload size. Under GA-based configurations, embedding and extraction times (TimeE and TimeX) range from $0.042$ to $0.053\,\mathrm{s}$ and from $0.026$ to $0.046\,\mathrm{s}$, respectively. In comparison, the ResNet50-only configuration achieves shorter execution times, with TimeE ranging from $0.016$ to $0.020\,\mathrm{s}$ and TimeX from $0.012$ to $0.014\,\mathrm{s}$.

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 64*64 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and GAN}
\label{tab:texture_ablation_bpp_grid_v1010}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 60.90 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.042 & 0.026 \\ \hline
True  & False & 0.100 & 0.0992 & 47.23 & 0.9956 & 0.000019 & 0.9937 & $\infty$ & 1.0000 & 0.016 & 0.012 \\ \hline
False & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.078 & 0.048 \\ \hline
False & False & 0.100 & 0.0992 & 55.71 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.048 & 0.028 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 59.86 & 0.9995 & 0.000001 & 0.9985 & 156.28 & 1.0000 & 0.052 & 0.032 \\ \hline
True  & False & 0.200 & 0.1250 & 46.28 & 0.9948 & 0.000024 & 0.9930 & 156.28 & 1.0000 & 0.020 & 0.014 \\ \hline
False & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.050 & 0.031 \\ \hline
False & False & 0.200 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.029 & 0.020 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 59.86 & 0.9995 & 0.000001 & 0.9985 & 156.28 & 1.0000 & 0.052 & 0.033 \\ \hline
True  & False & 0.400 & 0.1250 & 46.28 & 0.9948 & 0.000024 & 0.9930 & 156.28 & 1.0000 & 0.020 & 0.014 \\ \hline
False & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.053 & 0.046 \\ \hline
False & False & 0.400 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.030 & 0.019 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:texture_ablation_bpp_grid_v1111} presents a $2 \times 2$ ablation study involving ResNet50 and the Genetic Algorithm (GA) using a $128 \times 128$ secret image. The results show that the effective embedding rates exactly match the target payloads (\(\mathrm{BPP}_{\mathrm{used}} = 0.0992,\, 0.1953,\, 0.3966\)), with no indication of early saturation.The Genetic Algorithm (GA) maintains high cover-image fidelity across all three payload levels. Specifically, the peak signal-to-noise ratio from cover to stego (\(\mathrm{PSNR}_{c \rightarrow s}\)) reaches approximately \(61\), \(58\), and \(55\,\mathrm{dB}\), while the structural similarity index (\(\mathrm{SSIM}_{c \rightarrow s}\)) attains values of \(0.9996\), \(0.9992\), and \(0.9984\). The mean squared error (\(\mathrm{MSE}_{c \rightarrow s}\)) remains within the range of \(10^{-6}\) to \(3 \times 10^{-6}\), and the image fidelity measure (\(\mathrm{IF}_{c \rightarrow s}\)) decreases only slightly from \(0.9987\) to \(0.9975\) as the payload increases.When GA is disabled, the \(\mathrm{PSNR}_{c \rightarrow s}\) values decrease to approximately \(55.69\), \(52.71\), and \(49.65\,\mathrm{dB}\). Using ResNet50 alone yields the lowest cover-image fidelity, with \(\mathrm{PSNR}_{c \rightarrow s}\) values of approximately \(47.22\), \(44.54\), and \(42.16\,\mathrm{dB}\). Combining ResNet50 with GA produces results identical to those obtained with GA alone, as the \textit{True/True} and \textit{False/True} configurations yield equivalent performance.Secret extraction remains perfectly lossless under all tested conditions, as indicated by \(\mathrm{PSNR}_{s \rightarrow x} = \infty\) and \(\mathrm{SSIM}_{s \rightarrow x} = 1.0000\). Processing time increases with higher payloads and GA activation. However, combining GA with ResNet50 results in slightly shorter execution times than using GA alone at medium and high payload levels (e.g., \(\mathrm{TimeE} = 0.099\,\mathrm{s}\) versus \(0.148\,\mathrm{s}\) at \(\mathrm{BPP}_{\mathrm{used}} = 0.1953\), and \(\mathrm{TimeE} = 0.177\,\mathrm{s}\) versus \(0.163\,\mathrm{s}\) at \(\mathrm{BPP}_{\mathrm{used}} = 0.3966\)).




\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 128*128 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and GAN}
\label{tab:texture_ablation_bpp_grid_v1111}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 47.22 & 0.9957 & 0.000019 & 0.9937 & $\infty$ & 1.0000 & 0.018 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.026 \\ \hline
False & False & 0.100 & 0.0992 & 55.69 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.026 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.099 & 0.048 \\ \hline
True  & False & 0.200 & 0.1953 & 44.54 & 0.9923 & 0.000035 & 0.9915 & $\infty$ & 1.0000 & 0.033 & 0.021 \\ \hline
False & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.148 & 0.092 \\ \hline
False & False & 0.200 & 0.1953 & 52.71 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.084 & 0.055 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.177 & 0.101 \\ \hline
True  & False & 0.400 & 0.3966 & 42.16 & 0.9861 & 0.000061 & 0.9888 & $\infty$ & 1.0000 & 0.065 & 0.040 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.163 & 0.094 \\ \hline
False & False & 0.400 & 0.3966 & 49.65 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.089 & 0.053 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:texture_ablation_bpp_grid_v1222} presents a $2 \times 2$ ablation study involving ResNet50 and the Genetic Algorithm (GA) using a $512 \times 512$ secret image. The results show that the effective embedding rates exactly match the target payloads (\(\mathrm{BPP}_{\mathrm{used}} = 0.0992,\, 0.1953,\, 0.3966\)), indicating operation within the requested payload range without any signs of early saturation.

The Genetic Algorithm (GA) is the primary factor responsible for improving cover-image fidelity. When GA is enabled, the system achieves $\mathrm{PSNR}_{c \rightarrow s}$ values of approximately $61$, $58$, and $55\,\mathrm{dB}$ at the three payload levels, with corresponding $\mathrm{SSIM}_{c \rightarrow s}$ values of $0.9996$, $0.9992$, and $0.9984$. At the same time, $\mathrm{MSE}_{c \rightarrow s}$ remains within the range of $10^{-6}$ to $3 \times 10^{-6}$, and $\mathrm{IF}_{c \rightarrow s}$ decreases only slightly as the payload increases, from $0.9987$ to $0.9974$.ResNet50 provides no measurable improvement when GA is active, as the \textit{True/True} configuration yields results equivalent to the \textit{False/True} configuration. When operated independently, ResNet50 produces the lowest cover-image fidelity, with $\mathrm{PSNR}_{c \rightarrow s}$ values of approximately $47.24\,\mathrm{dB}$, $44.58\,\mathrm{dB}$, and $42.13\,\mathrm{dB}$. Disabling both GA and ResNet50 results in intermediate performance, with $\mathrm{PSNR}_{c \rightarrow s}$ values of $55.72$, $52.73$, and $49.64\,\mathrm{dB}$ across the three payload levels.Secret extraction remains perfectly lossless under all evaluated conditions, as indicated by $\mathrm{PSNR}_{s \rightarrow x} = \infty$ and $\mathrm{SSIM}_{s \rightarrow x} = 1.0000$. Processing time increases with both payload size and GA activation. Specifically, the embedding and extraction times (\(\mathrm{TimeE}/\mathrm{TimeX}\)) increase from approximately $0.045/0.027\,\mathrm{s}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.0992$ to $0.172/0.097\,\mathrm{s}$ at $\mathrm{BPP}_{\mathrm{used}} = 0.3966$ when GA is enabled, whereas configurations without GA exhibit lower execution times.

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 512*512 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and GAN}
\label{tab:texture_ablation_bpp_grid_v1222}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.027 \\ \hline
True  & False & 0.100 & 0.0992 & 47.24 & 0.9957 & 0.000019 & 0.9937 & $\infty$ & 1.0000 & 0.019 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.062 & 0.029 \\ \hline
False & False & 0.100 & 0.0992 & 55.72 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.018 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.083 & 0.052 \\ \hline
True  & False & 0.200 & 0.1953 & 44.58 & 0.9923 & 0.000035 & 0.9915 & $\infty$ & 1.0000 & 0.034 & 0.021 \\ \hline
False & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.081 & 0.048 \\ \hline
False & False & 0.200 & 0.1953 & 52.73 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.046 & 0.033 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.172 & 0.097 \\ \hline
True  & False & 0.400 & 0.3966 & 42.13 & 0.9861 & 0.000061 & 0.9887 & $\infty$ & 1.0000 & 0.065 & 0.044 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.161 & 0.095 \\ \hline
False & False & 0.400 & 0.3966 & 49.64 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.095 & 0.056 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


%\subsection{Security Analysis Using Xu-Net, VGG16 and CNN }
%\begin{figure}[htbp]
  %\centering
  %\begin{subfigure}{0.32\textwidth}
   % \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.1.PNG}
   % \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
  %\end{subfigure}\hfill
  %\begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.2.PNG}
   % \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
  %\end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
   % \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.4.PNG}
   % \caption{BPP = 0.4}
   % \label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
  %\caption{Xu-Net Convusion (VGG16+CNN) at different BPP.}
  %\label{fig:xu_hed_cnn_conf_all11}
%\end{figure}


%\begin{figure}[htbp]
  %\centering
 % \begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.1.PNG}
   % \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
  %  \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.2.PNG}
   % \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
  %\end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.4.PNG}
  %  \caption{BPP = 0.4}
  %  \label{fig:xu_hed_cnn_conf_0_4}
 % \end{subfigure}
 % \caption{Xu-Net ROC (VGG16+CNN) at different BPP.}
 % \label{fig:xu_hed_cnn_conf_all10}
%\end{figure}

%\subsection{Security Analysis Using Xu-Net,  VGG16 and GAN }

%\begin{figure}[htbp]
%  \centering
%  \begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.1.PNG}
   % \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
  %\end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.2.PNG}
  %  \caption{BPP = 0.2}
  %  \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
%  \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.4.PNG}
   % \caption{BPP = 0.4}
  %  \label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
  %\caption{Xu-Net Convusion (VGG16+GAN) at different BPP.}
  %\label{fig:xu_hed_cnn_conf_all11}
%\end{figure}


%\begin{figure}[htbp]
%  \centering
%  \begin{subfigure}{0.32\textwidth}
 %   \centering
 %   \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.1.PNG}
  %  \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
  %\end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
 %   \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.2.PNG}
 %   \caption{BPP = 0.2}
 %   \label{fig:xu_hed_cnn_conf_0_2}
%  \end{subfigure}\hfill
%  \begin{subfigure}{0.32\textwidth}
%    \centering
%    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.4.PNG}
%    \caption{BPP = 0.4}
 %   \label{fig:xu_hed_cnn_conf_0_4}
%  \end{subfigure}
%  \caption{Xu-Net ROC (VGG16+GAN) at different BPP.}
 % \label{fig:xu_hed_cnn_conf_all10}
%\end{figure}

%\subsection{Security Analysis Using Xu-Net, ResNet50 and CNN }


%\begin{figure}[htbp]
%  \centering
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.1.PNG}
  %  \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
 %   \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.2.PNG}
  %  \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.4.PNG}
  %  \caption{BPP = 0.4}
  %  \label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
  %\caption{Xu-Net Convusion (ResNet50+CNN) at different BPP.}
  %\label{fig:xu_hed_cnn_conf_all11}
%\end{figure}


%\begin{figure}[htbp]
 % \centering
  %\begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.1.PNG}
  %  \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
  %\end{subfigure}\hfill
  %\begin{subfigure}{0.32\textwidth}
   % \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.2.PNG}
   % \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
  %\begin{subfigure}{0.32\textwidth}
  %  \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.4.PNG}
   % \caption{BPP = 0.4}
   % \label{fig:xu_hed_cnn_conf_0_4}
 % \end{subfigure}
 % \caption{Xu-Net ROC (ResNet50+CNN) at different BPP.}
 % \label{fig:xu_hed_cnn_conf_all10}
%\end{figure}


%\subsection{Security Analysis Using Xu-Net,  ResNet50 and GAN }

%\begin{figure}[htbp]
%  \centering
%  \begin{subfigure}{0.32\textwidth}
%    \centering
 %   \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.1.PNG}
  %  \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.2.PNG}
   % \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
  %\begin{subfigure}{0.32\textwidth}
   % \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.4.PNG}
    %\caption{BPP = 0.4}
    %\label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
 % \caption{Xu-Net Convusion (ResNet50+GAN) at different BPP.}
  %\label{fig:xu_hed_cnn_conf_all11}
%\end{figure}


%\begin{figure}[htbp]
 % \centering
  %\begin{subfigure}{0.32\textwidth}
  %  \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.1.PNG}
  %  \caption{BPP = 0.1}
  %  \label{fig:xu_hed_cnn_conf_0_1}
%  \end{subfigure}\hfill
%  \begin{subfigure}{0.32\textwidth}
%    \centering
%    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.2.PNG}
%    \caption{BPP = 0.2}
%    \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
  %  \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.4.PNG}
 %   \caption{BPP = 0.4}
 %   \label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
 % \caption{Xu-Net ROC (ResNet50+GAN) at different BPP.}
 % \label{fig:xu_hed_cnn_conf_all10}
%\end{figure}



%\begin{table}[ht]
%\centering
%\begin{tabular}{lcc}
%\hline
%\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
%\hline
%BPP\_0.1\_ResNet50\_CNN  & 50.00\% & 0.5375 \\
%BPP\_0.2\_ResNet50\_CNN  & 50.00\% & 0.5500 \\
%BPP\_0.4\_ResNet50\_CNN  & 50.00\% & 0.5500 \\
%BPP\_0.1\_ResNet50\_GAN  & 50.00\% & 0.5375 \\
%BPP\_0.2\_ResNet50\_GAN  & 50.00\% & 0.5450 \\
%BPP\_0.4\_ResNet50\_GAN  & 50.00\% & 0.5450 \\
%\hline
%\end{tabular}
%\caption{Xu-Net Results Using ResNet50}
%\end{table}

%\begin{table}[ht]
%\centering
%\begin{tabular}{lcc}
%\hline
%\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
%\hline
%BPP\_0.1\_VGG16\_CNN  & 50.00\% & 0.5325 \\
%BPP\_0.2\_VGG16\_CNN  & 50.00\% & 0.5175 \\
%BPP\_0.4\_VGG16\_CNN  & 50.00\% & 0.5175 \\
%BPP\_0.1\_VGG16\_GAN  & 50.00\% & 0.5325 \\
%BPP\_0.2\_VGG16\_GAN  & 50.00\% & 0.5175 \\
%BPP\_0.4\_VGG16\_GAN  & 50.00\% & 0.5175 \\
%\hline
%\end{tabular}
%\caption{Xu-Net Results Using VGG16  }
%\end{table}


\subsection{Security Analysis Using Texture Maps}
Evaluating the security performance of a steganographic system is essential for determining its resistance to advanced steganalysis methods. In this subsection, the focus is on analyzing the security behavior of texture-based embedding models that utilize deep feature representations, such as texture maps derived from convolutional neural networks (CNNs). These texture maps capture spatial and structural patterns that guide the embedding process and may influence the detectability of hidden information. The security analysis examines how effectively the system conceals embedded data when confronted with modern deep-learning steganalyzers, including Xu-Net and Ye-Net. By comparing different configurations involving texture feature extraction and optimization modules, this analysis aims to assess the level of imperceptibility, resilience, and statistical indistinguishability achieved by texture-guided embedding methods under varying payload conditions.

\subsubsection{Security Analysis Using Xu-Net, VGG16, and CNN}

In this subsection, the Xu-Net steganalyzer is employed to evaluate the detectability of stego images generated using the VGG16+CNN embedding framework. The VGG16 network provides rich texture features that enhance spatial detail during embedding, while the CNN module performs adaptive encoding to preserve image fidelity. The objective of this analysis is to determine whether the combination of these components introduces detectable artifacts that Xu-Net can exploit to distinguish between cover and stego images. By analyzing confusion matrices and performance metrics—such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy—across multiple payload levels ($\mathrm{BPP} = 0.1, 0.2, 0.4$), this evaluation provides insight into the detectability, consistency, and resilience of the VGG16-based embedding approach under CNN-driven steganalysis.Figure~\ref{fig:xu_hed_cnn_conf_all112} presents the confusion matrices obtained by \textbf{Xu-Net (VGG16+CNN)} at different payload rates $\mathrm{BPP} \in \{0.1, 0.2, 0.4\}$ using a balanced test set of 40 images (20 cover and 20 stego images). Across all payload levels, the confusion matrices are identical, with $\mathrm{TN}=7$, $\mathrm{FP}=13$, $\mathrm{FN}=7$, and $\mathrm{TP}=13$. This yields an overall classification accuracy of 50\%, a true positive rate of 0.65, a true negative rate of 0.35, and a balanced accuracy of 0.50. These results indicate near–chance-level discrimination, with a noticeable bias toward predicting the \emph{stego} class due to the high false positive rate on cover images.The detection performance remains unchanged as the payload increases from 0.1 to 0.4~BPP, suggesting that this particular VGG16+CNN configuration does not increase detectability under the evaluated Xu-Net setting.


%The specific Xu--Net (VGG16+CNN) configuration fails to detect this steganographic method on this dataset but this result does not prove overall security because it needs verification through better detectors and separate datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (VGG16+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all112}
\end{figure}

The ROC curves shown in Figure~\ref{fig:xu_hed_cnn_conf_all102} illustrate the performance of Xu-Net (VGG16+CNN) at different payload rates ($\mathrm{BPP} = 0.1, 0.2, 0.4$). The corresponding area-under-the-curve (AUC) values are approximately $0.53$, $0.52$, and $0.53$, which are close to the random-guess baseline of $0.5$. Across all panels, the ROC curves remain near the diagonal reference line, with AUC values of about $0.53$ at $\mathrm{BPP} = 0.1$ and $0.52$ at $\mathrm{BPP} = 0.2$ and $0.4$.These near–chance-level values indicate that the detector operates at an equal-error rate of approximately $50\%$ and lacks any effective operating point that simultaneously achieves a high true positive rate (TPR) and a low false positive rate (FPR). The minimal variation in AUC as the payload rate increases further suggests that changes in BPP do not enhance detectability under this configuration. Overall, the results demonstrate that the specific Xu-Net (VGG16+CNN) detector fails to reliably detect the proposed embedding method on the evaluated dataset.

%but this result does not prove overall security because additional tests with different detectors and datasets are needed.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (VGG16+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all102}
\end{figure}

\subsubsection{Security Analysis Using Xu-Net,VGG16 and GAN }
In this subsection, the Xu-Net detector is applied to analyze the detectability of stego images generated using the VGG16+GAN embedding framework. The VGG16 network serves as a texture feature extractor that guides the embedding process by preserving fine structural details, while the GAN component introduces adversarial learning to enhance the naturalness and realism of the generated stego images. The primary objective of this analysis is to investigate whether Xu-Net can effectively distinguish between cover and stego images across different payload capacities ($\mathrm{BPP} = 0.1, 0.2, 0.4$). By examining confusion matrices and key performance indicators such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy, this evaluation highlights the resilience and undetectability of the VGG16+GAN–based embedding scheme under deep learning–based steganalysis.

Figure~\ref{fig:xu_hed_cnn_conf_all113} presents the confusion matrices obtained by \textbf{Xu-Net (VGG16+GAN)} at payload rates $\mathrm{BPP} \in {0.1, 0.2, 0.4}$ when evaluated on a balanced dataset consisting of 40 images (20 cover and 20 stego images). Across all payload levels, the confusion matrices are identical, with $\mathrm{TN} = 7$, $\mathrm{FP} = 13$, $\mathrm{FN} = 7$, and $\mathrm{TP} = 13$. This yields an overall classification accuracy of 50\%, a TPR of 0.65, a TNR of 0.35, and a balanced accuracy of 0.50.The classifier exhibits a bias toward predicting the \emph{stego} class, as reflected by the high false positive rate on cover images. Consequently, the detection performance corresponds to near–chance-level discrimination, with no effective operating threshold and an equal-error rate (EER) of approximately 50\%. Furthermore, the detector fails to capture any variation in detectability as the payload size increases, since the results remain unchanged across all tested BPP value.
%The specific Xu--Net (VGG16+GAN) detector fails to detect this steganographic method on this dataset but this result does not prove overall security because it needs verification through other detectors and different datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (VGG16+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all113}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_conf_all104} illustrates the ROC curves for \textbf{Xu-Net (VGG16+GAN)} at different payload rates, $\mathrm{BPP} \in {0.1, 0.2, 0.4}$. The ROC curves exhibit a staircase pattern that remains close to the diagonal reference line, yielding area-under-the-curve (AUC) values of approximately 0.53 at $\mathrm{BPP}=0.1$ and approximately 0.52 at $\mathrm{BPP}=0.2$ and $0.4$. These values are only marginally above the random-guess baseline of 0.5.The near–chance-level AUC values indicate that the detector operates at an equal-error rate of approximately 50\% and that no operating point exists that simultaneously achieves a high true positive rate and a low false positive rate. Furthermore, the minimal variation in detection performance across increasing payload rates suggests that higher BPP values do not improve detectability under this configuration. Overall, these results demonstrate that the Xu-Net (VGG16+GAN) detector is unable to reliably distinguish between cover and stego images on the evaluated dataset.

%The specific Xu--Net (VGG16+GAN) detector fails to detect this steganographic method on this dataset but this result does not provide universal security assurance because it needs verification with other detectors and different datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (VGG16+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all104}
\end{figure}

\subsubsection{Security Analysis Using Xu-Net,ResNet50 and CNN }

In this subsection, the focus is on evaluating the Xu-Net detector to analyze its ability to identify hidden information embedded using texture-guided mapping techniques. Texture maps—derived from deep architectures such as VGG16, ResNet50, or GAN-based models—capture fine-grained structural and statistical patterns that guide adaptive embedding in visually complex regions. The objective of this analysis is to determine whether these texture-driven embedding strategies introduce detectable artifacts that Xu-Net can exploit to distinguish between cover and stego images. By examining confusion matrices and performance metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy across multiple payload levels, this subsection provides a detailed assessment of the detectability, robustness, and security of texture-based embedding frameworks under CNN-driven steganalysis.Figure~\ref{fig:xu_hed_cnn_conf_all115} presents the confusion matrices obtained by \textbf{Xu-Net (ResNet50+CNN)} at payload rates $\mathrm{BPP} \in {0.1, 0.2, 0.4}$ using a balanced test set consisting of 20 cover images and 20 stego images. At $\mathrm{BPP} = 0.1$, the classifier reports 4 true negatives, 16 false positives, 4 false negatives, and 16 true positives, resulting in an overall accuracy of 50%, a TPR of 0.80, a TNR of 0.20, and a balanced accuracy of 0.50.

At higher payload rates of $\mathrm{BPP} = 0.2$ and $\mathrm{BPP} = 0.4$, the classifier predicts all samples as stego images, yielding $\mathrm{TN} = 0$, $\mathrm{FP} = 20$, $\mathrm{FN} = 0$, and $\mathrm{TP} = 20$. Under these conditions, the overall accuracy remains at 50\%, with a TPR of 1.00, a TNR of 0.00, and a balanced accuracy of 0.50. These results indicate near–chance-level detection performance and the absence of an effective operating threshold. Furthermore, detection performance does not improve as the payload increases, suggesting that Xu-Net is unable to exploit payload variations to enhance detectability for this texture-guided embedding configurations.
%The specific Xu--Net (ResNet50+CNN) detector fails to detect this steganographic embedding on this dataset but this result does not prove overall security because it needs verification through low ROC/AUC scores from multiple detectors on separate datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (ResNet50+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all115}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_conf_all106} presents the ROC curves for \textbf{Xu-Net (ResNet50+CNN)} at payload rates $\mathrm{BPP} \in {0.1, 0.2, 0.4}$. The curves exhibit a diagonal, staircase-like pattern, resulting in AUC values of approximately ${0.54, 0.56, 0.55}$, which are only slightly higher than the random-guess baseline (AUC = 0.5). Across all panels, the AUC values remain close to $0.54$ at $\mathrm{BPP} = 0.1$ and approximately $0.55$ at $\mathrm{BPP} = 0.2$ and $0.4$.The ROC curves indicate weak discriminative capability, as they remain close to the diagonal reference line. This behavior corresponds to an equal-error rate of approximately 50\%, and no operating point is observed that simultaneously achieves a high true positive rate (TPR) and a low false positive rate (FPR). Any apparent improvement in TPR occurs only when the FPR reaches relatively high levels. Furthermore, the minimal variation in performance as the payload rate increases suggests that higher BPP values do not enhance detection capability under this configuration.Overall, these results demonstrate that the Xu-Net (ResNet50+CNN) detector operates at near–chance-level performance and is unable to reliably distinguish between cover and stego images for the evaluated payloads.
%The embedding shows resistance to this particular detector on this particular dataset but this result does not prove overall security because it needs verification with more robust detectors and different datasets along with confidence interval analysis.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (ResNet50+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all106}
\end{figure}


\subsubsection{Security Analysis Using Xu-Net,ResNet50 and GAN }
In this subsection, the Xu-Net detector is employed to analyze the detectability of stego images generated using the ResNet50+GAN embedding framework. The ResNet50 component serves as a deep feature extractor that captures high-level texture and structural characteristics, while the GAN model contributes to generating visually natural and statistically consistent stego images through adversarial learning. The objective of this evaluation is to determine whether Xu-Net can effectively discriminate between cover and stego images across multiple payload rates ($\mathrm{BPP} = 0.1, 0.2, 0.4$). By examining confusion matrices and key performance metrics—such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy—this analysis provides insight into the detectability, bias, and overall security resilience of the ResNet50+GAN-based embedding method under CNN-based steganalysis.Figure~\ref{fig:xu_hed_cnn_conf_all1166} presents the confusion matrices obtained by Xu-Net for the \textbf{ResNet50+GAN} configuration at payload rates $\mathrm{BPP} \in {0.1, 0.2, 0.4}$, evaluated on a balanced test set consisting of 20 cover images and 20 stego images. Across all payload levels, the confusion matrices are identical, with $\mathrm{TN} = 4$, $\mathrm{FP} = 16$, $\mathrm{FN} = 4$, and $\mathrm{TP} = 16$. This results in an overall classification accuracy of 50\%, a TPR of 0.80, a TNR of 0.20, and a balanced accuracy of 0.50.These results indicate that the classifier exhibits a strong bias toward predicting the \emph{stego} class, as reflected by the high number of false positives on cover images. Consequently, the detector operates at near–chance-level performance with no effective decision threshold. Furthermore, detection performance remains unchanged as the payload rate increases, indicating that this configuration does not improve detectability under higher embedding capacities.

%The specific detector fails to detect this steganographic method when applied to this dataset although this result does not prove overall security because additional tests with different detectors and datasets are required.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (ResNet50+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1166}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_conf_all1088} presents the ROC curve performance of the detector at payload rates of $\mathrm{BPP} = 0.1, 0.2,$ and $0.4$. Across all three cases, the ROC curves remain close to the diagonal reference line, with area-under-the-curve (AUC) values of approximately 0.54, indicating near–random discrimination and an equal-error rate of about 50\%.The true positive rate (TPR) increases noticeably only when the false positive rate (FPR) becomes high, which prevents the identification of any meaningful operating point that simultaneously achieves a high TPR and a low FPR. Moreover, the minimal variation in performance across different payload values suggests that increasing the BPP does not enhance detection capability under this system configuration.Overall, these results indicate that the proposed steganographic embedding approach exhibits resistance to this particular detector when evaluated on the considered dataset.
%but this result does not ensure overall security because it needs verification through tests with different detectors and separate datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (ResNet50+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1088}
\end{figure}

The results reported in the table demonstrate that Xu-Net with a ResNet50 backbone operates at chance-level performance when using either a plain CNN head or a GAN-based component across all evaluated payload sizes. The classification accuracy remains fixed at 50.00\% for all configurations, while the area under the ROC curve (AUC) ranges between 0.5375 and 0.5500. These AUC values exhibit only marginal variation as the payload increases from 0.1 to 0.4~BPP, and no meaningful performance difference is observed between the CNN and GAN variants.The small AUC fluctuations (on the order of 0.0125) fall within the range of expected random variation and therefore do not indicate a genuine improvement in detection capability. Overall, the constant detection performance across increasing payload levels suggests that the current preprocessing strategy and network architecture are unable to extract informative embedding residuals. Consequently, Xu-Net with a ResNet50 backbone fails to leverage higher embedding rates to improve steganalysis performance under the evaluated conditions.
%The detector fails to distinguish between stego and cover images which requires the implementation of residual front-ends (SRM/HPF) and ROC/AUC threshold calibration with confidence intervals and larger datasets and specialized steganalysis architectures (SRNet or Yedroudj-Net).


\begin{table}[ht]
\centering
\begin{tabular}{lcc}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
\hline
BPP\_0.1\_ResNet50\_CNN_GA  & 50.00\% & 0.5375 \\
BPP\_0.2\_ResNet50\_CNN_GA  & 50.00\% & 0.5500 \\
BPP\_0.4\_ResNet50\_CNN_GA  & 50.00\% & 0.5500 \\
BPP\_0.1\_ResNet50\_GAN_GA  & 50.00\% & 0.5375 \\
BPP\_0.2\_ResNet50\_GAN_GA  & 50.00\% & 0.5450 \\
BPP\_0.4\_ResNet50\_GAN _GA & 50.00\% & 0.5450 \\
\hline
\end{tabular}
\caption{Xu-Net Results Using ResNet50}
\end{table}

The table named \textit{Xu-Net Results Using VGG16} demonstrates that Xu-Net with VGG16 backbone achieves random performance when using either a plain CNN head or a GAN component for all payload sizes. The Accuracy remains constant at 50.00\% in all configurations while the ROC AUC values reach only slightly above random performance at 0.5325 for BPP=0.1 and 0.5175 for BPP=0.2 and BPP=0.4. The AUC values between the CNN and GAN variants show no significant difference because the small variations could stem from normal statistical fluctuations rather than actual performance improvements. The observed 50\% accuracy with AUC values between 0.52 and 0.53 indicates that this VGG16-based Xu-Net configuration does not use embedding residuals effectively to detect stego images from cover images under the present preprocessing and training conditions and architectural setup.
\begin{table}[ht]
\centering
\begin{tabular}{lcc}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
\hline
BPP\_0.1\_VGG16\_CNN_GA  & 50.00\% & 0.5325 \\
BPP\_0.2\_VGG16\_CNN_GA  & 50.00\% & 0.5175 \\
BPP\_0.4\_VGG16\_CNN_GA  & 50.00\% & 0.5175 \\
BPP\_0.1\_VGG16\_GAN_GA  & 50.00\% & 0.5325 \\
BPP\_0.2\_VGG16\_GAN_GA  & 50.00\% & 0.5175 \\
BPP\_0.4\_VGG16\_GAN_GA  & 50.00\% & 0.5175 \\
\hline
\end{tabular}
\caption{Xu-Net Results Using VGG16  }
\end{table}


\subsubsection{Security Analysis Using Ye-Net,VGG16 and CNN }

In this subsection, the Ye-Net detector is employed to evaluate the performance of the VGG16+CNN embedding framework, which combines deep texture feature extraction with adaptive embedding through convolutional layers. The VGG16 network captures fine spatial and structural details, while the CNN-based encoder ensures controlled data distribution within complex image textures. The objective of this analysis is to investigate whether Ye-Net can effectively distinguish between cover and stego images across various payload capacities ($\mathrm{BPP} = 0.1, 0.2, 0.4$). By examining confusion matrices and performance metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy, this evaluation provides insight into the detectability, statistical indistinguishability, and overall security robustness of the VGG16+CNN–based embedding method under deep learning–based steganalysis.

Figure~\ref{fig:xu_hed_cnn_conf_all111} presents the confusion matrices obtained by Ye-Net for the \textbf{VGG16+CNN} configuration at payload rates of 0.1, 0.2, and 0.4~BPP. Across all tested payload levels, the classifier produces identical results by assigning every sample to the \emph{cover} class. Consequently, each confusion matrix reports $\mathrm{TN} = 20$, $\mathrm{FP} = 0$, $\mathrm{FN} = 20$, and $\mathrm{TP} = 0$. This yields an overall accuracy of 50\%, a true positive rate of 0\%, a true negative rate of 100\%, and a balanced accuracy of 50\%.These results indicate chance-level discrimination, corresponding to an expected area under the ROC curve (AUC) of 0.5 and an equal-error rate (EER) of 50\%. The detector fails to detect any variation in payload size, as performance remains unchanged across all tested BPP values. Overall, this behavior suggests that the proposed VGG16+CNN embedding method exhibits strong resistance to detection by Ye-Net on the evaluated dataset, although this result should be interpreted within the context of the specific detector configuration and dataset used.
%but additional tests with different detectors and datasets are needed to confirm security.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (VGG16+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all111}
\end{figure}

The ROC curves for Ye-Net (VGG16+CNN) at different payload rates ranging from $\mathrm{BPP} = 0.1$ to $0.4$ are shown in Figure~\ref{fig:xu_hed_cnn_conf_all101}. The curves exhibit no meaningful deviation from the diagonal reference line, with AUC values of 0.5000 at $\mathrm{BPP} = 0.1$, 0.5088 at $\mathrm{BPP} = 0.2$, and 0.4837 at $\mathrm{BPP} = 0.4$. These values indicate near–chance-level detection performance.
The results demonstrate that the detector operates at an equal-error rate of approximately 50\%, as no operating point exists that simultaneously achieves a high true positive rate and a low false positive rate. Moreover, detection performance does not improve as the payload size increases, indicating that this configuration is insensitive to changes in embedding capacity. Overall, these findings suggest that the proposed embedding method exhibits resistance to detection by Ye-Net under the VGG16+CNN configuration on the evaluated dataset.


\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (VGG16+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all101}
\end{figure}

\subsubsection{Security Analysis Using Ye-Net,VGG16 and GAN }

In this subsection, the performance of the Ye-Net detector is analyzed against stego images generated using the VGG16+GAN embedding framework. In this configuration, VGG16 extracts fine-grained texture and structural patterns, while the GAN component produces a more natural and statistically consistent embedding distribution across textured regions. A balanced test set and multiple payload rates ($\mathrm{BPP} = 0.1, 0.2, 0.4$) are used to evaluate the detector’s sensitivity to changes in embedding capacity. Key performance indicators—such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy—are employed to assess the detector’s discriminative capability. This analysis establishes the context for determining whether the Ye-Net versus VGG16+GAN configuration provides meaningful detection capability or operates at chance level, and what these outcomes imply for its operating threshold and overall ROC/AUC behavior.Figure~\ref{fig:xu_hed_cnn_conf_all111} presents the confusion matrices obtained by Ye-Net for the \textbf{VGG16+GAN} configuration at payload rates of 0.1, 0.2, and 0.4~BPP. Across all tested payload levels, the detector misclassifies all samples by labeling every image as a \emph{cover} image. Consequently, each confusion matrix reports $\mathrm{TN} = 20$, $\mathrm{FP} = 0$, $\mathrm{FN} = 20$, and $\mathrm{TP} = 0$. This results in an overall classification accuracy of 50\%, a true positive rate of 0\%, a true negative rate of 100\%, and a balanced accuracy of 50\%.
These outcomes indicate chance-level discrimination, with no effective operating threshold for distinguishing between cover and stego images. Furthermore, detection performance remains unchanged as the payload rate increases, suggesting that this specific Ye-Net configuration is insensitive to variations in embedding capacity. Overall, the results demonstrate that the Ye-Net detector fails to detect the VGG16+GAN-based steganographic embedding on the evaluated dataset.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_Conv_0.4.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_Conv_0.2.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (VGG16+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all111}
\end{figure}

The ROC curves for Ye-Net (VGG16+GAN) at BPP=0.1, 0.2, 0.4 appear in Figure~\ref{fig:xu_hed_cnn_conf_all1011}. The curves show no deviation from the diagonal line because AUC equals 0.5000 at 0.1 and AUC equals 0.5088 at 0.2 and 0.4. The near-chance values indicate that the system maintains a 50\% error rate while failing to find any operating point that would produce high true positives with low false positives. The minimal AUC increase when BPP rises indicates that this configuration does not enhance detection capabilities in real-world scenarios. The embedding shows resistance to this particular detector on this dataset.
%but this finding does not provide universal security assurance because it needs verification through tests on different detectors and independent data sets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_ROC_0.4.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_ROC_0.2.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (VGG16+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1011}
\end{figure}

\subsubsection{Security Analysis Using Ye-Net,ResNet50 and CNN }
In this subsection, the Ye-Net detector is employed to assess the detectability of stego images generated using the ResNet50+CNN embedding framework. The ResNet50 network provides deep residual texture features that enhance the spatial consistency of the embedding, while the CNN component performs adaptive encoding to preserve visual fidelity. The primary objective of this analysis is to determine whether Ye-Net can successfully distinguish between cover and stego images at various payload capacities ($\mathrm{BPP} = 0.1, 0.2, 0.4$). By examining confusion matrices and performance indicators such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy, this evaluation investigates the detectability, discriminative capability, and resilience of the ResNet50+CNN–based steganographic model when confronted with a deep convolutional detector.

Figure~\ref{fig:xu_hed_cnn_conf_all112} presents the confusion matrices obtained by Ye-Net for the \textbf{ResNet50+CNN} configuration at payload rates of 0.1, 0.2, and 0.4~BPP. Across all payload levels, the classifier exhibits a degenerate decision behavior by labeling all samples as \emph{cover} images. Consequently, each confusion matrix reports $\mathrm{TN} = 20$, $\mathrm{FP} = 0$, $\mathrm{FN} = 20$, and $\mathrm{TP} = 0$. This results in an overall accuracy of 50%, a true positive rate of 0.0, a true negative rate of 1.0, and a balanced accuracy of 0.50.

These results indicate chance-level discrimination with no effective operating threshold for separating cover and stego images. Furthermore, detection performance remains unchanged as the payload size increases, suggesting that higher BPP values do not enhance detectability in this particular configuration. Overall, the findings demonstrate that the proposed ResNet50+CNN embedding approach exhibits resistance to detection by Ye-Net on the evaluated dataset.
%but this result does not provide universal security protection because it needs additional testing with different detectors and independent datasets.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (ResNet50+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all112}
\end{figure}

The ROC curves for Ye--Net (ResNet50+CNN) appear in Figure~\ref{fig:xu_hed_cnn_conf_all1012} when BPP equals 0.1, 0.2 and 0.4.
The ROC curve shows no discrimination ability because it runs along the diagonal line with AUC equal to 0.5000 and an equal error rate of 50\%.The detector fails to find any operating point which would achieve both high true positive rates and low false positive rates.The results show that BPP variations do not enhance detection performance under this specific configuration.The embedding shows resistance to this particular detector when applied to this dataset.
%yet this result does not provide universal security protection because it needs additional tests with different detectors and separate datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (ReNet50+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1012}
\end{figure}


\subsubsection{Security Analysis Using Ye-Net,ResNet50 and GAN }

In this subsection, the Ye-Net detector is used to analyze the detectability of stego images generated using the ResNet50+GAN embedding framework. The ResNet50 module provides deep residual texture representations that guide the embedding process, while the GAN component introduces adversarial learning to generate visually realistic and statistically consistent stego images. The objective of this evaluation is to determine whether Ye-Net can effectively discriminate between cover and stego images at various embedding capacities ($\mathrm{BPP} = 0.1, 0.2, 0.4$). By examining the resulting confusion matrices and performance metrics—such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy—this analysis provides insight into the resilience, statistical indistinguishability, and detection limitations of the ResNet50+GAN–based steganographic model under deep learning–based steganalysis.Figure~\ref{fig:xu_hed_cnn_conf_all1112} presents the confusion matrices obtained by Ye-Net for the \textbf{ResNet50+GAN} configuration at payload rates of 0.1, 0.2, and 0.4~BPP. Across all tested payload levels, the detector exhibits a degenerate decision behavior by classifying all samples as \emph{cover} images. Consequently, each confusion matrix reports $\mathrm{TN} = 20$, $\mathrm{FP} = 0$, $\mathrm{FN} = 20$, and $\mathrm{TP} = 0$. This results in an overall accuracy of 50\%, a true positive rate of 0.0, a true negative rate of 1.0, and a balanced accuracy of 0.50.These results indicate chance-level discrimination with no effective operating threshold. Furthermore, detection performance remains unchanged as the payload size increases, demonstrating that higher BPP values do not enhance detectability under this configuration. Overall, the findings suggest that the proposed ResNet50+GAN embedding approach exhibits resistance to detection by Ye-Net on the evaluated dataset.

إذا رغبتِ، أستطيع:

توحيد هذه الفقرة مع نتائج Ye-Net (VGG16+CNN) وYe-Net (ResNet50+CNN) في خلاصة مقارنة واحدة

أو اختصارها إلى Security Insight موجز

أو ربطها مباشرةً بخلاصة Ye-Net Security Evaluation في نهاية الفصل %although this result does not ensure overall security because it needs verification with more robust detectors and separate testing datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetGAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetGAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (ResNet+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1112}
\end{figure}

The ROC curves for Ye-Net (ResNet50+GAN) appear in Figure~\ref{fig:xu_hed_cnn_conf_all1014} at different BPP values of 0.1, 0.2 and 0.4.
The ROC curves maintain a position near the diagonal line for all payload values while achieving AUC scores of 0.51 at 0.1 and 0.505 at 0.2 and 0.500 at 0.4.
The detection results show a 50\% equal-error rate because the operating point fails to achieve both high true-positive detection and low false-positive detection rates.The detector shows no significant improvement in detectability when BPP increases under this configuration because the results remain almost identical.The embedding shows resistance to this particular detector when applied to this dataset. 
%but additional security assessments must be performed to confirm universal protection.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetGAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetGAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (ResNet50+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1014}
\end{figure}

% Table 1: ResNet50 results
The evaluation results for ResNet50-based detectors are presented in Table~\ref{tab:roc_resnet5050}, which reports ROC performance metrics for three payload levels ($\mathrm{BPP} = 0.1, 0.2, 0.4$) using two different classifier heads (CNN and GAN). Across all configurations, the classification accuracy remains constant at 50\%, while the area under the ROC curve (AUC) stays at random-guess levels, with values of 0.5000 for the CNN head and ranging from 0.5050 to 0.5100 for the GAN head.The corresponding ROC curves closely follow the diagonal reference line, reflecting the absence of improvement over random guessing. No reliable operating point is observed that simultaneously achieves a high true positive rate and a low false positive rate, as the equal-error rate remains close to 50\%. Although the GAN-based variant exhibits slightly higher AUC values than the CNN-based variant, the observed differences (less than 0.01) are statistically insignificant given the standard test set sizes used in this study.Overall, detection performance remains unaffected by the amount of embedded data, as the detectors fail to identify steganographic content across all evaluated payload levels from 0.1 to 0.4~BPP. These results indicate that the ResNet50-based detectors are unable to extract discriminative steganographic signals from the evaluated dataset. However, further validation using different network architectures and independent datasets is required before drawing conclusions regarding overall security.
\begin{table}[htbp]
\centering
\caption{ROC performance per dataset (ResNet50 variants)}
\label{tab:roc_resnet5050}
\begin{tabular}{lcc}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
\hline
BPP\_0.1\_ResNet50\_CNN_GA & 50.00\% & 0.5000 \\
BPP\_0.2\_ResNet50\_CNN_GA & 50.00\% & 0.5000 \\
BPP\_0.4\_ResNet50\_CNN_GA & 50.00\% & 0.5000 \\
BPP\_0.1\_ResNet50\_GAN_GA & 50.00\% & 0.5100 \\
BPP\_0.2\_ResNet50\_GAN_GA & 50.00\% & 0.5050 \\
BPP\_0.4\_ResNet50\_GAN_GA & 50.00\% & 0.5050 \\
\hline
\end{tabular}
\end{table}

% Table 2: VGG16 results
The results reported in Table~\ref{tab:roc_vgg1616} present the ROC performance of VGG16-based detectors evaluated at three payload levels ($\mathrm{BPP} = 0.1, 0.2, 0.4$) using two different classifier heads (CNN and GAN). Across all configurations, the classification accuracy remains constant at 50\%, while the AUC values remain at or near chance level.For the CNN head, the AUC values are 0.5000, 0.5088, and 0.4837 at $\mathrm{BPP} = 0.1$, $0.2$, and $0.4$, respectively. The AUC value of 0.4837 falls below 0.5, indicating a reversed ranking; in this case, inverting the decision scores would yield an AUC greater than 0.5. The GAN head produces AUC values of 0.5000, 0.5088, and 0.5088, reflecting non-discriminative behavior that is only marginally above chance level.The ROC curves closely follow the diagonal reference line, corresponding to an equal-error rate of approximately 50\%, and no operating point is observed that simultaneously achieves a high true positive rate and a low false positive rate. The small AUC deviations from 0.5 (less than 0.0088) lack practical significance and would not be expected to reach statistical significance under typical experimental conditions. Furthermore, detection performance shows no meaningful change as the payload increases from 0.1 to 0.4~BPP under this configuration.Overall, these results indicate that the proposed embedding method exhibits resistance to detection by these VGG16-based Xu-Net variants on the evaluated dataset.
%but additional security assessments with different detectors and datasets and AUC confidence intervals are needed to establish general security claims.
\begin{table}[htbp]
\centering
\caption{ROC performance per dataset (VGG16 variants)}
\label{tab:roc_vgg1616}
\begin{tabular}{lcc}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
\hline
BPP\_0.1\_VGG16\_CNN_GA & 50.00\% & 0.5000 \\
BPP\_0.2\_VGG16\_CNN_GA & 50.00\% & 0.5088 \\
BPP\_0.4\_VGG16\_CNN_GA & 50.00\% & 0.4837 \\
BPP\_0.1\_VGG16\_GAN_GA & 50.00\% & 0.5000 \\
BPP\_0.2\_VGG16\_GAN_GA & 50.00\% & 0.5088 \\
BPP\_0.4\_VGG16\_GAN_GA & 50.00\% & 0.5088 \\
\hline
\end{tabular}
\end{table}

\subsection{Robustness Analysis Using Texture Maps}

Evaluating the robustness of a steganographic system is a fundamental step in determining its ability to preserve hidden information when subjected to various image distortions and attacks. In this subsection, the robustness of the texture map–based embedding framework—specifically the ResNet50–CNN hybrid configuration—is examined under common degradation scenarios such as JPEG compression, Gaussian noise, and salt-and-pepper noise. The objective of this analysis is to assess whether the system can maintain stable extraction performance and accurate recovery of the hidden secret in the presence of external perturbations. By analyzing quantitative indicators such as the bit error rate (BER) and relative reconstruction error (Re) across different attack intensities, this subsection investigates the level of attack resistance, synchronization stability, and overall resilience of the texture-guided embedding and extraction modules within the proposed steganographic model.

The Mini embedding configuration of the ResNet50–CNN hybrid steganographic system undergoes robustness testing under various common image distortions, as reported in Table~\ref{tab:mini-ber-re-5}. The results show no significant variation in the bit error rate (\textit{BER_secret}) or relative reconstruction error (\textit{Re_secret}) across all tested attack conditions. In the absence of any attack, the system yields $\mathrm{BER}=0.4966$ and $\mathrm{Re}=0.7364$, values that correspond to random recovery behavior (i.e., $\mathrm{BER} \approx 0.5$). Identical performance is observed when the system is subjected to JPEG compression at different quality levels ($q=90$–$30$), Gaussian noise with varying standard deviations ($\sigma=0.005$–$0.02$), and salt-and-pepper noise with different noise densities ($0.005$–$0.02$).

The invariance of BER and Re with respect to attack type and severity indicates that the extraction stage consistently produces random outputs, regardless of the applied distortion. This behavior suggests that the extraction process fails to recover meaningful secret information due to a lack of proper synchronization between the embedding and extraction stages. Consequently, the observed stability does not reflect genuine robustness against attacks, but rather persistent decoding failure. Therefore, under the Mini embedding configuration, the ResNet50–CNN hybrid model does not demonstrate actual resistance to image distortions, as its recovery mechanism is unable to reliably extract the embedded payload.
%The model needs to demonstrate perfect baseline recovery with \(\mathrm{BER}\ll0.5\) and low \(\mathrm{Re}\) values before performing tests for compression and noise resistance.
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using ResNet50 and CNN}
  \label{tab:mini-ber-re-5}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.496582 & 0.736363 \\
    Mini & JPEG       & q=90                   & 0.500671 & 0.750305 \\
    Mini & JPEG       & q=70                   & 0.502441 & 0.752016 \\
    Mini & JPEG       & q=50                   & 0.497467 & 0.746293 \\
    Mini & JPEG       & q=30                   & 0.498138 & 0.743772 \\
    Mini & Gaussian   & sigma=0.005            & 0.503082 & 0.763648 \\
    Mini & Gaussian   & sigma=0.01             & 0.501892 & 0.743975 \\
    Mini & Gaussian   & sigma=0.02             & 0.500336 & 0.752425 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.496765 & 0.736136 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.501709 & 0.731580 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.498474 & 0.745356 \\
    \bottomrule
  \end{tabular}
\end{table}

The robustness assessment of the proposed ResNet50–GAN hybrid model is presented in Table~\ref{tab:mini-ber-re-6}, which reports its performance under various distortion scenarios in the Mini embedding mode. The extraction performance shows no significant variation in the bit error rate (\textit{BER_secret}) or the relative reconstruction error (\textit{Re_secret}) as the attack intensity changes. In the baseline case without any attack, the system yields $\mathrm{BER}=0.4962$ and $\mathrm{Re}=0.7453$, and these values remain within a small deviation range (approximately $\pm 0.01$) when the stego images are subjected to JPEG compression ($q=90$–$30$), Gaussian noise with $\sigma=0.005$–$0.02$, and salt-and-pepper noise with density $0.005$–$0.02$.
The persistence of $\mathrm{BER}$ values close to 0.5 indicates that the recovered secret data exhibit random statistical characteristics rather than accurate reconstruction, implying a lack of effective robustness. The consistent results across different attack types and intensities suggest that the system does not successfully decode the embedded information. Instead, the observed stability arises from a failure in synchronization and decoding between the embedding and extraction stages, rather than from genuine resistance to image distortions.
 
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using ResNet50 and GAN}
  \label{tab:mini-ber-re-6}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.496185 & 0.745267 \\
    Mini & JPEG       & q=90                   & 0.495850 & 0.740568 \\
    Mini & JPEG       & q=70                   & 0.503662 & 0.745260 \\
    Mini & JPEG       & q=50                   & 0.499847 & 0.745397 \\
    Mini & JPEG       & q=30                   & 0.499176 & 0.734256 \\
    Mini & Gaussian   & sigma=0.005            & 0.501648 & 0.749499 \\
    Mini & Gaussian   & sigma=0.01             & 0.506653 & 0.747139 \\
    Mini & Gaussian   & sigma=0.02             & 0.502319 & 0.741347 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.496033 & 0.744975 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.507751 & 0.746075 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.501801 & 0.748125 \\
    \bottomrule
  \end{tabular}
\end{table}


The Mini embedding mode of the hybrid VGG16–CNN steganographic model is evaluated for robustness under various image distortion scenarios, as reported in Table~\ref{tab:mini-ber-re-7}. The bit error rate (\textit{BER_secret}) and relative reconstruction error (\textit{Re_secret}) remain within narrow ranges across all tested conditions, with BER values between 0.49 and 0.50 and Re values between 0.74 and 0.75.In the baseline case without any attack, the system yields $\mathrm{BER}=0.4942$ and $\mathrm{Re}=0.7493$, and identical results are observed when the stego images are subjected to JPEG compression at quality factors $q=90$–$30$, Gaussian noise with standard deviations $\sigma=0.005$–$0.02$, and salt-and-pepper noise with densities of $0.005$–$0.02$. The extraction metrics exhibit no observable degradation across the tested attack conditions, as their values remain effectively unchanged.However, BER values close to 0.5 indicate random data recovery rather than successful extraction of the embedded secret, implying an absence of genuine robustness. The system operates in a desynchronized state between the embedding and extraction stages, resulting in uniform, non-informative reconstruction outcomes under all evaluated conditions. Consequently, the observed stability reflects persistent decoding failure rather than effective resistance to image distortions.
 

\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using VGG16 and CNN}
  \label{tab:mini-ber-re-7}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.494232 & 0.749280 \\
    Mini & JPEG       & q=90                   & 0.501190 & 0.745536 \\
    Mini & JPEG       & q=70                   & 0.501617 & 0.749880 \\
    Mini & JPEG       & q=50                   & 0.500458 & 0.743316 \\
    Mini & JPEG       & q=30                   & 0.502716 & 0.737526 \\
    Mini & Gaussian   & sigma=0.005            & 0.500763 & 0.744185 \\
    Mini & Gaussian   & sigma=0.01             & 0.496490 & 0.737585 \\
    Mini & Gaussian   & sigma=0.02             & 0.494690 & 0.746854 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.494659 & 0.749983 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.494080 & 0.743057 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.493988 & 0.752851 \\
    \bottomrule
  \end{tabular}
\end{table}


The Mini embedding configuration of the proposed VGG16–GAN hybrid steganographic framework is evaluated for robustness under multiple image distortion scenarios, as shown in Table~\ref{tab:mini-ber-re-8}. The bit error rate (\textit{BER_secret}) and relative reconstruction error (\textit{Re_secret}) exhibit minimal variation across all tested conditions, with $\mathrm{BER}$ remaining between 0.49 and 0.51 and $\mathrm{Re}$ remaining between 0.73 and 0.75.In the baseline case without any attack, the system yields $\mathrm{BER}=0.4942$ and $\mathrm{Re}=0.7493$. These values remain effectively unchanged when the stego images are subjected to JPEG compression at quality factors $q=90$–$30$, Gaussian noise with standard deviations $\sigma=0.005$–$0.02$, and salt-and-pepper noise with noise densities of $0.005$–$0.02$. The extraction performance shows no observable change as attack intensity varies.However, BER values close to 0.5 indicate random recovery of the secret information rather than accurate reconstruction, suggesting a loss of synchronization between the embedding and extraction stages. As a result, the system produces identical metric values across all attack scenarios because it fails to correctly extract meaningful information from the distorted inputs. This apparent stability therefore reflects persistent decoding failure rather than genuine robustness to image distortions.
 %The system needs to achieve successful baseline recovery with \(\mathrm{BER}\ll0.5\) and low \(\mathrm{Re}\) values before performing attack-based evaluations to establish VGG16 encoder and GAN decoder alignment.
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using VGG16 and GAN}
  \label{tab:mini-ber-re-8}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.494232 & 0.749280 \\
    Mini & JPEG       & q=90                   & 0.511190 & 0.745536 \\
    Mini & JPEG       & q=70                   & 0.511617 & 0.749880 \\
    Mini & JPEG       & q=50                   & 0.510458 & 0.743316 \\
    Mini & JPEG       & q=30                   & 0.512716 & 0.737526 \\
    Mini & Gaussian   & sigma=0.005            & 0.510763 & 0.744185 \\
    Mini & Gaussian   & sigma=0.01             & 0.496490 & 0.737585 \\
    Mini & Gaussian   & sigma=0.02             & 0.494690 & 0.746854 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.504659 & 0.749983 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.504080 & 0.743057 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.493988 & 0.752851 \\
    \bottomrule
  \end{tabular}
\end{table}


\newpage
\setstretch{2}

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%% Please enter the Chapter Title in the following in place of xxxxx. Use a label in place of yyyyy to be
%% referred later.
%% Please write the text in between the braces in place of AAAAA

\chapter{Results and Analysis}
\label{yyyyy}

\section{Evaluation Metrics}
\label{Ch7: Background}
Steganography’s effectiveness and reliability are often evaluated based on imperceptibility, security, capacity, and robustness. By evaluating our proposed approach based on these factors, we can better understand their efficiency, reliability, and suitability.
\begin{itemize}
    \item \textbf{Imperceptibility}: It indicates how well a stenographic technique can conceal data within a cover medium without altering its quality.
    The aim is to embed data in a manner that remains undetectable by senses or detection algorithms, making it challenging for potential attackers to detect the concealed information. It also plays a role in maintaining the nature of communication and ensuring that the stenographic method stays discreet and unnoticeable.
    \item  \textbf{Security}: It revolves around safeguarding the presence of concealed data within the stego medium from disclosure or detection. The primary goal is to protect the confidentiality of information and prevent extraction by external parties or adversaries. It also is vital for protecting the privacy and confidentiality of concealed data, thwarting access or manipulation attempts.
    \item  \textbf{Capacity}: It denotes the volume of data (bits) that can be inserted into a cover medium while preserving imperceptibility. 
    The main aim is to maximize the data concealed within the cover medium while maintaining its quality and avoiding suspicion.
    
    \item  \textbf{Robustness}: It refers to the system’s ability to accurately extract data when intentional or unintentional modifications are made to the stego medium. The goal is to ensure that the concealed information can be reliably retrieved despite attacks or changes to the stego medium. It is also essential for ensuring the reliability and resilience of techniques enabling data extraction under varying conditions and safeguarding against attacks that could alter the stego medium.
   
\end{itemize}


\subsection{ Imperceptibility Metrics }

Imperceptibility is typically determined by comparing the values of pixels of the cover image with those of the stego image. There are techniques, commonly employed for assessing imperceptibility, that are described as follows.

\subsubsection{Mean Square Error (MSE) }
\label{}
\textbf{MSE} is as a metric indicates the average squared difference between the pixel values in the  original cover image  and the stego image. It measures how much the stego image deviates from the original image. A lower MSE implies that the modifications made by the embedding steganography method are less noticeable and don't significantly affect the appearance of the cover image, because the differences in pixel values are reduced.  In the context of evaluating imperceptibility in a system, it's better to have lower MSE. The value of MSE is calculated by using Equation~5.1 for colour image and  Equation~5.2 for the grayscale image. The range of MSE value is between 0 and 1, and the lower values (that are close to zero) are better than the higher values (that are closer to one)~\cite{wani2023,duan2020}.The x,y,z represents the  channel pixels values for  colour image,and i,j represent the index values of the grayscale image.

\noindent MSE for color image with $L$ layers:
\begin{equation}
\text{MSE} = \frac{1}{WH} \sum_{x=1}^{W} \sum_{y=1}^{H} \sum_{z=1}^{L} (I_{xyz} - I'_{xyz})^2
\end{equation}
%\hl{Note: what is x,y,z here}

where:
\begin{align*}
    & \text{MSE} : \text{Mean Squared Error} \\
    & W, H,L : \text{Image dimensions} \\
    & I(x,y,z) : \text{Pixel value of the original image} \\
    & I'(x,y,z) : \text{Pixel value of the stego image}
\end{align*}

\noindent MSE for grayscale image with $L$ layers:
\begin{equation}
    \text{MSE} = \frac{1}{mn} \sum_{i=1}^{m} \sum_{j=1}^{n} (I(i,j) - K(i,j))^2
\end{equation}


where:
\begin{align*}
    & \text{MSE} : \text{Mean Squared Error} \\
    & m, n : \text{Image dimensions} \\
    & I(i,j) : \text{Pixel value of the original image} \\
    & K(i,j) : \text{Pixel value of the stego image}
\end{align*}

\subsubsection{Peak Signal to Noise Ratio (PSNR)}
\label{}

\textbf{\large{ RMSE }}  is a form of MSE in which the square root of the MSE is calculated to determine the average magnitude of the errors between the original and stego images.Similarly, a lower RMSE signifies a reduced average error magnitude of errors between the cover image and the stego image~\cite{plachta2022,Himthani2022}. Lower RMSE values suggest that the stenographic modification is closer to the original pixel value and thus less noticeable to the human eye. The RMSE can be computed from the following  Equation~5.3 :\newline 
\begin{equation}
    \text{RMSE} = \sqrt{\text{MSE}}
\end{equation}
The range of RMSE value is between 0 and 1, and the lower values (that are close to zero) are better than the higher values (that are closer to one).




\textbf{PSNR} assesses how well a stego image matches the cover image and is expressed in decibels (dB). It measures the noise level in stego image. As the embedding payload increases, the PSNR decreases due to introduces distortions.  PSNR values above 40 dB indicate high image quality while anything below 30 dB indicate lower quality~\cite{plachta2022,Himthani2022}. 
The calculation of PSNR can be done using either the logarithmeic  Mean Square Error (MSE) as shown in  Equation~4.5
\begin{equation}
\text{PSNR} = 10 \log_{10}\left(\frac{I_{\text{max}}^2}{\text{MSE}}\right) \quad \text{}
\end{equation}

%\begin{equation}
%\text{PSNR} = 20 \log_{10}\left(\frac{I_{\text{max}}}{\text{RMSE}}\right) \quad \text{}
%\end{equation}
%\hl{Note: What is RMSE in the equation?}
Here, 
$I_{\text{max}}$ is the maximum pixel value of the image. For grayscale images, $I_{\text{max}}$ is typically 255. In certain cases where a different maximum value $C_{\text{max}}$ is used, if it does not reach 255, the accuracy of the PSNR measurement may be compromised.

\subsubsection{Structural Similarity Index (SSIM)}
\label{}

\textbf{SSIM} is a better method for assessing image fidelity than traditional approaches (such as MSE and PSNR), as they don't provide a comprehensive assessment. It evaluates the similarity between two images in terms of brightness, contrast and structure by considering how our eyes perceive them. SSIM is good at detecting changes that unnoticed by PSNR. When the SSIM value is near to 1, it shows that the images are very similar; whereas a value near to -1 indicates they are quite different. Studies have indicated that SSIM and PSNR measurements may not always be suitable for assessing image fidelity. A high PSNR value doesn't necessarily guarantee a high SSIM value~\cite{chinniyan2022,chinniyan2022}. If the PSNR value is high but the corresponding SSIM value is not satisfactory it could indicate changes in the shape of the histogram. The SSIM can be computed from the following  Equation~5.5.

\begin{equation}
    \text{SSIM}(I, K) = \frac{{(2\mu_I\mu_K + C_1)(2\sigma_{IK} + C_2)}}{{(\mu_I^2 + \mu_K^2 + C_1)(\sigma_I^2 + \sigma_K^2 + C_2)}}
\end{equation}

where:
\begin{align*}
    &  I, K : \text{Two different images}  \\
    & \text{SSIM}(I, K) : \text{Structural Similarity Index Measure} \\
    & \mu_I, \mu_K : \text{Mean intensity of images $I$ and $K$} \\
    & \sigma_I, \sigma_K : \text{Standard deviation of intensities of images $I$ and $K$} \\
    & \sigma_{IK} : \text{Covariance of intensities of images $I$ and $K$} \\
    & C_1, C_2 : \text{Constants to stabilize the division}
\end{align*}

\subsubsection{Image Fidelity (IF)}
 \textbf{IF}: It is another metric to identify the quantity of errors in pixel values. %which makes it quite  similar to MSE and both have their scale between 0 and 1.
 If the value is closer to 1, it means that the two images are more similar ~\cite{chinniyan_image_2022,zou2019}.The method of calculation is shown in Equation~5.6 as follows.
\begin{equation}
    \text{IF}(I, K) = \frac{1}{N} \sum_{i=1}^{N} \left(1 - \frac{|I_i - K_i|}{\max(I_i, K_i)}\right)
\end{equation}

where:
\begin{align*}
    & \text{IF}(I, K) : \text{Information Fidelity between images $I$ and $K$} \\
    & N : \text{Total number of pixels in the images} \\
    & I_i, K_i : \text{Pixel values at position $i$ in images $I$ and $K$}
\end{align*}




\subsection{ Capacity Metrics }
 \subsubsection{Payload Capacity (PC)}
\label{}

\textbf{PC} : It is a measure of how much data can be hidden in the cover image by the proposed steganography, and it will be quite dependent on the size of cover image 
%and the method  used 
~\cite{chakraborty2020,SETIADI2023}. Equation~5.7 shows the calculation of this measure.

\begin{equation}
    \text{Payload Capacity} = {{\text{T} \times N \times 8}}
\end{equation}

where:
\begin{align*}
    & \text{T} : \text{Total pixels of cover image} \\
    & N : \text{bytes per pixels : 1 for grayscale  or 3 for colour image  } \\
\end{align*}

%\newline


\subsubsection{Bit per Pixel (BPP)}
\label{}

\textbf{BPP} : It is the average value of the embedding per pixel of the whole image~\cite{KUMAR2023,sabah_abdulazeez2023}. Another term used interchangeably with BPP is bits per byte (BPB). To calculate BPP one can divide the payload capacity (PC) by the number of pixels in the image as illustrated in the following equation Equation~5.8.
\begin{equation}
    \text{BPP} = \frac{{\text{Payload Capacity (bits)}}}{N}
\end{equation}

where:
\begin{align*}
    & \text{BPP} : \text{Bits Per Pixel} \\
    & \text{Payload Capacity (bits)} : \text{Total payload capacity in bits} \\
    & N : \text{Total number of pixels in the image}
\end{align*}



\subsection{ Security Metrics  }
\subsubsection{Xu-Net Model}
The convolutional neural network architecture Xu-Net serves as a powerful baseline for image steganalysis because it was created for this purpose. The network design focuses on reducing prominent image elements while it enhances the detection of hidden embedding signals. The first stage of preprocessing uses fixed residual filters or learnable shallow filters to reduce low-frequency image details which reveals the noise patterns from hiding methods. The following convolutional stages use sequential feature extraction with normalization and nonlinear activation functions to detect statistical differences between cover images and their steganographic versions. The practical XuNet variants use specific operations for steganalysis which include restricted convolutions with small receptive fields and absolute-value or truncation layers for sign-invariant residuals and batch normalization for training stability and pooling/striding for local residual statistic aggregation before a compact classifier produces the final decision. XuNet-style models achieve superior performance in end-to-end training with paired cover–stego datasets and demonstrate both their ability to learn steganalytic features and their effectiveness as practical detectors in comparative evaluations~\cite{sedighi2016,Kumar2024}.

\subsubsection{Ye-Net Model}
The convolutional neural network architecture Ye-Net focuses on detecting embedded residual patterns through its design for image steganalysis operations. The model starts with high-pass preprocessing through fixed or learnable residual filters to reveal noise-like artifacts before using multiple convolutional blocks with small receptive fields and activation functions and normalization layers to extract local residual patterns. The network uses absolute-value operations and truncation techniques to achieve sign-invariant results while controlling the impact of extreme values. The network uses pooling operations and strided convolutions to combine spatial information from different parts of the image. The network concludes with small fully connected layers which combine learned features to perform binary classification between cover images and stego images. YeNet-style architectures achieve successful steganalytic representation discovery through end-to-end training with matched cover–stego pairs and serve as a standard reference point in comparative evaluation studies~\cite{Lin2024,Patwari2025}.

\subsection{ Robustness Metrics }
To measure the robustness of our system againt attacks, we pass each attacked stego image to  the revealing network to extract the hidden information after attacking it.
The  revealing network’s responsibility is to decode the hidden information from the attacked stego image, and then we make an evaluation to determine the revealing network's capability of extracting the  hidden information from the attacked stego images. The revealing network can be evaluated to determine the strength of the  steganographic system against attacks. 
 
\subsubsection{Error Rate Extraction (Re)}
\textbf{Re} is a method used for assessing message extraction in reliable steganography. This method determines the bit errors in the extracted message.
In fact, as the error value is close to 0, the error is considered to be  decreasing; if it is 0, then the extraction is perfect~\cite{Sharma2024,Verma2024}. The calculation of this measure is done through Equation~5.9. 

\begin{equation}
Re = \frac{Ne}{L message}
\end{equation}
In the above equation, Re is the number of error message bits, and Lmessage is the total message bits. Several studies~\cite{Sharma2024,Verma2024} also used other metrics, such as PSNR, MSE, SSIM, etc., to measure robustness

\subsubsection{BER}
BER measures the ratio of incorrectly received bits to the total number of bits transmitted or extracted during communication. It calculates the average difference rate between transmitted bits and received bits to show the reliability and accuracy of communication systems and steganographic processes~\cite{Panigrahi2025,Ray2021}. It uses the following formula Equation~5.10:

\begin{equation}
\mathrm{BER} = \frac{1}{N}\sum_{i=1}^{N} \mathbf{1}\{b_i \neq \hat{b}_i\}
\label{eq:BER}
\end{equation}

The indicator function \( \mathbf{1}\{\cdot\} \) appears in the equation to determine when the transmitted bit \( b_i \) differs from the received bit \( \hat{b}_i \) while N represents the total number of bits.
\subsection{Robustness Attacks}
Media platforms face geometric attacks so image steganography models require a high extraction rate to counter these attacks effectively. The robustness of a steganography model is assessed by its capability to retrieve data from compromised or attacked stego images with low damage. The types of attacks employed in the experiments are detailed in this section. There are a  set of attacks that are experimented in our works such as : JPEG compression, that used to reduce the size of an image file, with quality factors $Q$ from 10 to 90; Gaussian noise that add random noise chosen from a Gaussian distribution, with variance $\sigma$  of 0.001, and 0.005; Salt and pepper noise that randomly selects some pixels and changes their value to 0 or 255, with variance $\sigma$ of 0.001.


\subsubsection{JPEG compression attack}
It is employed to decrease the image size and then encoding it using quality factors from 10 to 90. When an image is compressed using JPEG compression techniques the image quality decreases as data is lost during the compression process. This leads to  loss of fine details. So, to assess the strength or robustness of an image hiding system against JPEG compression attacks, the quality factors are used and can influence how well the hidden information is seen. In the context of JPEG compression settings, the quality factors usually varies between 10 and 100. A higher quality factor means lower compression and better image quality while a lower quality factor results in higher compression with lower image quality. The quality factor plays a role in balancing image size reduction and image fidelity~\cite{Setiadi2024,Liu2024}.
 
\subsubsection{Gaussian noise attack}
It is considered by adding noise selected from Gaussian distribution with variance  ranging between 0.001 and 0.005. It is often applied to mimic random variations in image data, and it is characterized by a normal distribution. When Gaussian noise is incorporated into an image it might create a graininess or speckled appearance which could impact the images quality and fidelity. In the context of Gaussian noise, the variance factor signifies the variance of the distribution utilized for generating noise. A higher variance leads to noisier images with more varied pixel intensities, whereas a lower variance leads to smoother noise patterns with less variation~\cite{Wang2025,Abdulhammed2024}.

\subsubsection{Salt and pepper noise attack}
It involves choosing randomly some pixels and adjusting their values to either 0, that mean the lowest intensity level (pepper), or 255, that means high intensity level (salt) with a variance represented by $\sigma$  of 0.001. When salt density is increased bright spots appear in the image while raising pepper density brings forth dark spots. By adjusting these densities effectively, we can control how much of the noise affects the image visually and where it appears on the image. When it comes to a salt and pepper noise attack situation the variance factor doesn't have an impact because salt and pepper noise doesn't follow Gaussian distribution. Salt and pepper noise is actually defined by the levels of salt density and pepper density which dictate how many pixels in the image are influenced by this type of noise~\cite{Jasim2024,Tahbaz2024}.

\section{Edges Detectors Results Analysis}

In this section, we analyze and compare the results of different edge detection algorithms used in our steganographic framework. The main goal is to evaluate how each edge detector—particularly HED and RCF—performs in identifying accurate object boundaries while preserving the structure and texture of the original image during both embedding and extraction. To ensure a fair comparison, we designed an experiment in which the entire steganography system remains constant while only the edge detection model changes. The same datasets (BOSSbase and USC-SIPI) and embedding settings (fixed thresholding and GA-optimized modes) are used for all tests. For each edge detector, we embed and extract a payload at specific BPP target values and measure several performance indicators: PSNR, SSIM, MSE, and IF to evaluate image quality, as well as TimeE (embedding time) and TimeX (extraction time) to assess computational efficiency. When relevant, we also include BPP used  to show embedding capacity. This evaluation directly supports our main research objective—to determine which edge detection model provides the best balance between visual quality, robustness, and processing speed for integration into the proposed steganographic system. 
%The following subsections are organized as follows.

\noindent
The experimental design in the following tables is a full-setup that explores how two binary factors—attention maps (e.g., RCF, HED, VGG16, ResNet50) and GA (genetic algorithm optimization)—influence the performance of an image steganography system under three target bit-per-pixel levels, \(\mathrm{BPP}\in\{0.100,\,0.200,\,0.400\}\).
Each factor has two states: \texttt{\(\text{attention maps}=\textit{True}\)} means the system uses texture-guided or edge-guided embedding based on these attention maps, while \texttt{\(\text{attention maps}=\textit{False}\)} means fallback attention guidance (traditional methods) is applied.
Similarly, \texttt{\(\text{GA}=\textit{True}\)} indicates that a genetic optimization process is used to select or tune embedding locations, whereas \texttt{\(\text{GA}=\textit{False}\)} means embedding is performed without any optimization step.
This produces four main experimental cases per BPP level: (1) both attention map and GA enabled (\textit{True}, \textit{True}); (2) attention map enabled but GA disabled (\textit{True}, \textit{False}); (3) attention map disabled but GA enabled (\textit{False}, \textit{True}); and (4) both disabled (\textit{False}, \textit{False}).
For each case, the system records quantitative measures, including embedding and extraction quality (PSNR, SSIM, MSE, IF), actual bit rate used \((\mathrm{BPP}_{\mathrm{used}})\), and processing time.
Repeating these four cases across the three \(\mathrm{BPP}\) targets allows the study to isolate and compare the individual and interactive effects of edge/attention guidance, evolutionary optimization, and payload capacity on system behavior, without relying on specific numerical outcomes.
% \hl{Note: Tell about what you are doing in this section?}% \hl{Mention the experiment design.}
% \hl{Connect with your Objectives or Research Questions.}
% \hl{Tell about the overall organization of the following subsections}
In the next subsections, after measuring the imperceptibility and capacity, we analyze the security through Xu-Net and Ye-Net.

\subsection{Imperceptibility \& Capacity Results With RCF \& CNN}
% \hl{Note: Now Tell about what you are doing here}
% \hl{Note: Describe the overall design of Table 5.1 (the columns). What are the measured? What are varied? Clarify to make them understand easily?}
% \hl{Note: Mention the difference between the remaining tables and Table 5.1}
% In this section, we will measure the imperceptibility and capacity for RCF and CNN 
% \noindent
In this experiment, the goal is to analyze how using \textbf{RCF} (edge-based feature extraction) and \textbf{GA} (Genetic Algorithm optimization) affects the performance of an image steganography system. 
The overall design presented in Table~\ref{tab:rcf_secret64_bpp_grid20} follows a \textit{factorial experimental setup}, where two binary factors (RCF and GA) are varied under three target payload levels expressed as 
bit-per-pixel (BPP) = 0.100, 0.200, and 0.400.

\smallskip
\noindent
The table columns represent both the \textbf{experimental factors} and the \textbf{measured performance metrics}. 
The first three columns (\textit{RCF, GA, BPP\textsubscript{tg}}) show the independent variables—these are the parameters intentionally varied during the experiment. 
The following columns report the measured outcomes, including:

\begin{itemize}
    \item \textbf{BPP\textsubscript{used}} – the actual embedding rate achieved;
    \item \textbf{PSNR\textsubscript{c→s}}, \textbf{SSIM\textsubscript{c→s}}, \textbf{MSE\textsubscript{c→s}}, \textbf{IF\textsubscript{c→s}} – quality metrics between the cover and stego images, indicating the amount of visual distortion caused by embedding;
    \item \textbf{PSNR\textsubscript{s→x}} and \textbf{SSIM\textsubscript{s→x}} – quality metrics evaluating the accuracy of secret image recovery;
    \item \textbf{TimeE} and \textbf{TimeX} – the embedding and extraction times, respectively, measuring computational efficiency.
\end{itemize}

\noindent
By varying the two binary factors (\texttt{RCF = True/False} and \texttt{GA = True/False}) across the three BPP target levels, 
the experiment systematically investigates how the presence or absence of edge-based guidance and genetic optimization influences both embedding fidelity and secret recovery performance. 
In simple terms, the table summarizes how different combinations of feature guidance and optimization affect the quality, capacity, and efficiency of the steganographic process. The difference  between this table and the following table is in each table, we use different edge or texture detector with different attention map used. 

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % تقليل الفراغ الأفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Result Analysis Using RCF and CNN for secret image size $64\times64$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:rcf_secret64_bpp_grid20}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{RCF} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.096 & 0.058 \\ \hline
True  & False & 0.100 & 0.0992 & 57.43 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.101 & 0.036 \\ \hline
False & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.042 & 0.027 \\ \hline
False & False & 0.100 & 0.0992 & 57.49 & 0.9987 & 0.000002 & 0.9960 & $\infty$ & 1.0000 & 0.052 & 0.016 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.049 & 0.030 \\ \hline
True  & False & 0.200 & 0.1250 & 56.51 & 0.9983 & 0.000002 & 0.9955 & 156.28 & 1.0000 & 0.030 & 0.019 \\ \hline
False & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.132 & 0.075 \\ \hline
False & False & 0.200 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.078 & 0.047 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.076 & 0.030 \\ \hline
True  & False & 0.400 & 0.1250 & 56.51 & 0.9983 & 0.000002 & 0.9955 & 156.28 & 1.0000 & 0.029 & 0.021 \\ \hline
False & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28  & 1.0000 & 0.077 & 0.031 \\ \hline
False & False & 0.400 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.029 & 0.018 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup

\noindent Table~\ref{tab:rcf_secret64_bpp_grid20} shows that the genetic algorithm (GA) strand is the main factor maintaining cover fidelity according to the quantitative analysis, whereas RCF primarily enhances  capacity without affecting distortion when GA is enabled. 
At BPP target, $\mathrm{BPP}_{\mathrm{tg}}=0.1$ (with $\mathrm{BPP}_{\mathrm{used}}=0.0992$), enabling GA increases $\mathrm{PSNR}_{\text{cover}\to\text{stego}}$ from $57.43$--$57.49\,\mathrm{dB}$ (GA off, RCF on/off) to $61.17\,\mathrm{dB}$ (GA on, RCF on/off), while simultaneously raising $\mathrm{SSIM}$ to $0.9993$ and $\mathrm{IF}$ to $0.9974$, and reducing $\mathrm{MSE}$ to approximately $10^{-6}$. Identical performance improvements are observed at higher payload targets; $\mathrm{PSNR}$ reaches $60\,\mathrm{dB}$, yielding a $3.6$--$3.8\,\mathrm{dB}$ gain due to GA with gradual increases in $\mathrm{SSIM}$ and $\mathrm{IF}$. The system attains its maximum used BPP-used of $0.125$ once the target BPP-target reaches $0.2$ or higher because the secret size limits the achievable BPP-target. Secret recovery remains perfect under all testing conditions, with $\mathrm{SSIM}_{\text{secret}\to\text{extracted}}=1.0000$ and $\mathrm{PSNR}_{\text{secret}\to\text{extracted}}=\infty$ at $\mathrm{BPP}_{\mathrm{tg}}=0.1$ and $\approx 156.3\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}}\in\{0.2,0.4\}$. All operations complete within one second: embedding requires $0.03$--$0.13\,\mathrm{s}$ and extraction $0.02$--$0.08\,\mathrm{s}$. Overall, GA improves cover fidelity, whereas RCF provides extra capacity that remains unused in this secret-constrained environment.



% في الـ preamble يُفضّل:
% \usepackage{graphicx}
% \usepackage{array}

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and CNN for secret image size $128\times128$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:rcf_secret128_bpp_grid21}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{RCF} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.040 & 0.023 \\ \hline
True  & False & 0.100 & 0.0992 & 57.39 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.024 & 0.016 \\ \hline
False & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.040 & 0.024 \\ \hline
False & False & 0.100 & 0.0992 & 57.37 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.023 & 0.015 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.074 & 0.043 \\ \hline
True  & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.045 & 0.025 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.073 & 0.044 \\ \hline
False & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.044 & 0.027 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.406 & 0.217 \\ \hline
True  & False & 0.400 & 0.3966 & 51.29 & 0.9942 & 0.000007 & 0.9917 & $\infty$ & 1.0000 & 0.190 & 0.119 \\ \hline
False & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.148 & 0.091 \\ \hline
False & False & 0.400 & 0.3966 & 51.49 & 0.9944 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.084 & 0.052 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:rcf_secret128_bpp_grid21} shows that the genetic algorithm (GA) maintains the highest impact on cover fidelity according to the ablation results, while RCF produces minimal effects after GA activation. In this table, we apply the experiment results made on image size  128*128 secret image. The activation of GA at $\mathrm{BPP}_{\mathrm{tg}}=0.1$ produces a $61.23\,\mathrm{dB}$ PSNR value for the cover-to-stego transformation, which represents a {$3.8\,\mathrm{dB}$ improvement from the original $57.39$–$57.37\,\mathrm{dB}$ range}. The activation of GA produces a $0.0004$ increase in SSIM and a $0.0005$ increase in IF. The PSNR values for the cover-to-stego transformation reach $58.25\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}}=0.2$ and $55.15\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{tg}}=0.4$. The increase of $\mathrm{BPP}_{\mathrm{used}}$ from $0.0992$ to $0.1953$ and $0.3966$ results in a {$3\,\mathrm{dB}$ decrease in cover quality at each doubling step yet GA maintains a consistent $3.6$–$3.9\,\mathrm{dB}$ advantage over fixed allocation}. The secret recovery process produces exact byte-level results in all tested configurations because $\mathrm{SSIM}_{\text{secret}\to\text{extracted}}=1.0000$ and $\mathrm{PSNR}_{\text{secret}\to\text{extracted}}=\infty$. The processing times for embedding and extraction operations range from $0.02$ to $0.41\,\mathrm{s}$ and $0.02$ to $0.22\,\mathrm{s}$ respectively while the payload size determines the duration. The genetic allocator (GA) provides consistent and reliable improvements in cover quality across all payload sizes but the random capacity filler (RCF) adds mostly unused capacity space when GA controls the allocation process.


% يفضّل في الـ preamble:
% \usepackage{graphicx}
% \usepackage{array}

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and CNN for secret image size $512\times512$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:rcf_secret512_bpp_grid12}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{RCF} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.147 & 0.071 \\ \hline
True  & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.039 & 0.018 \\ \hline
False & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.039 & 0.023 \\ \hline
False & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.026 & 0.035 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.079 & 0.045 \\ \hline
True  & False & 0.200 & 0.1953 & 54.53 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.045 & 0.027 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.075 & 0.043 \\ \hline
False & False & 0.200 & 0.1953 & 54.56 & 0.9973 & 0.000003 & 0.9943 & $\infty$ & 1.0000 & 0.044 & 0.055 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.152 & 0.087 \\ \hline
True  & False & 0.400 & 0.3966 & 51.27 & 0.9942 & 0.000007 & 0.9917 & $\infty$ & 1.0000 & 0.079 & 0.052 \\ \hline
False & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.150 & 0.088 \\ \hline
False & False & 0.400 & 0.3966 & 51.47 & 0.9943 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.081 & 0.049 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup



Table~\ref{tab:rcf_secret512_bpp_grid12} shows that the genetic algorithm (GA) determines the cover fidelity of a $512\times512$ secret image while RCF produces no impact when GA operates. The activation of GA produces a $61.20\,\mathrm{dB}$ PSNR value for cover to stego image conversion at $\mathrm{BPP}_{\mathrm{tg}}=0.1$ with $\mathrm{BPP}_{\mathrm{used}}=0.0992$ while the PSNR value remains at $57.46\,\mathrm{dB}$ when RCF is disabled and GA is disabled. The activation of GA produces better results in terms of $\mathrm{SSIM}$ and $\mathrm{IF}$ and lower $\mathrm{MSE}$ values. The PSNR values show consistent differences between RCF on and off conditions when GA is enabled at all three tested payload levels.  The PSNR values increase by 3.6–3.9 dB when GA is applied to the three different BPP levels. The PSNR value of the cover image decreases by 3 dB when the used BPP doubles from 0.0992 to 0.1953 and then to 0.3966, but the SSIM value remains above 0.9974 at BPP$_{tg}$ = 0.1 and above 0.9970 at higher payloads. The system operates at sub-second speeds for embedding and extraction operations, which increase slightly based on payload size. The genetic allocator (GA) produces consistent and measurable improvements in cover image quality at all payload levels, but the RCF algorithm adds minimal capacity when GA operates.





\subsection{Imperceptibility \& Capacity Results With HED \& CNN}

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and CNN for secret image size $64\times64$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:hed_secret64_bpp_grid1}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.255 & 0.142 \\ \hline
True  & False & 0.100 & 0.0992 & 56.96 & 0.9985 & 0.000002 & 0.9957 & $\infty$ & 1.0000 & 0.133 & 0.097 \\ \hline
False & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.097 & 0.056 \\ \hline
False & False & 0.100 & 0.0992 & 57.49 & 0.9987 & 0.000002 & 0.9960 & $\infty$ & 1.0000 & 0.059 & 0.034 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.117 & 0.070 \\ \hline
True  & False & 0.200 & 0.1250 & 55.88 & 0.9981 & 0.000003 & 0.9951 & 156.28 & 1.0000 & 0.074 & 0.042 \\ \hline
False & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.048 & 0.028 \\ \hline
False & False & 0.200 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.028 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.116 & 0.070 \\ \hline
True  & False & 0.400 & 0.1250 & 55.88 & 0.9981 & 0.000003 & 0.9951 & 156.28 & 1.0000 & 0.070 & 0.042 \\ \hline
False & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.049 & 0.030 \\ \hline
False & False & 0.400 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.030 & 0.019 \\ \hline
\end{tabular}
}
\end{table}
\endgroup

In this section, we describe the same experiment design as with the previous section, but we measure the imperceptibility and capacity using the HED and CNN.
Table~\ref{tab:hed_secret64_bpp_grid1} shows that the HED-based attention system with genetic algorithm (GA) produces the highest cover fidelity for a 64x64 secret but the data shows that GA outperforms HED as the main contributor. The addition of GA at BPPtg=0.1 with BPPused =0.0992 used, tg: make all as proper subscripts results in a PSNRcover→stego value of 61.17 dB and SSIM and IF values of 0.9993 and 0.9974, respectively, while MSE decreases by half. The same 3.6-3.8 dB advantage continues to exist at BPPtg values of 0.2 and 0.4 (BPPused reaches 0.1250) because GA enhances PSNR from 55.88-56.37 dB to 60.15 dB while SSIM, IF values also improve. The BPPused reaches a saturation point of 0.125 when BPPtg reaches 0.2 or higher because a 64x64 grayscale secret contains 32,768 bits, which equals 0.125 bpp on a 512x512 cover image. The secret size becomes the limiting factor because the target bit rate increase from 0.2 to 0.4 does not result in any cover quality deterioration. The secret size acts as a bottleneck because HED provides additional capacity but this extra space remains unused due to the secret size constraint. The absence of GA leads to a 0.4-0.5 dB decrease in PSNR values. The secret reconstruction process maintains perfect lossless performance in every tested scenario because SSIMsecret→extracted equals 1.0000 and PSNRsecret→extracted reaches infinity at 0.1 and 156.28 dB at 0.2 and above. The addition of HED with GA results in longer processing times for embedding and extraction operations, which take 0.255 seconds and 0.142 seconds, respectively, compared to 0.097 seconds and 0.056 seconds without HED at BPPtg=0.1. The total processing time remains under one second. The research demonstrates that GA functions as the primary quality control mechanism, while HED provides additional capacity at a cost of longer processing times without producing significant changes in image quality during secret-limited operations.

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and CNN for secret image size $128\times128$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:hed_secret128_bpp_grid2}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.097 & 0.061 \\ \hline
True  & False & 0.100 & 0.0992 & 56.94 & 0.9985 & 0.000002 & 0.9957 & $\infty$ & 1.0000 & 0.055 & 0.036 \\ \hline
False & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.038 & 0.023 \\ \hline
False & False & 0.100 & 0.0992 & 57.37 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.024 & 0.015 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.079 & 0.046 \\ \hline
True  & False & 0.200 & 0.1953 & 53.81 & 0.9969 & 0.000004 & 0.9938 & $\infty$ & 1.0000 & 0.041 & 0.025 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.228 & 0.105 \\ \hline
False & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.096 & 0.064 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.176 & 0.085 \\ \hline
True  & False & 0.400 & 0.3966 & 49.73 & 0.9930 & 0.000011 & 0.9901 & $\infty$ & 1.0000 & 0.076 & 0.045 \\ \hline
False & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.158 & 0.094 \\ \hline
False & False & 0.400 & 0.3966 & 51.49 & 0.9944 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.081 & 0.050 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup




Table~\ref{tab:hed_secret128_bpp_grid2} shows that the HED-based attention achieves the highest cover fidelity for a 128x128 secret when it operates with the genetic algorithm (GA). The genetic algorithm (GA) stands as the primary factor which determines the outcome while HED mainly affects the available capacity headroom. The addition of GA to the system at $\mathrm{BPP}_{\mathrm{tg}}=0.1$ ($\mathrm{BPP}_{\mathrm{used}}=0.0992$) results in a $\mathrm{PSNR}_{\text{cover}\to\text{stego}}$ improvement from 56.94 dB to 61.23 dB while simultaneously raising $\mathrm{SSIM}$ to 0.9994 and increasing $\mathrm{IF}$ to 0.9974 and decreasing $\mathrm{MSE}$. The same level of consistent performance enhancement occurs at both $\mathrm{BPP}_{\mathrm{tg}}=0.2$ and $\mathrm{BPP}_{\mathrm{tg}}=0.4$ (with $\mathrm{BPP}_{\mathrm{used}}=0.1953$ and $0.3966$). The PSNR resulting in 58.25 dB at 0.2 and 55.15 dB at  value increases by 3.7 to 5.4 dB when GA is applied to the system. The SSIM values reach 0.9987 and 0.9974 during these tests. The PSNR values remain identical at 61.23 dB when HED operates without GA at 0.1 and at 58.25 dB and 55.15 dB when GA is disabled at 0.2 and 0.4, respectively. The nominal capacity increases when HED operates without GA but the actual capacity remains the same when GA is enabled. The secret reconstruction process maintains lossless quality because the pipeline adjusts the secret size to match the allocated bits which results in $\mathrm{SSIM}_{\text{secret}\to\text{extracted}}=1.0000$ and $\mathrm{PSNR}=\infty$. The system operates at sub-second speeds while processing times slightly increase when dealing with larger payloads (e.g., GA+HED: 0.097/0.061 s at 0.1 to 0.176/0.085 s at 0.4 for embedding/extraction). The perceptual quality of the system improves substantially and predictably through GA while HED mainly provides extra unused capacity.




\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and CNN for secret image size $512\times512$ at BPP Targets = 0.100, 0.200, 0.400 }
\label{tab:hed_secret512_bpp_grid3}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.044 & 0.025 \\ \hline
True  & False & 0.100 & 0.0992 & 56.93 & 0.9985 & 0.000002 & 0.9957 & $\infty$ & 1.0000 & 0.024 & 0.015 \\ \hline
False & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.039 & 0.023 \\ \hline
False & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.025 & 0.014 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.076 & 0.047 \\ \hline
True  & False & 0.200 & 0.1953 & 53.81 & 0.9969 & 0.000004 & 0.9938 & $\infty$ & 1.0000 & 0.086 & 0.062 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.071 & 0.042 \\ \hline
False & False & 0.200 & 0.1953 & 54.56 & 0.9973 & 0.000003 & 0.9943 & $\infty$ & 1.0000 & 0.045 & 0.025 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.142 & 0.084 \\ \hline
True  & False & 0.400 & 0.3966 & 49.73 & 0.9930 & 0.000011 & 0.9901 & $\infty$ & 1.0000 & 0.077 & 0.044 \\ \hline
False & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.322 & 0.082 \\ \hline
False & False & 0.400 & 0.3966 & 51.47 & 0.9943 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.077 & 0.046 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup



Table~\ref{tab:hed_secret512_bpp_grid3} shows that the evaluation of a 512x512 secret image with three payload targets at 0.10, 0.20 and 0.40 bits per pixel (BPP) using a 2x2 ablation of HED preselection and a genetic algorithm (GA) shows that GA is the main factor affecting fidelity. The best cover-to-stego quality occurs at 61.20/58.25/55.17 dB PSNR with SSIM values of 0.9993/0.9987/0.9974 and MSE values of $10^{-6}\ \text{to}\ 3\times10^{-6}$
when the genetic algorithm is active at 0.10/0.20/0.40 bpp. The best PSNR values at these payload rates decrease to 57.46/54.56/51.47 dB when the genetic algorithm is disabled (SSIM values reach 0.9987/0.9973/0.9943). The PSNR and SSIM values remain almost unchanged when HED is active or inactive during GA operation. The absence of GA results in a 0.5–1.7 dB increase in distortion. The secret recovery process maintains lossless performance in every tested scenario because PSNRsecret→extracted equals infinity and SSIM equals 1.0000. The actual data transmission rates match the specified targets with minimal underutilization (BPPused = 0.0992, 0.1953, 0.3966; -0.8\%, -2.35\%, -0.85\%). The extraction process runs faster than embedding and the runtime depends on the payload size but HED introduces minimal delay at low rates until it starts affecting GA performance at high rates (0.40 bpp: 0.142 s with HED vs. 0.322 s without HED under GA). The ablation results demonstrate that GA produces the main improvements in cover–stego fidelity while the combined configurations maintain flawless secret extraction at every payload level.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and GAN for secret image size $64\times64$ at BPP targets 0.100, 0.200, and 0.400 }
\label{tab:hed_secret64_bpp_targets5}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.031 & 0.018 \\ \hline
True  & False & 0.100 & 0.0992 & 56.46 & 0.9986 & 0.000002 & 0.9954 & $\infty$ & 1.0000 & 0.021 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.017 \\ \hline
False & False & 0.100 & 0.0992 & 57.49 & 0.9987 & 0.000002 & 0.9960 & $\infty$ & 1.0000 & 0.019 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.037 & 0.021 \\ \hline
True  & False & 0.200 & 0.1250 & 55.40 & 0.9981 & 0.000003 & 0.9949 & 156.28 & 1.0000 & 0.026 & 0.027 \\ \hline
False & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.039 & 0.023 \\ \hline
False & False & 0.200 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.021 & 0.012 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.037 & 0.021 \\ \hline
True  & False & 0.400 & 0.1250 & 55.40 & 0.9981 & 0.000003 & 0.9949 & 156.28 & 1.0000 & 0.025 & 0.015 \\ \hline
False & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.039 & 0.023 \\ \hline
False & False & 0.400 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.032 & 0.017 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup




\subsection{Imperceptibility \& Capacity Results With RCF \& GAN}
In this section, we perform the same experiment design as with the previous section, but we will measure the imperceptibility and capacity using the RCF and GAN.
Table~\ref{tab:hed_secret64_bpp_targets5} shows that the evaluation of a 64x64 secret key with three payload targets at 0.10, 0.20, and 0.40 bits per pixel demonstrates that the genetic algorithm (GA) produces the best cover-to-stego quality. The best cover-to-stego quality reaches 61.17 dB PSNR at 0.10 bpp and 60.15 dB at the effective 0.125 bpp when using the 0.20/0.40 targets while achieving SSIM values between 0.9992 and 0.9993 and MSE values of approximately $10^{-6}$. The absence of GA results in a 4-5 dB decrease in PSNR values (56.46-56.37 dB) while SSIM values decrease to 0.9981-0.9987. The RCF preselection method produces no difference in PSNR or SSIM values when the GA is active, but it results in a 0.9-1.1 in every scenario because PSNRsecret→extracted equals infinity at 0.10 bpp and reaches a dB increase in distortion when the GA is disabled. The secret reconstruction process maintains perfect lossless data extraction at 156 dB with SSIM=1.0000 at higher targets. The actual payload reaches its maximum at 0.125 BPP for both the 0.20 and 0.40 targets because of capacity and bit-allocation constraints, which prevent further distortion growth. The GA-enhanced configuration maintains stable and low extraction and embedding times between 0.02 and 0.04 seconds, which shows that the genetic algorithm does not introduce significant runtime delays.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and GAN for secret image size $128\times128$ at BPP targets 0.100, 0.200, and 0.400}
\label{tab:hed_secret128_bpp_targets6}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.017 \\ \hline
True  & False & 0.100 & 0.0992 & 56.41 & 0.9986 & 0.000002 & 0.9954 & $\infty$ & 1.0000 & 0.021 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.029 & 0.017 \\ \hline
False & False & 0.100 & 0.0992 & 57.37 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.027 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.059 & 0.033 \\ \hline
True  & False & 0.200 & 0.1953 & 53.03 & 0.9967 & 0.000005 & 0.9932 & $\infty$ & 1.0000 & 0.035 & 0.020 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.060 & 0.032 \\ \hline
False & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.050 & 0.037 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.127 & 0.063 \\ \hline
True  & False & 0.400 & 0.3966 & 48.28 & 0.9914 & 0.000015 & 0.9883 & $\infty$ & 1.0000 & 0.063 & 0.035 \\ \hline
False & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.121 & 0.076 \\ \hline
False & False & 0.400 & 0.3966 & 51.49 & 0.9944 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.061 & 0.036 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:hed_secret128_bpp_targets6} shows that the evaluation of a 128x128 secret embedded across three payload targets at 0.10, 0.20 and 0.40 bits per pixel demonstrates that the genetic algorithm (GA) controls the cover-to-stego distortion more than the 2x2 ablation of RCF preselection. The PSNR values remain high at 61.23 dB for 0.10 bpp and 58.25 dB for 0.1953 bpp and 55.15 dB for 0.3966 bpp when GA is enabled while SSIM values range from 0.9974 to 0.9994 and MSE values stay between $10^{-6}$ and $3\times10^{-6}$. The quality of the stego image decreases by 4-7 dB when GA is disabled at all three payload levels {(0.10 bpp: 56.41 dB, 0.1953 bpp: 53.03-54.50 dB, 0.3966 bpp: 48.28-51.49 dB) while SSIM values decrease slightly. The distortion metrics remain identical when RCF is active or inactive during GA operation. The distortion metrics show no difference between RCF activation and deactivation when GA is active but RCF produces worse results than without it when GA is disabled. The secret reconstruction process achieves perfect results in every scenario because PSNRsecret→extracted equals infinity and SSIM equals 1.0000. The actual bit rates achieved by the system match the specified targets at 0.0992, 0.1953 and 0.3966 bits per pixel. The GA-enhanced configuration maintains practical embedding and extraction times which increase gradually with the rate (0.03 s to 0.12 s for embedding and 0.01 s to 0.08 s for extraction).


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using RCF and GAN for secret image size $512\times512$ at BPP targets 0.100, 0.200, and 0.400}
\label{tab:hed_secret512_bpp_targets7}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.017 \\ \hline
True  & False & 0.100 & 0.0992 & 56.40 & 0.9986 & 0.000002 & 0.9954 & $\infty$ & 1.0000 & 0.021 & 0.012 \\ \hline
False & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.017 \\ \hline
False & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.017 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.060 & 0.032 \\ \hline
True  & False & 0.200 & 0.1953 & 53.06 & 0.9967 & 0.000005 & 0.9933 & $\infty$ & 1.0000 & 0.035 & 0.020 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.100 & 0.055 \\ \hline
False & False & 0.200 & 0.1953 & 54.56 & 0.9973 & 0.000003 & 0.9943 & $\infty$ & 1.0000 & 0.055 & 0.031 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.202 & 0.114 \\ \hline
True  & False & 0.400 & 0.3966 & 48.30 & 0.9914 & 0.000015 & 0.9884 & $\infty$ & 1.0000 & 0.124 & 0.069 \\ \hline
False & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.114 & 0.061 \\ \hline
False & False & 0.400 & 0.3966 & 51.47 & 0.9943 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.064 & 0.035 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:hed_secret512_bpp_targets7} shows that the study used a 512x512 secret image distributed across three payload targets at 0.10, 0.20 and 0.40 bits per pixel. The results show that the genetic algorithm (GA) is the most important factor in determining the cover to stego distortion. The PSNR value reaches 61.20 dB at 0.0992 BPP and decreases to 58.25 dB at 0.1953 BPP and 55.17 dB at 0.3966 BPP while SSIM stays between 0.9974 and 0.9993 and MSE remains between $10^{-6}$ and $3\times10^{-6}$. The quality of the stego image decreases by 1-3 dB at low bit rates and up to 3 dB at 0.40 bpp when GA is disabled (e.g., 56.40 dB to 51.47-48.30 dB). The SSIM value decreases and MSE value increases to $1.5\times 10^{-5}$
\noindent The RCF has no effect on the distortion when the GA is active, but it degrades the distortion when the GA is disabled, especially at high payloads (\(48.30\,\mathrm{dB}\) with RCF vs.\ \(51.47\,\mathrm{dB}\) without RCF at \(0.3966\,\mathrm{bpp}\)). The secret reconstruction process achieves perfect results in all scenarios because \( \mathrm{PSNR}_{\text{secret}\to\text{extracted}}=\infty \) and \( \mathrm{SSIM}=1.0000 \). The achieved bitrates match the specified targets at \(0.0992\), \(0.1953\), and \(0.3966\,\mathrm{bpp}\).The embedding and extraction processes maintain short execution times that increase proportionally with the embedding/extraction rate (\(0.02\text{--}0.20\,\mathrm{s}\) for embedding and \(0.01\text{--}0.11\,\mathrm{s}\) for extraction). The GA-enhanced configuration thus demonstrates practicality through its efficient processing times.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and GAN for secret image size $64\times64$ at BPP targets 0.100, 0.200, and 0.400}
\label{tab:hed_secret64_bpp_targets_grid8}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 60.54 & 0.9992 & 0.000001 & 0.9972 & $\infty$ & 1.0000 & 0.029 & 0.017 \\ \hline
True  & False & 0.100 & 0.0992 & 57.23 & 0.9987 & 0.000002 & 0.9958 & $\infty$ & 1.0000 & 0.021 & 0.012 \\ \hline
False & True  & 0.100 & 0.0992 & 61.17 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.041 & 0.020 \\ \hline
False & False & 0.100 & 0.0992 & 57.49 & 0.9987 & 0.000002 & 0.9960 & $\infty$ & 1.0000 & 0.017 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 59.75 & 0.9991 & 0.000001 & 0.9969 & 156.28 & 1.0000 & 0.036 & 0.020 \\ \hline
True  & False & 0.200 & 0.1250 & 56.12 & 0.9983 & 0.000002 & 0.9953 & 156.28 & 1.0000 & 0.033 & 0.036 \\ \hline
False & True  & 0.200 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.037 & 0.020 \\ \hline
False & False & 0.200 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.025 & 0.012 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 59.75 & 0.9991 & 0.000001 & 0.9969 & 156.28 & 1.0000 & 0.071 & 0.020 \\ \hline
True  & False & 0.400 & 0.1250 & 56.12 & 0.9983 & 0.000002 & 0.9953 & 156.28 & 1.0000 & 0.025 & 0.014 \\ \hline
False & True  & 0.400 & 0.1250 & 60.15 & 0.9992 & 0.000001 & 0.9970 & 156.28 & 1.0000 & 0.037 & 0.020 \\ \hline
False & False & 0.400 & 0.1250 & 56.37 & 0.9983 & 0.000002 & 0.9954 & 156.28 & 1.0000 & 0.020 & 0.012 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


% \subsection{Imperceptibility \& Capacity Results With HED \& GAN}
\subsection{Imperceptibility \& Capacity Results With HED \& GAN}

In this section, we describe the same experiment design as with the previous section, but we will measure the imperceptibility and capacity using the RCF and GAN.
\noindent Table~\ref{tab:hed_secret64_bpp_targets_grid8} shows that evaluating a $64\times64$ secret across three payload targets ($0.10$, $0.20$, and $0.40$ bits per pixel) demonstrates that the genetic algorithm (GA) is the main factor affecting cover$\to$stego distortion. When GA is active, the system achieves high cover$\to$stego quality with $\mathrm{PSNR}$ between $60.54$–$61.17\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}}=0.0992$ and between $59.75$–$60.15\,\mathrm{dB}$ at $\mathrm{BPP}_{\mathrm{used}}=0.125$, while maintaining $\mathrm{SSIM}$ between $0.9991$ and $0.9993$, $\mathrm{IF}$ between $0.9969$ and $0.9974$, and $\mathrm{MSE}$ on the order of $10^{-6}$. When GA is disabled, the system yields $\mathrm{PSNR}$ of $57.23$–$57.49\,\mathrm{dB}$ at $0.0992\,\mathrm{bpp}$ and $56.12$–$56.37\,\mathrm{dB}$ at $0.125\,\mathrm{bpp}$, with $\mathrm{SSIM}$ decreasing to $0.9983$–$0.9987$ and $\mathrm{MSE}$ increasing to $\approx 2\times10^{-6}$. Performance remains essentially unchanged when GA is active because HED has minimal impact, but a small negative effect appears when GA is disabled. Secret reconstruction is perfect in all tested scenarios: $\mathrm{PSNR}_{\text{secret}\to\text{extracted}}=\infty$ at $0.10\,\mathrm{bpp}$ and $\approx156\,\mathrm{dB}$ with $\mathrm{SSIM}=1.0000$ at higher targets. The system reaches a rate limit at $\mathrm{BPP}_{\mathrm{used}}=0.125$ for the $0.20$ and $0.40$ targets, yielding stable distortion levels rather than degradation with increased nominal payload.The GA-enhanced pipeline also operates at reasonable speeds, with embedding taking $0.02$–$0.07\,\mathrm{s}$ and extraction $0.01$–$0.04\,\mathrm{s}$.


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and GAN for secret image size $128\times128$ at BPP targets 0.100, 0.200, and 0.400}
\label{tab:hed_secret128_bpp_targets_grid10}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 60.90 & 0.9993 & 0.000001 & 0.9973 & $\infty$ & 1.0000 & 0.029 & 0.017 \\ \hline
True  & False & 0.100 & 0.0992 & 57.15 & 0.9987 & 0.000002 & 0.9958 & $\infty$ & 1.0000 & 0.020 & 0.012 \\ \hline
False & True  & 0.100 & 0.0992 & 61.23 & 0.9994 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.050 & 0.030 \\ \hline
False & False & 0.100 & 0.0992 & 57.37 & 0.9986 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.029 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 57.95 & 0.9986 & 0.000002 & 0.9962 & $\infty$ & 1.0000 & 0.056 & 0.033 \\ \hline
True  & False & 0.200 & 0.1953 & 54.14 & 0.9973 & 0.000004 & 0.9941 & $\infty$ & 1.0000 & 0.036 & 0.021 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.059 & 0.033 \\ \hline
False & False & 0.200 & 0.1953 & 54.50 & 0.9972 & 0.000004 & 0.9943 & $\infty$ & 1.0000 & 0.031 & 0.018 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 54.85 & 0.9972 & 0.000003 & 0.9945 & $\infty$ & 1.0000 & 0.209 & 0.110 \\ \hline
True  & False & 0.400 & 0.3966 & 49.24 & 0.9932 & 0.000012 & 0.9895 & $\infty$ & 1.0000 & 0.109 & 0.064 \\ \hline
False & True  & 0.400 & 0.3966 & 55.15 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.114 & 0.064 \\ \hline
False & False & 0.400 & 0.3966 & 51.49 & 0.9944 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.062 & 0.035 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:hed_secret128_bpp_targets_grid10} shows that the secret data of 128x128 pixels distributed across three payload targets at 0.10, 0.20 and 0.40 bits per pixel shows that the genetic algorithm (GA) controls the cover-to-stego distortion more than HED preselection. The PSNR values range from 60.90 to 61.23 dB when GA is enabled and the used BPP ranges from 0.0992 to 0.3966. The SSIM values range from $03\times10^{-6}$. The quality reduction reaches 3-6 dB when GA is disabled at 0.9972 to 0.9994, and MSE values range from $10^{-6}$  to  all three BPP levels (0.0992, 0.1953 and 0.3966). The SSIM values decrease to 0.9932-0.9987 and MSE values increase to $1.2\times10^{-5}$. The HED preselection method produces minimal changes when GA is active but it worsens the distortion at high payloads when GA is disabled (49.24 dB with HED vs. 51.49 dB without HED at 0.3966 bpp). The secret reconstruction process achieves perfect numerical results in all cases because $\mathrm{PSNR}_{\text{secret}\rightarrow\text{extracted}}$ equals infinity and SSIM equals 1.0000. The actual bit rates match the target values at 0.0992, 0.1953 and 0.3966 BPP. The computational time remains reasonable and grows with the rate increase (embedding time from 0.03 to 0.21 seconds and extraction time from 0.01 to 0.11 seconds) which supports the practical use of the GA-enhanced pipeline.

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % سماكة الحدود
\setlength{\tabcolsep}{3.5pt}       % فراغ أفقي داخل الخلايا
\renewcommand{\arraystretch}{1.15}  % ارتفاع الصف
\begin{table}[htbp]
\centering
\caption{Results Analysis Using HED and GAN for secret image size $512\times512$ at BPP targets 0.100, 0.200, and 0.400 }
\label{tab:hed_secret512_bpp_targets_grid22}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{HED} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.030 & 0.018 \\ \hline
True  & False & 0.100 & 0.0992 & 57.14 & 0.9987 & 0.000002 & 0.9958 & $\infty$ & 1.0000 & 0.021 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.20 & 0.9993 & 0.000001 & 0.9974 & $\infty$ & 1.0000 & 0.045 & 0.017 \\ \hline
False & False & 0.100 & 0.0992 & 57.46 & 0.9987 & 0.000002 & 0.9959 & $\infty$ & 1.0000 & 0.018 & 0.010 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.060 & 0.032 \\ \hline
True  & False & 0.200 & 0.1953 & 54.13 & 0.9973 & 0.000004 & 0.9940 & $\infty$ & 1.0000 & 0.036 & 0.021 \\ \hline
False & True  & 0.200 & 0.1953 & 58.25 & 0.9987 & 0.000001 & 0.9963 & $\infty$ & 1.0000 & 0.060 & 0.032 \\ \hline
False & False & 0.200 & 0.1953 & 54.56 & 0.9973 & 0.000003 & 0.9943 & $\infty$ & 1.0000 & 0.031 & 0.018 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.115 & 0.062 \\ \hline
True  & False & 0.400 & 0.3966 & 49.26 & 0.9932 & 0.000012 & 0.9896 & $\infty$ & 1.0000 & 0.136 & 0.044 \\ \hline
False & True  & 0.400 & 0.3966 & 55.17 & 0.9974 & 0.000003 & 0.9947 & $\infty$ & 1.0000 & 0.113 & 0.061 \\ \hline
False & False & 0.400 & 0.3966 & 51.47 & 0.9943 & 0.000007 & 0.9919 & $\infty$ & 1.0000 & 0.061 & 0.036 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:hed_secret512_bpp_targets_grid22} shows that the secret image of 512x512 pixels at three payload targets (0.10, 0.20, 0.40 bpp) demonstrates that the genetic algorithm (GA) determines cover\(\rightarrow\) stego fidelity while HED preselection has no effect when GA is active. The PSNR values decrease gradually with increasing rate when GA is active because the PSNR reaches 61.20 dB at 0.0992 BPP and 58.25 dB at 0.1953 BPP and 55.17 dB at 0.3966 BPP. The SSIM values stay between 0.9974 and 0.9993 and MSE values remain between 10\({}^{-6}\) and 3\(\times\)10\({}^{-6}\). The PSNR values decrease by several decibels when GA is disabled at all embedding rates (57.14-57.46 dB at 0.0992, 54.13-54.56 dB at 0.1953 and 49.26-51.47 dB at 0.3966). The SSIM value decreases to 0.9932 while MSE increases to 1.2\(\times\)10\({}^{-5}\) when HED operates without GA at 0.3966 bpp. The highest payload shows the most significant deterioration in performance. The secret recovery process achieves perfect numerical results in all testing scenarios because PSNR\({}_{\text{secret}\rightarrow\text{extracted}}\) equals infinity and SSIM reaches 1.0000. The achieved embedding rates match the specified targets at 0.0992, 0.1953 and 0.3966. The system operates at a reasonable speed which increases with the amount of data being processed (embedding time ranges from 0.03 to 0.12 seconds and extraction time ranges from 0.01 to 0.06 seconds).




\subsection{Security Analysis Using Edge Maps}

%In this work, we assume a passive steganalyst with no access to cover–stego pairs and no internal keys or selection masks. The algorithm is public (Kerckhoffs’ principle), but seeds/masks—if any—remain private. Consequently, the adversary cannot train matched-pair supervised detectors nor replicate the exact embedding channel, which reduces detection power compared to stronger threat models (e.g., known-cover or chosen-cover attacks.)

We consider a steganographic system with a cover image \(I \in \{0,\dots,255\}^{H \times W}\), a secret payload \(S\) (bitstream or media), and an attention map \(A \in [0,1]^{H \times W}\) that guides where embedding is preferable. The encoder and decoder are
\[
E_{\theta}:\,(I,S,A;\,K_{\text{emb}},K_{\text{white}},\mathrm{nonce}) \mapsto \hat I,
\qquad
D_{\phi}:\,(\hat I;\,K_{\text{emb}},K_{\text{white}},\mathrm{nonce}) \mapsto \hat S,
\]
operated at a fixed payload rate \(r\) bits-per-pixel (BPP). Evaluation targets high visual fidelity and imperceptibility—maximize \(\mathrm{PSNR}(I,\hat I)\) and \(\mathrm{SSIM}(I,\hat I)\)—low reconstruction error—minimize \(\mathrm{BER}(S,\hat S)\)—and low detectability by standard steganalyzers, with simple robustness sanity checks (e.g., JPEG, noise, median filtering). According to the threat model, we assume a passive adversary in the Kerckhoffs sense: the algorithm and hyperparameters are public, but the attacker has no access to the per-image keys/seeds, no paired cover-stego images of the same scene, and cannot modify the channel. So, the security mechanism without heavyweight encryption is used: secrecy is achieved by (i) an \textbf{embedding key} \(K_{\text{emb}}\) that seeds a PRNG to generate a keyed permutation over candidate pixels/blocks selected from \(A\) (via thresholding or sampling proportional to \(A\)), thereby fixing which positions carry payload at rate \(r\); (ii) an optional \textbf{whitening/permutation key} \(K_{\text{white}}\) that scrambles or XOR-whitens the payload bits so any partially recovered bits appear structureless; and (iii) a fresh per-image \textbf{nonce} to ensure unique permutations/keystreams across images. The nonce is stored as a short redundant header protected for reliability; ECC improves robustness but does not provide secrecy. Under this setting, recovering \(S\) from \(\hat I\) without the keys is computationally infeasible—the keyed placement space is combinatorial and the whitened payload is indistinguishable from noise—so a passive attacker’s best option is detection rather than extraction, while secrecy is preserved at fixed BPP. In the follwing  sections we will measure the security using two deep learnig models like Xu-Net and Ye-Net  to measure the accuracy and AUC that are supported with confusion matrices and ROC.

\subsubsection{Security Analysis Using Xu-Net, HED and CNN }

In this subsection, a security analysis is conducted to examine how effectively the proposed HED+CNN-based embedding—optimized by the Genetic Algorithm (GA) allocator—can evade detection by Xu-Net under different embedding rates (0.1, 0.2, and 0.4 bpp). The purpose is to evaluate whether the system maintains its secrecy and imperceptibility not only visually but also statistically when confronted with a state-of-the-art CNN-based detector.

Figure~\ref{fig:xu_hed_cnn_conf_all1} present that the confusion matrices show that HED{+}CNN embedding with the GA allocator becomes operationally difficult for Xu-Net to detect at bit rates between 0.1 and 0.4 bpp. The detector performs slightly better than chance at 0.1 bpp but produces numerous false alarms and fails to detect steganography at 0.2 and 0.4 bpp by always predicting ``stego" which results in random accuracy on a balanced dataset. The confusion matrices show that the system fails to recognize any difference between cover images and stego images. The overall accuracy reaches only 52.5\% at BPP=0.1 which approaches random guessing. The classifier produces a degenerate decision at BPP=0.2 and BPP=0.4 by identifying all samples as stego images which results in TPR=1.0 and TNR=0.0. The results demonstrate that the current detector fails to distinguish between cover and stego images which leads to an expected ROC area of 0.5 indicating chance-level performance.


\begin{figure}
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (HED+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_roc_all2} shows that the ROC curves of Xu-Net at \( \mathrm{BPP}\in\{0.1,\,0.2,\,0.4\} \) presents minimal variation from the chance diagonal because their AUC values remain at 0.52. The ROC curves for Xu-Net (HED+CNN) appear in the figure at three different payload rates of BPP = 0.1, 0.2 and 0.4. The ROC curves maintain a close position to the diagonal line while the AUC values reach approximately 0.52 for all decision threshold points. The results indicate that the system maintains a 50\% equal-error rate and lacks any decision threshold which provides both high true-positive detection and low false-positive results. The steganographic method demonstrates resistance to detection through this particular detector system when using this specific dataset configuration.


\begin{figure}
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_roc_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (HED+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_roc_all2}
\end{figure}
%\end{document}

\subsubsection{Security Analysis Using Xu-Net, HED, and GAN}
In this subsection, a comprehensive security analysis is carried out to examine the ability of the proposed HED+GAN embedding framework to withstand detection by Xu-Net at various embedding capacities (0.1, 0.2, and 0.4 bpp). The goal of this evaluation is to determine whether the integration of HED edge guidance with GAN-based feature learning enhances the concealment of hidden information to the extent that even an advanced CNN-based steganalyzer like Xu-Net cannot reliably differentiate between normal and stego images.

Figure~\ref{fig:xu_hed_gan_conf_all3} shows that the Xu-Net confusion matrices for \textbf{HED+GAN} achieve 52.5\% accuracy at 0.1 bpp on a balanced test set containing 20 cover images and 20 stego images.The confusion matrix shows 6 true negatives and 14 false positives and 5 false negatives and 15 true positives. The confusion matrices for Xu-Net (HED+GAN) show near–chance behavior and a strong preference for identifying \emph{stego} at all three payload rates. The model achieves 6 correct cover detections out of 20 while correctly identifying 15 out of 20 stegos at \(\mathrm{BPP}=0.1\) (accuracy \(\approx 52.5\%\), \( \mathrm{TPR}=0.75\), \( \mathrm{TNR}=0.30\)). The model performance deteriorates at both \((\mathrm{BPP}=0.2\) and \(\mathrm{BPP}=0.4\)) because it identifies 2 out of 20 covers but detects 18 out of 20 stegos (accuracy \(=50\%\), \( \mathrm{TPR}=0.90\), \( \mathrm{TNR}=0.10\)) which results in balanced accuracy values of \(\approx 0.525\) at 0.1 BPP and \(\approx 0.50\) at 0.2/0.4 BPP. The detector shows no ability to distinguish between cover and stego images based on this configuration which makes the embedding resistant to this particular XuNet (HED+GAN) setup and dataset but does not prove overall security without additional verification.

\begin{figure}[H]
  \centering

  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_gan_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_gan_conf_0_4}
  \end{subfigure}

  \caption{XuNet Confusion (HED+GAN) at different BPP.}
  \label{fig:xu_hed_gan_conf_all3}
\end{figure}

Figure~\ref{fig:xu_hed_gan_roc_all4} shows that the ROC curves for \textbf{XuNet} when testing \emph{HED{+}GAN} stego at \( \mathrm{BPP}\in\{0.1,\,0.2,\,0.4\} \) show minimal deviation from the chance diagonal with
\( \mathrm{AUC}\approx 0.53,\,0.52,\,0.52 \) values. The test set contains a small number of balanced samples which results in a staircase pattern because it contains approximately 20 cover images and 20 stego images. The ROC curves for Xu-Net (HED+GAN) appear in the following figure at BPP values of 0.1, 0.2 and 0.4. The ROC curves maintain a close position to the diagonal line while the calculated AUC values show near-chance discrimination at 0.53 for BPP=0.1 and 0.52 for BPP=0.2 and 0.4 with equal-error rates at 50\% and no operating point that achieves high TPR and low FPR. The results show that BPP changes minimally which indicates that BPP increases do not enhance detectability under this system configuration. The obtained results from this experiment show that the embedding method remains undetectable when using XuNet (HED+GAN) on this specific dataset.



\begin{figure}[H]
  \centering

  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_gan_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_HED_GAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_gan_roc_0_4}
  \end{subfigure}

  \caption{XuNet ROC (HED+GAN) at different BPP.}
  \label{fig:xu_hed_gan_roc_all4}
\end{figure}



\subsubsection{ Security Analysis Using Xu-Net, RCF, and CNN}

In this subsection, the security evaluation focuses on the proposed RCF+CNN embedding framework, where RCF (Rich Convolutional Features) provides edge-based feature guidance during embedding. The objective is to analyze how effectively this hybrid model can conceal hidden information and resist detection by Xu-Net across different embedding payloads (0.1, 0.2, and 0.4 bpp). This analysis provides insights into the degree of detectability and resilience of the RCF-guided embedding process when confronted with a state-of-the-art steganalyzer.

Figure~\ref{fig:xu_rcf_cnn_conf_all5} shows that the Xu-Net steganalyzer produces confusion matrices that show equal detection performance for both cover and stego images when evaluating the RCF+CNN embedder on a balanced test set containing 20 cover images and 20 stego images per payload.The evaluation of 0.1 bpp results in 8 true negatives and 12 false positives and 8 false negatives and 12 true positives which produces 
The evaluation metrics show 50\% accuracy and 0.60 true positive rate and 0.40 true negative rate and 0.50 precision and 0.55 F1 score and MCC value of 0. 
The confusion matrices for \textbf{XuNet (RCF+CNN)} at
\(\mathrm{BPP}=0.1,\,0.2,\,0.4\) show near–chance performance with a slight preference for \emph{stego} predictions.The classifier achieves 50\% accuracy at \(0.1\) by correctly identifying 8 out of 20 covers and 12 out of 20 stegos while producing TPR = 0.60 and TNR = 0.40. The classifier maintains 50\% accuracy at both \(0.2\) and \(0.4\) while achieving TPR = 0.65 and TNR = 0.35 by correctly identifying 7 out of 20 covers and 13 out of 20 stegos correctly. The balanced accuracy remains at 0.50 for all payload values because the classifier fails to distinguish between cover and stego images. The specific XuNet (RCF+CNN) configuration shows resistance to detection on this dataset.

%this result does not provide universal security protection because ROC/AUC values near 0.5 need verification across multiple detectors and different datasets.

\begin{figure}[H]
  \centering

  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_cnn_conf_0_4}
  \end{subfigure}

  \caption{XuNet Confusion (RCF+CNN) at different BPP.}
  \label{fig:xu_rcf_cnn_conf_all5}
\end{figure}

Figure~\ref{fig:xu_rcf_cnn_roc_all6} shows that the the ROC curves for \textbf{XuNet (RCF+CNN)} at \(\mathrm{BPP}=0.1,\,0.2,\,0.4\) show a close proximity to the diagonal line with \(\mathrm{AUC}\approx 0.52\) in all three panels. The equal–error rate reaches 50\% because the operating points fail to achieve both high TPR and low FPR across all thresholds. The AUC values remain nearly constant when BPP increases which indicates that this configuration makes stego images less detectable. The results indicate that the embedding method shows resistance to this particular Xu-Net (RCF+CNN) detector when applied to this dataset.
%although they do not confirm complete security without additional evaluation tests.


\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_cnn_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_cnn_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_cnn_roc_0_4}
  \end{subfigure}

  \caption{XuNet ROC (RCF+CNN)at different BPP.}
  \label{fig:xu_rcf_cnn_roc_all6}
\end{figure}

\subsubsection{Security Analysis Using Xu-Net, RCF, and GAN}
In this subsection, the RCF+GAN embedding framework is examined using the Xu-Net detector to assess its resistance to statistical and spatial analysis. The RCF (Rich Convolutional Features) module provides edge-aware embedding guidance, while the GAN component enhances the realism of generated or modified pixel distributions. Together, these mechanisms aim to produce stego images that are visually indistinguishable from cover images and statistically resistant to detection. The goal of this analysis is to determine whether Xu-Net—a state-of-the-art CNN-based steganalyzer—can effectively differentiate between cover and stego images across varying payload capacities (0.1, 0.2, and 0.4 bpp), thereby quantifying the detectability and resilience of the RCF+GAN–based embedding scheme.

Figure~\ref{fig:xu_rcf_gan_conf_all7} shows that the confusion matrices show near-chance performance at all BPP values from 0.1 to 0.4 while the model consistently predicts stego images. The classifier achieves 50\% accuracy at BPP=0.1 by correctly identifying 7 out of 20 covers and 13 out of 20 stegos while showing 65\% true positive rate and 35\% true negative rate. The model detects 16 out of 20 stegos but only identifies 4 out of 20 covers at BPP values of 0.2 and 0.4 (accuracy = 50\%, TPR = 0.80, TNR = 0.20). The balanced accuracy maintains 0.50 at all payload levels because the model fails to distinguish between cover and stego images. The specific XuNet (RCF+GAN) configuration on this dataset fails to detect the steganographic embedding but this result does not prove overall security because it needs verification through ROC/AUC = 0.5 results from multiple detectors and tests on different datasets.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_gan_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_gan_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_gan_conf_0_4}
  \end{subfigure}
  \caption{XuNet Confusion (RCF+GAN) at different BPP.}
  \label{fig:xu_rcf_gan_conf_all7}
\end{figure}

Figure~\ref{fig:xu_rcf_gan_roc_all8} shows that the The ROC curves show Xu-Net (RCF+GAN) performance at BPP values of 0.1, 0.2 and 0.4 with AUC values of 0.53 at 0.1 and 0.52 at 0.2 and 0.4. The ROC curves maintain a close position to the diagonal line with an AUC value of 0.53 at 0.1 and 0.52 at 0.2 and 0.4. The AUC values near 0.5 show that the system fails to find any detection point which would achieve both high true positive rates and low false positive rates at a 50\% equal error rate. The minimal variation in detection performance between different payloads indicates that this configuration makes stego images less detectable when BPP increases. The specific XuNet (RCF+GAN) detector fails to detect the embedding in this dataset but this result does not prove overall prove security because it needs additional evidence from other datasets and stronger detectors with confidence intervals.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_gan_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_gan_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Xu_RCF_GAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_gan_roc_0_4}
  \end{subfigure}
  \caption{XuNet ROC (RCF+GAN) at different BPP.}
  \label{fig:xu_rcf_gan_roc_all8}
\end{figure}

% يتطلب الحزمة booktabs لتحسين تنسيق الجداو

The results from Table~\ref{tab:hed12} show XuNet achieves 50-52.5\% accuracy and 0.515-0.525 AUC values when testing with three different payload sizes (0.1, 0.2, and 0.4 bpp) and two different decoders (CNN and GAN). The test results show that the accuracy stays between 50-52.5\% while the area under the ROC curve maintains a stable value between 0.515 and 0.525. The test results show that the balanced test set achieves 50\% accuracy and 0.5 AUC, which represents random chance performance. The small performance differences above chance level (Acc by 2.5\% and AUC by 0.025) show that the detector fails to distinguish between cover and stego images. The detection results for HED{+}CNN and HED{+}GAN show no significant difference because the detector and dataset combination does not respond to decoder selection or payload increase from 0.1 to 0.4 bpp.  The evaluated adversary (XuNet with a fixed decision threshold) fails to detect HED-based embedding at 0.1-0.4 bpp rates, which makes the method suitable for stealthy operations. 
\begin{table}[htbp]
\centering
\caption{Xu-Net Security Analysis using HED Accuracy and AUC}
\label{tab:hed12}
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\ \hline
BPP\_0.1\_HED\_CNN_GA & 52.50\% & 0.5225 \\ \hline
BPP\_0.2\_HED\_CNN_GA & 50.00\% & 0.5150 \\ \hline
BPP\_0.4\_HED\_CNN_GA & 50.00\% & 0.5150 \\ \hline
BPP\_0.1\_HED\_GAN_GA & 52.50\% & 0.5250 \\ \hline
BPP\_0.2\_HED\_GAN_GA & 50.00\% & 0.5175 \\ \hline
BPP\_0.4\_HED\_GAN & 50.00\% & 0.5175 \\ \hline
\end{tabular}
\end{table}

{\color{blue}
It can be observed from Table~\ref{tab:hed12} that most of the reported accuracies are close to 50\%, with AUC values around 0.5. 
Since the steganalysis task is a balanced binary classification problem (cover versus stego), an accuracy of approximately 50\% corresponds to chance-level performance. 
This indicates that the Xu-Net classifier is unable to reliably distinguish between cover and stego images generated by the proposed method.
Such behavior reflects a high level of security, as the embedding strategy effectively suppresses detectable artifacts even at higher payloads (BPP = 0.2 and 0.4).
The reported AUC values further confirm this observation, as values close to 0.5 indicate random guessing rather than successful discrimination.Although the reported accuracy values remain close to 50\% in most cases, the corresponding AUC values exhibit slight variations.This behavior can be explained by the fundamental difference between the two metrics.
Accuracy is computed based on a fixed decision threshold and reflects the final classification outcome, which in a balanced binary steganalysis setting results in chance-level performance when the classifier fails to make confident decisions.
In contrast, the AUC metric evaluates the ranking ability of the classifier across all possible thresholds.
Therefore, even weak discriminative signals that are insufficient to alter the final classification accuracy may still lead to small variations in AUC values.
This observation indicates that while the Xu-Net classifier captures minor statistical differences, these differences are not strong enough to enable reliable detection, thus confirming the robustness of the proposed steganographic method.

}






Table~\ref{tab:rcf22} shows that the evaluation results for cover and stego images (cover = stego) demonstrate that \textbf{Accuracy} maintains a constant value of 50.00\% for all payload sizes and both decoding methods, yet the ROC area under the curve stays near chance with AUC values between 0.52 and 0.53. The results show Xu-Net performs at random guessing levels when distinguishing between cover and stego images through RCF-based embedding since the AUC values exceed 0.50 by only 0.02 to 0.03 points while showing no consistent pattern across different payloads from 0.1 to 0.4 bpp and decoder types (CNN and GAN). The results indicate that RCF-based embedding remains difficult to detect when using Xu-Net as the adversary at the tested payload levels, which indicates effective stealth capabilities. 
\begin{table}[htbp]
\centering
\caption{Xu-Net Security Analysis using Accuracy and AUC for RCF}
\label{tab:rcf22}
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\ \hline
BPP\_0.1\_RCF\_CNN_GA & 50.00\% & 0.5250 \\ \hline
BPP\_0.2\_RCF\_CNN_GA & 50.00\% & 0.5225 \\ \hline
BPP\_0.4\_RCF\_CNN_GA & 50.00\% & 0.5225 \\ \hline
BPP\_0.1\_RCF\_GAN_GA & 50.00\% & 0.5300 \\ \hline
BPP\_0.2\_RCF\_GAN_GA & 50.00\% & 0.5200 \\ \hline
BPP\_0.4\_RCF\_GAN_GA & 50.00\% & 0.5200 \\ \hline
\end{tabular}
\end{table}


\subsubsection{Security Analysis Using Ye-Net, HED, and CNN}
In this subsection, the performance of the Ye-Net detector is evaluated when analyzing stego images generated using the HED+CNN embedding method. Ye-Net is a convolutional neural network designed to detect subtle artifacts introduced during the embedding process, making it a suitable benchmark for evaluating the resilience of modern steganographic schemes. The objective of this analysis is to examine whether the combination of HED (Holistically-Nested Edge Detection) and CNN-based embedding can effectively resist detection by Ye-Net across multiple payload capacities (0.1, 0.2, and 0.4 bpp). This evaluation helps to understand the extent to which the embedding framework maintains its imperceptibility and statistical security under deep-learning-based analysis.

Figure~\ref{fig:xu_hed_cnn_conf_all9} shows that the three confusion matrices of \textbf{Ye-Net} when tested on \emph{HED{+}CNN} stego data with a balanced test set (\(20\) cover / \(20\) stego per payload) result in complete cover decision outcomes at all payload levels (\(\mathrm{BPP}=0.1,0.2,0.4\)). The confusion matrices of Ye--Net (HED+CNN) also show a degenerate decision rule because the model identifies all images as cover. The model achieves 100\% accuracy for cover images but fails to detect any stego images which results in 50\% overall accuracy and 0\% true positive rate and 100\% true negative rate and 50\% balanced accuracy for all payload sizes. The results show that the embedding method provides no useful detection capability or operating threshold. The embedding shows resistance to this detector on this dataset, but this result does not prove overall security.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (HED+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all9}
\end{figure}
Figure~\ref{fig:xu_hed_cnn_roc_all10} shows that the ROC curves for Ye-Net (HED+CNN) at BPP = 0.1, 0.2, 0.4 . The ROC curves maintain a close position to the diagonal line with AUC values of 0.50 at 0.1 and 0.511 at 0.2 and 0.4 in all panels. The near-chance detection results indicate that the system maintains a 50\% error rate while lacking any point that would achieve high true positive rates and low false positive rates. The small changes in detectability when BPP increases indicate that this specific configuration does not benefit from higher BPP values. The specific Ye-Net (HED+CNN) detector fails to detect this steganographic method on this particular dataset.
%but this result does not prove overall security.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_roc_0_4}
  \end{subfigure}
  \caption{YeNet ROC (HED+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_roc_all10}
\end{figure}


\subsubsection{Security Analysis Using Ye-Net,HED and GAN}
In this subsection, the focus is on testing the Ye-Net detector when applied to stego images generated by the HED+GAN embedding framework. By combining HED (Holistically-Nested Edge Detection) with GAN-based adaptive embedding, the proposed method aims to distribute secret information along complex edge regions and data-driven textures, making it difficult for CNN-based steganalyzers to detect anomalies. The goal of this analysis is to assess whether Ye-Net can effectively distinguish between cover and stego images across different payload levels (0.1, 0.2, and 0.4 bpp), thereby measuring the robustness and detectability of the HED+GAN embedding approach.
Figure~\ref{fig:xu_hed_gan_conf_all11} shows that the Ye-Net (HED+GAN) confusion matrices at BPP values of 0.1, 0.2 and 0.4 show a degenerate decision rule because the model predicts all samples as cover. The decision matrix contains TN=20 and FP=0 and FN=20 and TP=0 in each panel which results in 50\% overall accuracy and 0.0 TPR and 1.0 TNR and 0.50 balanced accuracy for all payload values. The pattern shows no discrimination ability and no useful operating threshold which results in expected AUC values of 0.5 and EER values of 50\%.The embedding shows resistance to this particular detector on this particular dataset.
%but this finding does not prove overall security.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_gan_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_gan_conf_0_4}
  \end{subfigure}
  \caption{YeNet Confusion (HED+GAN) at different BPP.}
  \label{fig:xu_hed_gan_conf_all11}
\end{figure}

Figure~\ref{fig:xu_hed_gan_roc_all12} presents that the ROC curves show Ye-Net (HED+GAN) performance at three different payload levels (\(\mathrm{BPP}\)) with the third panel showing 0.2 instead of 0.4.The ROC curves in all panels maintain a close position to the diagonal line which results in AUC values of 0.5075 at 0.1 and 0.5025 at 0.2 and 0.2 in the third panel. The AUC values demonstrate that the system performs at chance level with an equal error rate of 50\%. The minimal change in detectability occurs when BPP increases under this specific configuration. The embedding shows resistance to detection by Ye-Net (HED+GAN) on this particular dataset.
%but this result does not prove overall security since it needs verification with additional detectors and separate datasets..

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_gan_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
   \includegraphics[width=\linewidth]{Figs/Ye_HED_GAN_ROC_0.4.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_gan_roc_0_2}
  \end{subfigure}\hfill
  \caption{XuNet ROC (HED+GAN) at different BPP.}
  \label{fig:xu_hed_gan_roc_all12}
\end{figure}


\subsubsection{Security Analysis Using Ye-Net, RCF and CNN}

In this subsection, the Ye-Net detector is employed to evaluate the resistance of the proposed RCF+CNN embedding framework.The RCF (Rich Convolutional Features) module enhances the embedding by utilizing detailed edge and texture information, while the CNN refines feature representation to maintain high visual fidelity. The objective of this analysis is to determine whether Ye-Net can effectively distinguish between cover and stego images at varying payload capacities (0.1, 0.2, and 0.4 bpp), thereby assessing the detectability and resilience of the RCF-guided embedding approach under neural network–based steganalysis.
Figure~\ref{fig:xu_hed_cnn_conf_all13} shows that the confusion matrices for YeNet (HED+CNN) at \(\mathrm{BPP}=0.1,\,0.2,\,0.4\) show a degenerate classifier that identifies all samples as \emph{cover}. The accuracy of each panel reaches 50\% with TN=20 and FP=0 and FN=20 and TP=0 while TPR=0.0 and TNR=1.0 and balanced accuracy equals 0.50 for all payload values. The results show no ability to distinguish between different classes because the expected AUC value approaches 0.5 and EER value approaches 50\%. The embedding shows resistance to this particular detector on this dataset.

%but this finding does not prove overall security because it needs additional evidence from multiple detectors and different.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (RCF+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all13}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_roc_all14} shows that the the ROC curves for Ye-Net (HED+CNN) at BPP values of 0.1, 0.2 and 0.4. The ROC curves match the diagonal line in all panels while showing an AUC of 0.5000 which indicates chance-level discrimination at all thresholds and a 50\% equal-error rate. The results show no change in detectability when BPP increases under this configuration. The embedding shows resistance to this particular detector on this dataset.
%but this result does not prove overall security because it needs verification through low AUC values from multiple detectors and separate datasets with confidence intervals.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_roc_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (RCF+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_roc_all14}
\end{figure}



\subsubsection{Security Analysis Using Ye-Net, RCF and GAN}
In this subsection, the Ye-Net detector is employed to assess the detectability of stego images generated by the proposed RCF+GAN embedding framework. The RCF (Rich Convolutional Features) component provides detailed edge and texture information to guide the embedding process, while the GAN (Generative Adversarial Network) enhances the realism and statistical consistency of the modified images. Together, they aim to produce embeddings that are visually imperceptible and difficult for deep-learning detectors to identify. This analysis investigates whether Ye-Net can effectively distinguish between cover and stego images across different payload capacities (0.1, 0.2, and 0.4 bpp), thereby providing insights into the detectability and resilience of the RCF+GAN–based steganographic system under deep learning–based security evaluation.
Figure~\ref{fig:xu_rcf_gan_conf_all15} shows that the the confusion matrices for Ye--Net (RCF+GAN) at \(\mathrm{BPP}=0.1,\,0.2,\,0.4\) show a degenerate decision rule because the model labels all samples as \emph{cover}. The decision rules in each panel produce TN=20 and FP=0 and FN=20 and TP=0 which results in 50\% overall accuracy and 0.0 TPR and 1.0 TNR and 0.50 balanced accuracy. The results show that the model lacks discriminative power and useful operating thresholds. The embedding shows resistance to this particular detector on this dataset.
%but this finding does not prove overall security because it needs additional testing with different detectors and separate datasets.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_gan_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_gan_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_gan_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (RCF+GAN) at different BPP.}
  \label{fig:xu_rcf_gan_conf_all15}
\end{figure}

Figure~\ref{fig:xu_rcf_gan_roc_all16} shows that the the ROC curves for Ye-Net (RCF+GAN) at BPP = 0.1, 0.2, 0.4 are shown in Figure~7.16. The curves exist on or very close to the diagonal line with AUC values of 0.4812 at 0.1 (below chance level or inverted ranking) and 0.5000 at 0.2 and 0.4. The results show that the system performs at chance level with 50\% equal error rate while lacking any point that would achieve both high true positive rate and low false positive rate. The results show that higher BPP values do not enhance detectability under this configuration because the system maintains its current performance level. The specific detector fails to detect the embedding in this dataset.
%but this result does not prove overall security because AUC performance needs evaluation with multiple detectors and different datasets..


\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_rcf_gan_roc_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_rcf_gan_roc_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Ye_RCF_GAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_rcf_gan_roc_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (RCF+GAN) at different BPP.}
  \label{fig:xu_rcf_gan_roc_all16}
\end{figure}
Table~\ref{tab:hed_performance} shows that the analysis of the results shows that the detector maintains a constant performance level across all HED-conditioned datasets and payloads with BPP values of 0.1, 0.2 and 0.4. The detector achieves 50\% accuracy in all cases while the ROC curve area remains at chance level with AUC values between 0.50 and 0.5112 for both HED+CNN and HED+GAN models. The test results show 50\% accuracy and 0.5 AUC values which indicate random performance while the small increase above 0.5 (less than 0.0112) appears statistically insignificant and demonstrates no pattern with payload or decoder selection. The operating point of the system remains at 50\% equal–error rate.The analysis of image steganography reveals that HED-based embedding becomes operationally challenging to detect when using this adversary and dataset at bit rates between 0.1 and 0.4 bpp. The detection performance does not improve when increasing the payload size and the choice between CNN and GAN decoders produces minimal impact. 
\begin{table}[H]
\centering
\caption{Performance on HED Datasets}
\label{tab:hed_performance}
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\ \hline
BPP\_0.1\_HED\_CNN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.2\_HED\_CNN_GA & 50.00\% & 0.5112 \\ \hline
BPP\_0.4\_HED\_CNN_GA & 50.00\% & 0.5112 \\ \hline
BPP\_0.1\_HED\_GAN_GA & 50.00\% & 0.5075 \\ \hline
BPP\_0.2\_HED\_GAN_GA & 50.00\% & 0.5025 \\ \hline
BPP\_0.4\_HED\_GAN_GA & 50.00\% & 0.5025 \\ \hline
\end{tabular}
\end{table}



The RCF datasets in Table~\ref{tab:rcf_performance2} show chance–level detection performance in every experimental condition.The RCF{+}CNN decoder achieves 50.00\% accuracy and 0.5000 AUC at BPP values of 0.1, 0.2 and 0.4 while showing no better performance than random guessing on a balanced test set. The RCF{+}GAN decoder shows identical performance at 0.2 and 0.4 bpp but performs below chance at 0.1 bpp with Accuracy = 48.12\% and AUC = 0.4812. The detector shows a weak negative correlation which indicates it focuses on incorrect detection indicators (the inverted AUC value would result in 0.5188 which remains near chance level). The detection performance remains at a constant 50\% error rate throughout all payload levels and decoder types because no operating point achieves a beneficial trade-off.RCF–guided embedding becomes operationally difficult to detect under this adversary and dataset when the compression ratio ranges between 0.1 and 0.4 bpp for both CNN and GAN decoders. 

\begin{table}[H]
\centering
\caption{Performance on RCF Datasets}
\label{tab:rcf_performance2}
\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}
\begin{tabular}{|l|c|c|}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\ \hline
BPP\_0.1\_RCF\_CNN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.2\_RCF\_CNN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.4\_RCF\_CNN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.1\_RCF\_GAN_GA & 48.12\% & 0.4812 \\ \hline
BPP\_0.2\_RCF\_GAN_GA & 50.00\% & 0.5000 \\ \hline
BPP\_0.4\_RCF\_GAN_GA & 50.00\% & 0.5000 \\ \hline
\end{tabular}
\end{table}

\subsection{Robustness Analysis Using Edge Maps}

In this subsection, the robustness of the proposed method is analyzed using different edge map detectors as a guiding mechanism for embedding and extraction. The objective is to assess how well the system can withstand common image processing operations such as JPEG compression, Gaussian noise, and Salt \& Pepper noise, which typically degrade image quality and challenge the reliability of data recovery. By examining the bit error rate (BER) and reconstruction accuracy (Re) of the extracted secret images, this analysis aims to reveal whether the use of edge-based embedding strategies contributes to improved resilience or if inconsistencies in the embedding–extraction synchronization affect the robustness of the overall system.
Table~\ref{tab:mini-ber-re1} shows that the Mini mode results indicate that both \textit{BER\_secret} and \textit{Re\_secret} maintain their saturated states through tests including baseline and attack scenarios: The results show \(\mathrm{BER}\approx 0.5\) and \(\mathrm{Re}\approx 0.74\!-\!0.75\) with no discernible pattern of change when attack power increases (JPEG, Gaussian, Salt\&Pepper). The observed behavior indicates that the extraction process does not match the embedding process because of potential key or threshold mismatches or header decoding or mini attention map reconstruction issues, which produce random recovery results that prevent any reliable robustness assessment.
%The pipeline needs to prove successful baseline recovery with \(\mathrm{BER}\ll 0.5\) and low \(\mathrm{Re}\) values before performing attack tests with reported statistical results from multiple seed images.
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using HED and CNN}
  \label{tab:mini-ber-re1}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none      & -                      & 0.500031 & 0.749064 \\
    Mini & JPEG      & q=90                   & 0.499207 & 0.738150 \\
    Mini & JPEG      & q=70                   & 0.506622 & 0.747780 \\
    Mini & JPEG      & q=50                   & 0.499817 & 0.731722 \\
    Mini & JPEG      & q=30                   & 0.503113 & 0.746924 \\
    Mini & Gaussian  & sigma=0.005            & 0.499084 & 0.747151 \\
    Mini & Gaussian  & sigma=0.01             & 0.500580 & 0.740908 \\
    Mini & Gaussian  & sigma=0.02             & 0.504547 & 0.751235 \\
    Mini & SaltPepper & amount=0.005, svp=0.5 & 0.500305 & 0.749631 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5 & 0.495209 & 0.746226 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5 & 0.501312 & 0.749188 \\
    \bottomrule
  \end{tabular}
\end{table}

The Mini embedding mode robustness assessment of the proposed HED--GAN hybrid model appears in Table~\ref{tab:mini-ber-re-2}. The two robustness indicators show no change in their values when the system faces different attack types at various intensities. The baseline measurement without any attack produces $\mathrm{BER}\approx0.50$ and $\mathrm{Re}\approx0.75$ while all other attack scenarios produce identical results. The system maintains its stability because the decoding process fails to extract useful secret data which results in output statistics that match random noise patterns (since a BER of 0.5 indicates random guessing). The measured attack resistance stems from a synchronization or key mismatch between the embedding and extraction stages instead of actual robustness. %The HED--GAN architecture requires two stages of improvement: first achieve accurate baseline recovery with $\mathrm{BER}\ll0.5$ and low $\mathrm{Re}$ values then perform statistical tests on multiple samples to verify its actual robustness.

\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using HED and GAN}
  \label{tab:mini-ber-re-2}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.501709 & 0.750947 \\
    Mini & JPEG       & q=90                   & 0.496399 & 0.744457 \\
    Mini & JPEG       & q=70                   & 0.498627 & 0.750294 \\
    Mini & JPEG       & q=50                   & 0.496704 & 0.738242 \\
    Mini & JPEG       & q=30                   & 0.494202 & 0.745596 \\
    Mini & Gaussian   & sigma=0.005            & 0.498413 & 0.747821 \\
    Mini & Gaussian   & sigma=0.01             & 0.500366 & 0.727148 \\
    Mini & Gaussian   & sigma=0.02             & 0.499023 & 0.751670 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.501984 & 0.750027 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.501678 & 0.750629 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.502747 & 0.754499 \\
    \bottomrule
  \end{tabular}
\end{table}

The proposed RCF-CNN model demonstrates its robustness performance through different image distortions in the Mini embedding configuration based on Table~\ref{tab:mini-ber-re-3}. The bit error rate (\textit{BER\_secret}) and relative error (\textit{Re\_secret}) maintain constant values throughout all test conditions while BER stays within $0.497\pm0.003$ and Re stays between 0.74 and 0.75. The extraction results show no change in BER values and Re values when the system operates without attacks or when it faces JPEG compression at q=90--30 or Gaussian noise at $\sigma=0.005\!-\!0.02$ or Salt\&Pepper noise at amount=0.005--0.02. The extraction results show no signs of degradation because the applied attacks do not affect the results. The BER values near 0.5 indicate random data recovery which suggests decoding errors instead of actual robustness. The system shows extraction failure through its stable performance instead of demonstrating actual resistance.
%The evaluation of robustness requires two essential steps: first achieve high extraction accuracy with BER below 0.5 and Re near zero and then establish proper embedding-extraction synchronization before performing perturbation tests for comparison.
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using RCF and CNN}
  \label{tab:mini-ber-re-3}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.496613 & 0.741609 \\
    Mini & JPEG       & q=90                   & 0.497803 & 0.744655 \\
    Mini & JPEG       & q=70                   & 0.497253 & 0.750494 \\
    Mini & JPEG       & q=50                   & 0.501587 & 0.749983 \\
    Mini & JPEG       & q=30                   & 0.501373 & 0.746055 \\
    Mini & Gaussian   & sigma=0.005            & 0.499115 & 0.743936 \\
    Mini & Gaussian   & sigma=0.01             & 0.500488 & 0.748001 \\
    Mini & Gaussian   & sigma=0.02             & 0.499512 & 0.749053 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.496582 & 0.741397 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.496796 & 0.740624 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.496704 & 0.742853 \\
    \bottomrule
  \end{tabular}
\end{table}

 The RCF-GAN hybrid steganographic model demonstrates its robustness through various image distortions in the Mini embedding configuration based on Table~\ref{tab:mini-ber-re-4}. The bit error rate (\textit{BER\_secret}) and relative reconstruction error (\textit{Re\_secret}) maintain stable values throughout all tested conditions at 0.495--0.505 BER and 0.73--0.75 Re. The baseline measurement without any attack shows \(\mathrm{BER}=0.4939\) and \(\mathrm{Re}=0.7415\) while the other conditions produce identical results. The extraction process shows no increase in either metric when attack severity increases because the extraction process remains unaffected by the introduced distortions. The BER values stay near 0.5 which indicates the recovered secret information becomes random because the embedding and extraction stages lose synchronization instead of showing actual robustness. The system maintains stability through attack but this stability results from failed decoding operations instead of actual resistance to disturbances. The framework needs to establish precise baseline recovery with \(\mathrm{BER}\ll0.5\) and low \(\mathrm{Re}\) values before performing resistance tests against compression and noise attacks.
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using RCF and GAN}
  \label{tab:mini-ber-re-4}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.493988 & 0.741467 \\
    Mini & JPEG       & q=90                   & 0.504272 & 0.739156 \\
    Mini & JPEG       & q=70                   & 0.498016 & 0.747490 \\
    Mini & JPEG       & q=50                   & 0.496857 & 0.743401 \\
    Mini & JPEG       & q=30                   & 0.499878 & 0.746629 \\
    Mini & Gaussian   & sigma=0.005            & 0.501404 & 0.751044 \\
    Mini & Gaussian   & sigma=0.01             & 0.498749 & 0.731864 \\
    Mini & Gaussian   & sigma=0.02             & 0.502136 & 0.736265 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.494537 & 0.740553 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.494415 & 0.740937 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.494751 & 0.742224 \\
    \bottomrule
  \end{tabular}
\end{table}


\section{Textures Features Detectors Results Analysis}

Analyzing the texture feature detectors plays a vital role in understanding how effectively a steganographic or image analysis system can identify and preserve fine image structures. This section presents the experimental evaluation of texture feature extraction models, focusing on their ability to capture spatial and structural information that enhances image representation. Specifically, deep learning–based architectures such as VGG16 and ResNet50 are examined for their performance in detecting texture features and maintaining image quality across different embedding conditions. The analysis employs common quantitative metrics—including PSNR, SSIM, MSE, and IF—as well as embedding and extraction times (TimeE and TimeX) to evaluate precision, stability, and computational efficiency. The goal of this evaluation is to identify which texture detection model achieves the optimal balance between visual fidelity, embedding robustness, and processing performance, thereby guiding model selection for the proposed steganographic framework.
The experimental results from texture feature extraction and analysis appear in this section. The evaluation section assesses how different texture-based feature detectors including VGG16 and ResNet50 architectures, extract detailed patterns and structural elements from image areas. The evaluation uses PSNR, SSIM, MSE, and IF metrics together with TimeE and TimeX measurements to measure the precision and stability and processing speed of each model. The section evaluates texture feature detectors to establish their effectiveness in maintaining visual quality and improving embedding quality for steganographic applications. The analysis enables model selection for the proposed system by demonstrating which texture detection method achieves the best balance between high-quality reconstruction and efficient computation.

\subsection{Imperceptibility and Capacity Results Using VGG16 and CNN}
Evaluating the imperceptibility and capacity of a steganographic system is fundamental to determining its ability to embed information while preserving high visual quality and maintaining an effective payload rate. In this subsection, the performance of the proposed framework is analyzed using VGG16 as a texture feature extractor and CNN as the embedding network. The objective is to examine how well this configuration balances image fidelity, embedding efficiency, and extraction accuracy across different target payloads (0.100, 0.200, 0.400). The analysis focuses on the effects of activating the Genetic Algorithm (GA) optimizer, which aims to enhance embedding adaptivity and perceptual quality. By evaluating quantitative measures such as PSNR, SSIM, MSE, and IF, along with embedding and extraction times, this section highlights the system’s capacity limits and its ability to maintain high imperceptibility even as the embedding payload increases.

In this section we will measure the imperceptibility  and capacity  using VGG16 and CNN.Table~\ref{tab:texture_ablation_bpp_grid1} shows the texture-attention ablation (\(\mathrm{VGG16}\times\mathrm{GA}\)) with a \(64\times64\) secret and target payloads \(\mathrm{BPP}_{\mathrm{tg}}\in\{0.100,0.200,0.400\}\) maintained an effective payload \(\mathrm{BPP}_{\mathrm{used}}\) of 0.0992 at \(\mathrm{BPP}_{\mathrm{tg}}=0.100\) and reached a maximum of 0.1250 for \(\mathrm{BPP}_{\mathrm{tg}}\in\{0.200,0.400\}\) which indicates a fixed capacity limit that does not depend on the target.\noindent
The genetic algorithm (GA) activation proved to be the main factor that improved cover fidelity because the $\mathrm{PSNR}_{c\to s}$ values increased from $51$--$55\,\mathrm{dB}$ (no GA) to $60$--$61\,\mathrm{dB}$ (with GA) when
$\mathrm{BPP}_{\mathrm{used}}$ matched between the two conditions, while $\mathrm{SSIM}_{c\to s}$ reached $0.9995$--$0.9996$, $\mathrm{MSE}_{c\to s}$ approached $10^{-6}$, and $\mathrm{IF}_{c\to s}$ reached $0.998$--$0.999$.
The ResNet50 block failed to produce any measurable improvement when GA was activated, and it actually decreased $\mathrm{PSNR}_{c\to s}$ values when both modules were disabled, indicating that this configuration does not require
the ResNet50 model. The extraction quality maintained perfect lossless performance in every experimental condition:$\mathrm{PSNR}_{s\to x}$ reached $\infty$ at $\mathrm{BPP}_{\mathrm{tg}}=0.100$ and achieved $156\,\mathrm{dB}$ at
$\mathrm{BPP}_{\mathrm{tg}}=0.200$ and $0.400$, while $\mathrm{SSIM}_{s\to x}$ remained $1.0000$. Overall, enabling GA yielded better perceptual quality at fixed payload rates; however, further increases in $\mathrm{BPP}_{\mathrm{tg}}$
beyond the observed threshold did not raise $\mathrm{BPP}_{\mathrm{used}}$ nor affect quality, suggesting the system had reached its payload capacity.

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 64*64 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and CNN}
\label{tab:texture_ablation_bpp_grid1}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{Resnet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 52.26 & 0.9982 & 0.000006 & 0.9965 & $\infty$ & 1.0000 & 0.023 & 0.016 \\ \hline
False & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.026 \\ \hline
False & False & 0.100 & 0.0992 & 55.71 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.057 & 0.046 \\ \hline
True  & False & 0.200 & 0.1250 & 51.40 & 0.9979 & 0.000007 & 0.9961 & 156.28 & 1.0000 & 0.027 & 0.018 \\ \hline
False & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.097 & 0.061 \\ \hline
False & False & 0.200 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.055 & 0.037 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.071 & 0.034 \\ \hline
True  & False & 0.400 & 0.1250 & 51.40 & 0.9979 & 0.000007 & 0.9961 & 156.28 & 1.0000 & 0.028 & 0.018 \\ \hline
False & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.089 & 0.032 \\ \hline
False & False & 0.400 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.031 & 0.020 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:texture_ablation_bpp_grid_v22} shows the 2x2 ablation study with VGG16 and GA at 128x128 secret size and target BPP values of 0.100, 0.200, and 0.400 showed no early saturation because the actual payload matched the targets (BPP-used = 0.0992, 0.1953, and 0.3966). The genetic algorithm (GA) produced the highest cover fidelity results in all experiments. The \(\mathrm{PSNR}_{c\to s}\)
values improved from 47-52 dB without GA to 55-61 dB with GA at matched payloads and \(\mathrm{SSIM}_{c\to s}\) reached 0.9984 under GA while remaining between 0.9943-0.9982 without GA and MSEc→s values decreased to \(10^{-6}\)  to \(10^{-6}\) range. The cover quality decreased when payload increased even though the GA was enabled. The\(\mathrm{PSNR}_{c\to s}\)  values reached 61 dB at BPP-used = 0.0992 and decreased to 58 dB at 0.1953 and 55 dB at 0.3966 while \(\mathrm{IF}_{c\to s}\) values decreased from 0.9987 to 0.9975. The ResNet50 model failed to provide any measurable improvement when the GA was active \((\mathrm{True}/\mathrm{True} \approx \mathrm{False}/\mathrm{True})\)
 and produced the lowest\(\mathrm{PSNR}_{c\to s}\) values at all payload levels when used without GA which indicates its supplementary function in this context. \noindent The secret extraction process maintained complete lossless performance throughout all testing scenarios, as \(\mathrm{PSNR}_{s\to x}=\infty\) and \(\mathrm{SSIM}_{s\to x}=1.0000\). The execution time increased when both GA and payload size increased.
The embedding and extraction processes took approximately twice as long when GA was enabled at each BPP setting and higher payload sizes added additional processing time. %The capacity field (Cap(bits)) maintained a fixed value for each configuration at 262144 bits regardless of target variations which indicates it stores the total number of available embedding locations instead of actual embedded bits that BPP-used measures.
\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 128*128 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and CNN}
\label{tab:texture_ablation_bpp_grid_v22}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{Resnet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.027 \\ \hline
True  & False & 0.100 & 0.0992 & 52.30 & 0.9982 & 0.000006 & 0.9965 & $\infty$ & 1.0000 & 0.023 & 0.016 \\ \hline
False & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.029 \\ \hline
False & False & 0.100 & 0.0992 & 55.69 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.026 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.161 & 0.088 \\ \hline
True  & False & 0.200 & 0.1953 & 50.02 & 0.9971 & 0.000010 & 0.9955 & $\infty$ & 1.0000 & 0.078 & 0.048 \\ \hline
False & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.156 & 0.090 \\ \hline
False & False & 0.200 & 0.1953 & 52.71 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.086 & 0.052 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.167 & 0.111 \\ \hline
True  & False & 0.400 & 0.3966 & 47.35 & 0.9943 & 0.000018 & 0.9938 & $\infty$ & 1.0000 & 0.081 & 0.050 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.161 & 0.097 \\ \hline
False & False & 0.400 & 0.3966 & 49.65 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.088 & 0.055 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:texture_ablation_bpp_grid_v33} shows the \(2\times2\) ablation study (\(\mathrm{VGG16}\times\mathrm{GA}\)) with a \(512\times512\) secret and target payloads \(\mathrm{BPP}_{\mathrm{tg}}\in\{0.100,0.200,0.400\}\) achieved effective payloads that exactly matched the target values (\(\mathrm{BPP}_{\mathrm{used}}=0.0992,\,0.1953,\,0.3966\)). The genetic algorithm (GA) produced the highest cover fidelity results for all tested conditions. The PSNR values for cover-to-secret image quality improved between 47-55 dB without GA to 55-61 dB with GA at matched payloads. The SSIM values for cover-to-secret image quality reached 0.9984-0.9996 while MSE values decreased to \(10^{-6}\)and IF values stayed at 0.997-0.999. The cover quality decreased when payload increased from 0.0992 to 0.1953 and then to 0.3966 even when the GA was active. The ResNet50 model failed to provide any measurable improvement when the GA was active because True/True results matched False/True results. The model achieved the lowest PSNR values for cover-to-secret image quality when operating independently. The extraction of secrets maintained perfect lossless quality throughout all scenarios because PSNRs reached infinity and SSIM values reached 1.0000. The execution time grew longer when both GA and payload size increased. The activation of GA during embedding and extraction operations resulted in a time increase that reached double the duration of non-GA operations while higher payload sizes extended the processing duration. 
\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 512*512 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and CNN}
\label{tab:texture_ablation_bpp_grid_v33}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{Resnet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 52.28 & 0.9982 & 0.000006 & 0.9965 & $\infty$ & 1.0000 & 0.023 & 0.016 \\ \hline
False & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.083 & 0.048 \\ \hline
False & False & 0.100 & 0.0992 & 55.72 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.050 & 0.031 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.159 & 0.088 \\ \hline
True  & False & 0.200 & 0.1953 & 50.01 & 0.9971 & 0.000010 & 0.9955 & $\infty$ & 1.0000 & 0.080 & 0.047 \\ \hline
False & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.085 & 0.049 \\ \hline
False & False & 0.200 & 0.1953 & 52.73 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.047 & 0.029 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.163 & 0.095 \\ \hline
True  & False & 0.400 & 0.3966 & 47.32 & 0.9943 & 0.000019 & 0.9938 & $\infty$ & 1.0000 & 0.082 & 0.051 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.179 & 0.097 \\ \hline
False & False & 0.400 & 0.3966 & 49.64 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.089 & 0.055 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


\subsection{Impercibtibility and Capacity Results Using VGG16 and GAN}

In this subsection, the performance of the proposed framework integrating VGG16 and GAN architectures is analyzed to assess its ability to balance high visual fidelity with efficient payload utilization. The VGG16 network acts as a texture-based feature extractor that captures fine image structures, while the GAN component contributes to learning an optimal embedding distribution that maintains realism and imperceptibility. The analysis investigates how the inclusion of the Genetic Algorithm (GA) further refines this process by optimizing embedding parameters to improve cover quality and reconstruction performance. Using quantitative metrics such as PSNR, SSIM, MSE, IF, and timing measures (TimeE, TimeX). 
Table~\ref{tab:texture_ablation_bpp_grid_v44} shows the 2x2 ablation study of VGG16 with GA used a 64x64 secret image while targeting BPPtg values of 0.100, 0.200 and 0.400. The actual payload usage remained at 0.0992 when the target was 0.100 but reached its maximum at 0.1250 for targets 0.200 and 0.400.\noindent The genetic algorithm (GA) became the primary factor improving cover fidelity: when GA was enabled, \(\mathrm{PSNR}_{c\to s}\) rose from \(52\text{--}55~\mathrm{dB}\) to \(60\text{--}61~\mathrm{dB}\), while \(\mathrm{SSIM}_{c\to s}\) reached \(0.9991\text{--}0.9996\), \(\mathrm{MSE}_{c\to s}\) approached \(10^{-6}\), and \(\mathrm{IF}_{c\to s}\) reached \(0.9976\text{--}0.9987\). The ResNet50 model produced no measurable improvement when the GA module was active, since \((\mathrm{True}/\mathrm{True})\approx(\mathrm{False}/\mathrm{True})\); moreover, in standalone use it yielded lower \(\mathrm{PSNR}_{c\to s}\) than disabling both modules.
The results show that the ResNet50 model serves as a supplementary component when used without the GA module. The secret extraction process operated with complete losslessness throughout all experiments because PSNRs reached infinity at BPPtg = 0.100 and achieved 156 dB at BPPtg = 0.200 and 0.400 while SSIMs reached 1.0000. The addition of GA to the system caused runtime extensions which resulted in 1.5 to 1.8 times longer processing times for embedding and extraction operations.

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 64*64 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and GAN}
\label{tab:texture_ablation_bpp_grid_v44}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.029 \\ \hline
True  & False & 0.100 & 0.0992 & 52.94 & 0.9985 & 0.000005 & 0.9968 & $\infty$ & 1.0000 & 0.029 & 0.019 \\ \hline
False & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.084 & 0.050 \\ \hline
False & False & 0.100 & 0.0992 & 55.71 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.049 & 0.031 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.056 & 0.034 \\ \hline
True  & False & 0.200 & 0.1250 & 52.13 & 0.9983 & 0.000006 & 0.9964 & 156.28 & 1.0000 & 0.035 & 0.023 \\ \hline
False & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.054 & 0.033 \\ \hline
False & False & 0.200 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.031 & 0.020 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.056 & 0.035 \\ \hline
True  & False & 0.400 & 0.1250 & 52.13 & 0.9983 & 0.000006 & 0.9964 & 156.28 & 1.0000 & 0.035 & 0.022 \\ \hline
False & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.054 & 0.036 \\ \hline
False & False & 0.400 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.031 & 0.020 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


Table~\ref{tab:texture_ablation_bpp_grid_v55} shows the \(2\times2\) ablation (\(\mathrm{VGG16}\times\mathrm{GA}\)) with a \(128\times128\) secret that achieved target BPP values of \(\big(0.0992,0.1953,0.3966\big)\) for its effective payload. The genetic algorithm (GA) delivered the main improvement in cover fidelity: with GA active the peak signal-to-noise ratio from cover to stego (\(\mathrm{PSNR}_{c\to s}\)) ranged approximately \(55\mbox{--}61\ \mathrm{dB}\), compared to \(47\mbox{--}53\ \mathrm{dB}\) without GA. Meanwhile, structural similarity indices (\(\mathrm{SSIM}_{c\to s}\)) were maintained between \(0.9984\) and \(0.9996\), mean-squared errors (\(\mathrm{MSE}_{c\to s}\)) remained on the order of \(10^{-6}\), and image fidelity measures (\(\mathrm{IF}_{c\to s}\)) were close to \(0.998\). Cover quality decreased monotonically with increasing payload even when the GA was used (approximately \(61\ \mathrm{dB}\) at \(0.0992\) \(\rightarrow\) \(58\ \mathrm{dB}\) at \(0.1953\) \(\rightarrow\) \(55\ \mathrm{dB}\) at \(0.3966\)). The ResNet50 block did not produce measurable improvement under GA optimization, since the results for the True/True and False/True configurations were essentially equivalent; when used alone, ResNet50 tended to produce the lowest \(\mathrm{PSNR}_{c\to s}\) values, indicating its role as a supplementary element in this setup. Extraction remained perfectly lossless across all tested conditions, as evidenced by \(\mathrm{PSNR}_{s\to x}=\infty\) and \(\mathrm{SSIM}_{s\to x}=1.0000\). Finally, execution time increased with BPP because the optimization became more complex and the system had to process a larger number of embedded bits.

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 128*128 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and GAN}
\label{tab:texture_ablation_bpp_grid_v55}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 52.96 & 0.9985 & 0.000005 & 0.9968 & $\infty$ & 1.0000 & 0.028 & 0.019 \\ \hline
False & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.041 & 0.026 \\ \hline
False & False & 0.100 & 0.0992 & 55.69 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.040 & 0.016 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.082 & 0.049 \\ \hline
True  & False & 0.200 & 0.1953 & 50.58 & 0.9974 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.055 & 0.033 \\ \hline
False & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.079 & 0.047 \\ \hline
False & False & 0.200 & 0.1953 & 52.71 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.043 & 0.028 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.164 & 0.103 \\ \hline
True  & False & 0.400 & 0.3966 & 47.05 & 0.9939 & 0.000020 & 0.9936 & $\infty$ & 1.0000 & 0.097 & 0.058 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.311 & 0.169 \\ \hline
False & False & 0.400 & 0.3966 & 49.65 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.159 & 0.116 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup



Table~\ref{tab:texture_ablation_bpp_grid_v66} reports the \(2\times2\) ablation using VGG16 with GA on a \(512\times512\) secret image and shows effective tracking of the target payloads \(\big(\mathrm{BPP}_{\mathrm{used}}=0.0992,\,0.1953,\,0.3966\big)\).
The genetic algorithm (GA) proved essential for maintaining cover fidelity: \(\mathrm{PSNR}_{c\to s}\) increased from \(47\text{--}56~\mathrm{dB}\) (no GA) to \(55\text{--}61~\mathrm{dB}\) (with GA) at equivalent payload levels, while \(\mathrm{SSIM}_{c\to s}\) remained at \(0.9996,\,0.9992,\,0.9984\); \(\mathrm{MSE}_{c\to s}\) stayed between \(10^{-6}\) and \(3\times10^{-6}\); and \(\mathrm{IF}_{c\to s}\) decreased slightly with increasing payload from \(0.9987\) to \(0.9974\).
The ResNet50 block provided no measurable benefit when GA was active since \(\mathrm{True}/\mathrm{True}\approx\mathrm{False}/\mathrm{True}\), and ResNet50 alone yielded the lowest \(\mathrm{PSNR}_{c\to s}\) when used independently.
Cover quality decreased as payload increased (approximately \(61\to58\to55~\mathrm{dB}\) across the three targets).
Secret extraction maintained lossless performance for all conditions: \(\mathrm{PSNR}_{s\to x}=\infty\) and \(\mathrm{SSIM}_{s\to x}=1.0000\).
Execution time grew with both GA and payload size: for GA-based embedding/extraction at \(\mathrm{BPP}_{\mathrm{used}}=0.3966\) the times were \(0.165\text{--}0.179~\mathrm{s}\) and \(0.095\text{--}0.098~\mathrm{s}\), respectively; without GA they were \(0.088\text{--}0.094~\mathrm{s}\) and \(0.054\text{--}0.058~\mathrm{s}\).


\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 512*512 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using VGG16 and GAN}
\label{tab:texture_ablation_bpp_grid_v66}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 52.92 & 0.9985 & 0.000005 & 0.9968 & $\infty$ & 1.0000 & 0.046 & 0.019 \\ \hline
False & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.027 \\ \hline
False & False & 0.100 & 0.0992 & 55.72 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.018 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.085 & 0.051 \\ \hline
True  & False & 0.200 & 0.1953 & 50.57 & 0.9974 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.055 & 0.035 \\ \hline
False & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.086 & 0.050 \\ \hline
False & False & 0.200 & 0.1953 & 52.73 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.049 & 0.029 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.165 & 0.095 \\ \hline
True  & False & 0.400 & 0.3966 & 47.02 & 0.9939 & 0.000020 & 0.9936 & $\infty$ & 1.0000 & 0.094 & 0.058 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.165 & 0.098 \\ \hline
False & False & 0.400 & 0.3966 & 49.64 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.088 & 0.054 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup



\subsection{Imperceptibility and Capacity Results Using ResNet50 and CNN}

In this subsection, the evaluation focuses on the performance of the ResNet50 feature extractor integrated with a CNN-based embedding network, with and without the application of the Genetic Algorithm (GA) optimizer. The aim is to analyze how these components influence cover fidelity, embedding efficiency, and extraction accuracy across various target payloads (0.100, 0.200, 0.400). The ResNet50 architecture is employed to capture deep texture and structural representations that enhance embedding precision, while the GA component is expected to refine parameter optimization for improved image quality and stability. By examining quantitative measures such as PSNR, SSIM, MSE, IF, and computational times (
TimeE,TimeX), this analysis provides insights into how the ResNet50+CNN configuration maintains visual imperceptibility, achieves lossless recovery, and manages computational efficiency under different embedding capacities.
\noindent
Table~\ref{tab:texture_ablation_bpp_grid_v77} reports the \(2\times2\) ablation with ResNet50 and GA at a \(64\times64\) secret size. The system produced an effective payload of \(\mathrm{BPP}_{\mathrm{used}}=0.0992\) when \(\mathrm{BPP}_{\mathrm{tg}}=0.100\), and reached a maximum of \(\mathrm{BPP}_{\mathrm{used}}=0.1250\) at \(\mathrm{BPP}_{\mathrm{tg}}=0.200\) and \(0.400\). The capacity limit remained unchanged across target settings within each configuration. GA was essential for maintaining cover fidelity: \(\mathrm{PSNR}_{c\to s}\) increased from \(49\text{–}56~\text{dB}\) (no GA) to \(60\text{–}61~\text{dB}\) (with GA) at equivalent payload sizes, while \(\mathrm{SSIM}_{c\to s}\) stayed at \(0.9991\text{–}0.9996\), \(\mathrm{MSE}_{c\to s}\) approached \(10^{-6}\), and \(\mathrm{IF}_{c\to s}\) remained \(0.9986\text{–}0.9987\). The ResNet50 block provided no additional improvement when GA was active since \(\mathrm{True}/\mathrm{True}\approx\mathrm{False}/\mathrm{True}\), and operating ResNet50 alone yielded the lowest \(\mathrm{PSNR}_{c\to s}\) (e.g., \(50.46~\text{dB}\) and \(49.42~\text{dB}\)) compared with disabling both modules (\(55.71~\text{dB}\) and \(54.68~\text{dB}\)). Secret extraction was perfectly lossless under all conditions: \(\mathrm{PSNR}_{s\to x}=\infty\) at \(\mathrm{BPP}_{\mathrm{used}}=0.0992\) and \(156.28~\text{dB}\) at \(\mathrm{BPP}_{\mathrm{used}}=0.1250\), while \(\mathrm{SSIM}_{s\to x}=1.0000\). Execution time increased with both GA and payload size (e.g., embedding from \(0.044~\text{s}\) to \(0.057~\text{s}\) with GA versus \(0.020~\text{s}\) to \(0.025~\text{s}\) without GA), indicating added optimization overhead. 

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 64*64 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and CNN}
\label{tab:texture_ablation_bpp_grid_v77}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.027 \\ \hline
True  & False & 0.100 & 0.0992 & 50.46 & 0.9975 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.020 & 0.014 \\ \hline
False & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.046 & 0.027 \\ \hline
False & False & 0.100 & 0.0992 & 55.71 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.057 & 0.034 \\ \hline
True  & False & 0.200 & 0.1250 & 49.42 & 0.9969 & 0.000011 & 0.9951 & 156.28 & 1.0000 & 0.025 & 0.017 \\ \hline
False & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.055 & 0.033 \\ \hline
False & False & 0.200 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.032 & 0.034 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.055 & 0.034 \\ \hline
True  & False & 0.400 & 0.1250 & 49.42 & 0.9969 & 0.000011 & 0.9951 & 156.28 & 1.0000 & 0.024 & 0.016 \\ \hline
False & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.054 & 0.033 \\ \hline
False & False & 0.400 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.033 & 0.020 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


\noindent
Table~\ref{tab:texture_ablation_bpp_grid_v88} presents the \(2\times 2\) ablation of ResNet50 with GA at a \(128\times128\) secret size and shows that the effective payloads exactly matched the targets \(\big(\mathrm{BPP}_{\mathrm{used}}=0.0992,\,0.1953,\,0.3966\big)\) without signs of early saturation. The genetic algorithm (GA) was the main factor preserving cover fidelity: it achieved \(\mathrm{PSNR}_{c\to s}\) of \(61/58/55~\mathrm{dB}\) at the three matched payloads, whereas ResNet50 alone yielded \(55.69/52.71/49.65~\mathrm{dB}\) and disabling both modules produced \(50.45/47.55/45.17~\mathrm{dB}\). Under GA, \(\mathrm{SSIM}_{c\to s}\) remained high at \(0.9996,\,0.9992,\,0.9984\), \(\mathrm{MSE}_{c\to s}\) stayed within \(10^{-6}\) to \(3\times10^{-6}\), and \(\mathrm{IF}_{c\to s}\) decreased only slightly from \(0.9987\) to \(0.9975\) with increasing payload. ResNet50 offered no measurable gain when GA was active since \(\mathrm{True}/\mathrm{True}\approx\mathrm{False}/\mathrm{True}\), and operating ResNet50 independently yielded the lowest \(\mathrm{PSNR}_{c\to s}\). Secret extraction was perfectly lossless across all conditions with \(\mathrm{PSNR}_{s\to x}=\infty\) and \(\mathrm{SSIM}_{s\to x}=1.0000\). Execution time increased with both payload and GA; combining GA with ResNet50 incurred excessive cost at medium--high payloads (total time \(\approx 0.331~\mathrm{s}\) at \(\mathrm{BPP}_{\mathrm{used}}=0.1953\)) compared to GA alone (\(\approx 0.084~\mathrm{s}\)) without any added quality benefits. 

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 128*128 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and CNN}
\label{tab:texture_ablation_bpp_grid_v88}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 50.45 & 0.9975 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.020 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.026 \\ \hline
False & False & 0.100 & 0.0992 & 55.69 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.331 & 0.231 \\ \hline
True  & False & 0.200 & 0.1953 & 47.55 & 0.9952 & 0.000018 & 0.9940 & $\infty$ & 1.0000 & 0.159 & 0.077 \\ \hline
False & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.084 & 0.049 \\ \hline
False & False & 0.200 & 0.1953 & 52.71 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.047 & 0.029 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.295 & 0.120 \\ \hline
True  & False & 0.400 & 0.3966 & 45.17 & 0.9915 & 0.000030 & 0.9921 & $\infty$ & 1.0000 & 0.073 & 0.046 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.163 & 0.096 \\ \hline
False & False & 0.400 & 0.3966 & 49.65 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.090 & 0.056 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


\noindent
Table~\ref{tab:texture_ablation_bpp_grid_v99} presents the \(2\times2\) ablation with ResNet50 and GA at a \(512\times512\) secret size and shows that the effective payloads exactly matched the targets \(\big(\mathrm{BPP}_{\mathrm{used}}=0.0992,\,0.1953,\,0.3966\big)\).
The genetic algorithm (GA) was the main factor determining cover fidelity: \(\mathrm{PSNR}_{c\to s}\) reached \(61/58/55~\mathrm{dB}\) with GA at the three payloads, but dropped to \(55.72/52.73/49.64~\mathrm{dB}\) when both modules were disabled and to \(50.48/47.57/45.15~\mathrm{dB}\) with ResNet50 alone.
Under GA, \(\mathrm{SSIM}_{c\to s}\) remained at \(0.9996,\,0.9992,\,0.9984\), while \(\mathrm{MSE}_{c\to s}\) stayed between \(10^{-6}\) and \(3\times10^{-6}\), and \(\mathrm{IF}_{c\to s}\) decreased only minimally with rising payload from \(0.9987\) to \(0.9974\).ResNet50 provided no measurable improvement when GA was running since \(\mathrm{True}/\mathrm{True}\) matched \(\mathrm{False}/\mathrm{True}\), and operating ResNet50 independently yielded the lowest \(\mathrm{PSNR}_{c\to s}\).
Secret extraction remained perfectly lossless across all setups: \(\mathrm{PSNR}_{s\to x}=\infty\) and \(\mathrm{SSIM}_{s\to x}=1.0000\).
Execution time rose with both GA activation and payload size (e.g., at \(\mathrm{BPP}_{\mathrm{used}}=0.3966\) the embedding/extraction times were \(0.161~\mathrm{s}\) and \(0.095~\mathrm{s}\) with GA, whereas without GA they ranged \(0.069\text{--}0.088~\mathrm{s}\) and \(0.053\text{--}0.059~\mathrm{s}\)).


%The value of Cap(bits) depends only on the target but not on the target value which indicates it shows available embedding locations. The actual embedded payload amount appears through the BPP_used measurement.
\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 512*512 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and CNN}
\label{tab:texture_ablation_bpp_grid_v99}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 50.48 & 0.9975 & 0.000009 & 0.9957 & $\infty$ & 1.0000 & 0.021 & 0.014 \\ \hline
False & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.048 & 0.027 \\ \hline
False & False & 0.100 & 0.0992 & 55.72 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.026 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.085 & 0.051 \\ \hline
True  & False & 0.200 & 0.1953 & 47.57 & 0.9953 & 0.000017 & 0.9940 & $\infty$ & 1.0000 & 0.035 & 0.023 \\ \hline
False & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.083 & 0.049 \\ \hline
False & False & 0.200 & 0.1953 & 52.73 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.046 & 0.029 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.161 & 0.095 \\ \hline
True  & False & 0.400 & 0.3966 & 45.15 & 0.9915 & 0.000031 & 0.9920 & $\infty$ & 1.0000 & 0.069 & 0.059 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.161 & 0.095 \\ \hline
False & False & 0.400 & 0.3966 & 49.64 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.088 & 0.053 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


\subsection{Imperceptibility and Capacity Results Using ResNet50 and GAN}

In this subsection, the performance of the ResNet50–GAN configuration is analyzed to assess how effectively deep texture extraction (via ResNet50) and adversarial learning (via GAN) contribute to improved image fidelity and embedding efficiency. The Genetic Algorithm (GA) is incorporated as an optimization component to enhance embedding adaptivity and ensure a better balance between imperceptibility and capacity. The analysis focuses on how these modules influence quantitative indicators such as PSNR, SSIM, MSE, and IF, as well as the embedding and extraction times (TimeE, TimeX), across different target payloads (=0.100, 0.200, 0.400). The purpose of this evaluation is to highlight the effects of integrating GAN-driven feature enhancement and GA-based optimization on the overall quality, stability, and performance of the steganographic framework.

Table~\ref{tab:texture_ablation_bpp_grid_v1010} shows the \(2\times2\) ablation study of ResNet50 with GA at a \(64\times64\) secret size produced an effective payload of 0.0992 at \(\mathrm{BPP}_{\mathrm{tg}}=0.100\) before reaching a saturation point of 0.1250 at \(\mathrm{BPP}_{\mathrm{tg}}\in\{0.200,0.400\}\) which proved to be independent from the target setting. The genetic algorithm (GA) function proved essential for maintaining cover fidelity because it produced \(\mathrm{PSNR}_{c\to s}\) values of 60.9–61.1 dB at \(\mathrm{BPP}_{\mathrm{used}}=0.0992\) and 59.9–60.0 dB at 0.1250 while achieving \(\mathrm{SSIM}_{c\to s}=0.9995\text{--}0.9996\) and \(\mathrm{MSE}_{c\to s}\approx 10^{-6}\) and \(\mathrm{IF}_{c\to s}=0.9985\text{--}0.9987\). The absence of GA resulted in \(\mathrm{PSNR}_{c\to s}\) values between 54.7–55.7 dB when ResNet50 was disabled while the ResNet50-only configuration produced the worst results at 46–47 dB which proved that GA produces better results than ResNet50 when it is active. The secret extraction process maintained perfect lossless performance in all tested scenarios because \(\mathrm{PSNR}_{s\to x}\) reached infinity at 0.0992 and reached 156.28 dB at 0.1250 while \(\mathrm{SSIM}_{s\to x}\) maintained a value of 1.0000. The execution time grew longer when the system used GA and it also increased slightly when the payload size increased. The execution time for TimeE and TimeX under GA conditions ranged from 0.042 to 0.053 seconds and 0.026 to 0.046 seconds respectively but ResNet50-only execution times reached 0.016 to 0.020 seconds and 0.012 to 0.014 seconds. 
\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 64*64 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and GAN}
\label{tab:texture_ablation_bpp_grid_v1010}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 60.90 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.042 & 0.026 \\ \hline
True  & False & 0.100 & 0.0992 & 47.23 & 0.9956 & 0.000019 & 0.9937 & $\infty$ & 1.0000 & 0.016 & 0.012 \\ \hline
False & True  & 0.100 & 0.0992 & 61.05 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.078 & 0.048 \\ \hline
False & False & 0.100 & 0.0992 & 55.71 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.048 & 0.028 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1250 & 59.86 & 0.9995 & 0.000001 & 0.9985 & 156.28 & 1.0000 & 0.052 & 0.032 \\ \hline
True  & False & 0.200 & 0.1250 & 46.28 & 0.9948 & 0.000024 & 0.9930 & 156.28 & 1.0000 & 0.020 & 0.014 \\ \hline
False & True  & 0.200 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.050 & 0.031 \\ \hline
False & False & 0.200 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.029 & 0.020 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.1250 & 59.86 & 0.9995 & 0.000001 & 0.9985 & 156.28 & 1.0000 & 0.052 & 0.033 \\ \hline
True  & False & 0.400 & 0.1250 & 46.28 & 0.9948 & 0.000024 & 0.9930 & 156.28 & 1.0000 & 0.020 & 0.014 \\ \hline
False & True  & 0.400 & 0.1250 & 60.02 & 0.9995 & 0.000001 & 0.9986 & 156.28 & 1.0000 & 0.053 & 0.046 \\ \hline
False & False & 0.400 & 0.1250 & 54.68 & 0.9989 & 0.000003 & 0.9973 & 156.28 & 1.0000 & 0.030 & 0.019 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


\noindent
Table~\ref{tab:texture_ablation_bpp_grid_v1111} shows the \(2\times2\) ablation study with ResNet50 and GA at a \(128\times128\) secret size and reports effective payload values that matched the targets \(\big(\mathrm{BPP}_{\mathrm{used}}=0.0992,\,0.1953,\,0.3966\big)\) without any signs of early saturation.The genetic algorithm (GA) maintained high cover fidelity at all three payload levels: \(\mathrm{PSNR}_{c\to s}\) reached \(61/58/55~\mathrm{dB}\), \(\mathrm{SSIM}_{c\to s}\) reached \(0.9996/0.9992/0.9984\), and \(\mathrm{MSE}_{c\to s}\) was within \(10^{-6}\) to \(3\times10^{-6}\), while \(\mathrm{IF}_{c\to s}\) decreased slightly from \(0.9987\) to \(0.9975\).Without GA, \(\mathrm{PSNR}_{c\to s}\) dropped to \(55.69/52.71/49.65~\mathrm{dB}\), and using ResNet50 alone produced the lowest values \(47.22/44.54/42.16~\mathrm{dB}\).The combination of ResNet50 with GA produced identical results to GA alone since \(\mathrm{True}/\mathrm{True}=\mathrm{False}/\mathrm{True}\).
Secret extraction remained lossless in every tested condition: \(\mathrm{PSNR}_{s\to x}=\infty\) and \(\mathrm{SSIM}_{s\to x}=1.0000\).Processing time increased with higher payloads and GA activation; moreover, the combination of GA with ResNet50 produced shorter processing times than GA alone at medium and high payload levels \big(\(\mathrm{TimeE}=0.099~\mathrm{s}\) vs.\ \(0.148~\mathrm{s}\) at \(0.1953\) and \(\mathrm{TimeE}=0.177~\mathrm{s}\) vs.\ \(0.163~\mathrm{s}\) at \(0.3966\)\big).



\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 128*128 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and GAN}
\label{tab:texture_ablation_bpp_grid_v1111}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.028 \\ \hline
True  & False & 0.100 & 0.0992 & 47.22 & 0.9957 & 0.000019 & 0.9937 & $\infty$ & 1.0000 & 0.018 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.04 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.044 & 0.026 \\ \hline
False & False & 0.100 & 0.0992 & 55.69 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.026 & 0.017 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.099 & 0.048 \\ \hline
True  & False & 0.200 & 0.1953 & 44.54 & 0.9923 & 0.000035 & 0.9915 & $\infty$ & 1.0000 & 0.033 & 0.021 \\ \hline
False & True  & 0.200 & 0.1953 & 58.06 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.148 & 0.092 \\ \hline
False & False & 0.200 & 0.1953 & 52.71 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.084 & 0.055 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.177 & 0.101 \\ \hline
True  & False & 0.400 & 0.3966 & 42.16 & 0.9861 & 0.000061 & 0.9888 & $\infty$ & 1.0000 & 0.065 & 0.040 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9975 & $\infty$ & 1.0000 & 0.163 & 0.094 \\ \hline
False & False & 0.400 & 0.3966 & 49.65 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.089 & 0.053 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup

\noindent
Table~\ref{tab:texture_ablation_bpp_grid_v1222} presents the \(2\times2\) ablation with ResNet50 and GA at a \(512\times512\) secret size and shows that the effective payloads exactly matched the targets \(\big(\mathrm{BPP}_{\mathrm{used}}=0.0992,\,0.1953,\,0.3966\big)\), indicating operation within the requested payload range without early saturation.
The genetic algorithm (GA) was the main factor improving cover fidelity: the system achieved \(\mathrm{PSNR}_{c\to s}\) of \(61/58/55~\mathrm{dB}\) with corresponding \(\mathrm{SSIM}_{c\to s}\) of \(0.9996/0.9992/0.9984\), while \(\mathrm{MSE}_{c\to s}\) remained between \(10^{-6}\) and \(3\times 10^{-6}\) and \(\mathrm{IF}_{c\to s}\) decreased only minimally with payload from \(0.9987\) to \(0.9974\).
ResNet50 provided no measurable improvement when GA was active, since \(\mathrm{True}/\mathrm{True}\) matched \(\mathrm{False}/\mathrm{True}\), and operating ResNet50 independently produced the lowest \(\mathrm{PSNR}_{c\to s}\) values (\(47.24~\mathrm{dB}\), \(44.58~\mathrm{dB}\), \(42.13~\mathrm{dB}\)).
Disabling both modules yielded intermediate quality with \(\mathrm{PSNR}_{c\to s}=55.72/52.73/49.64~\mathrm{dB}\).Secret extraction maintained complete losslessness in all conditions: \(\mathrm{PSNR}_{s\to x}=\infty\) and \(\mathrm{SSIM}_{s\to x}=1.0000\).
Processing time increased with both payload and GA usage: \(\mathrm{TimeE}/\mathrm{TimeX}\) extended from \(0.045/0.027~\mathrm{s}\) at \(\mathrm{BPP}_{\mathrm{used}}=0.0992\) to \(0.172/0.097~\mathrm{s}\) at \(\mathrm{BPP}_{\mathrm{used}}=0.3966\) under GA, whereas non-GA configurations were faster.

\begingroup
\setlength{\arrayrulewidth}{0.6pt}  % border thickness
\setlength{\tabcolsep}{3.5pt}       % horizontal padding
\renewcommand{\arraystretch}{1.15}  % row height
\begin{table}[htbp]
\centering
\caption{Results Analysis (Texture Attention) Using Secret Image Size 512*512 at BPP targets 0.100, 0.200, and 0.400 \,|\, Using ResNet50 and GAN}
\label{tab:texture_ablation_bpp_grid_v1222}
\resizebox{\textwidth}{!}{%
\begin{tabular}{|c|c|r|r|r|r|r|r|r|r|r|r|}
\hline
\textbf{ResNet50} & \textbf{GA} & \textbf{BPP$_{\mathrm{tg}}$} & \textbf{BPP$_{\mathrm{used}}$} &
\textbf{$\mathrm{PSNR}_{c\to s}$} & \textbf{$\mathrm{SSIM}_{c\to s}$} &
\textbf{$\mathrm{MSE}_{c\to s}$} & \textbf{$\mathrm{IF}_{c\to s}$} &
\textbf{$\mathrm{PSNR}_{s\to x}$} & \textbf{$\mathrm{SSIM}_{s\to x}$} &
\textbf{TimeE} & \textbf{TimeX} \\
\hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.100}} \\ \hline
True  & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.045 & 0.027 \\ \hline
True  & False & 0.100 & 0.0992 & 47.24 & 0.9957 & 0.000019 & 0.9937 & $\infty$ & 1.0000 & 0.019 & 0.013 \\ \hline
False & True  & 0.100 & 0.0992 & 61.06 & 0.9996 & 0.000001 & 0.9987 & $\infty$ & 1.0000 & 0.062 & 0.029 \\ \hline
False & False & 0.100 & 0.0992 & 55.72 & 0.9991 & 0.000003 & 0.9976 & $\infty$ & 1.0000 & 0.027 & 0.018 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.200}} \\ \hline
True  & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.083 & 0.052 \\ \hline
True  & False & 0.200 & 0.1953 & 44.58 & 0.9923 & 0.000035 & 0.9915 & $\infty$ & 1.0000 & 0.034 & 0.021 \\ \hline
False & True  & 0.200 & 0.1953 & 58.08 & 0.9992 & 0.000002 & 0.9982 & $\infty$ & 1.0000 & 0.081 & 0.048 \\ \hline
False & False & 0.200 & 0.1953 & 52.73 & 0.9982 & 0.000005 & 0.9967 & $\infty$ & 1.0000 & 0.046 & 0.033 \\ \hline
\multicolumn{12}{|l|}{\textbf{BPP Target = 0.400}} \\ \hline
True  & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.172 & 0.097 \\ \hline
True  & False & 0.400 & 0.3966 & 42.13 & 0.9861 & 0.000061 & 0.9887 & $\infty$ & 1.0000 & 0.065 & 0.044 \\ \hline
False & True  & 0.400 & 0.3966 & 55.03 & 0.9984 & 0.000003 & 0.9974 & $\infty$ & 1.0000 & 0.161 & 0.095 \\ \hline
False & False & 0.400 & 0.3966 & 49.64 & 0.9964 & 0.000011 & 0.9953 & $\infty$ & 1.0000 & 0.095 & 0.056 \\ \hline
\end{tabular}%
}
\end{table}
\endgroup


%\subsection{Security Analysis Using Xu-Net, VGG16 and CNN }
%\begin{figure}[htbp]
  %\centering
  %\begin{subfigure}{0.32\textwidth}
   % \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.1.PNG}
   % \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
  %\end{subfigure}\hfill
  %\begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.2.PNG}
   % \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
  %\end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
   % \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.4.PNG}
   % \caption{BPP = 0.4}
   % \label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
  %\caption{Xu-Net Convusion (VGG16+CNN) at different BPP.}
  %\label{fig:xu_hed_cnn_conf_all11}
%\end{figure}


%\begin{figure}[htbp]
  %\centering
 % \begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.1.PNG}
   % \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
  %  \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.2.PNG}
   % \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
  %\end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.4.PNG}
  %  \caption{BPP = 0.4}
  %  \label{fig:xu_hed_cnn_conf_0_4}
 % \end{subfigure}
 % \caption{Xu-Net ROC (VGG16+CNN) at different BPP.}
 % \label{fig:xu_hed_cnn_conf_all10}
%\end{figure}

%\subsection{Security Analysis Using Xu-Net,  VGG16 and GAN }

%\begin{figure}[htbp]
%  \centering
%  \begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.1.PNG}
   % \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
  %\end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.2.PNG}
  %  \caption{BPP = 0.2}
  %  \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
%  \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.4.PNG}
   % \caption{BPP = 0.4}
  %  \label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
  %\caption{Xu-Net Convusion (VGG16+GAN) at different BPP.}
  %\label{fig:xu_hed_cnn_conf_all11}
%\end{figure}


%\begin{figure}[htbp]
%  \centering
%  \begin{subfigure}{0.32\textwidth}
 %   \centering
 %   \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.1.PNG}
  %  \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
  %\end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
 %   \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.2.PNG}
 %   \caption{BPP = 0.2}
 %   \label{fig:xu_hed_cnn_conf_0_2}
%  \end{subfigure}\hfill
%  \begin{subfigure}{0.32\textwidth}
%    \centering
%    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.4.PNG}
%    \caption{BPP = 0.4}
 %   \label{fig:xu_hed_cnn_conf_0_4}
%  \end{subfigure}
%  \caption{Xu-Net ROC (VGG16+GAN) at different BPP.}
 % \label{fig:xu_hed_cnn_conf_all10}
%\end{figure}

%\subsection{Security Analysis Using Xu-Net, ResNet50 and CNN }


%\begin{figure}[htbp]
%  \centering
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.1.PNG}
  %  \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
 %   \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.2.PNG}
  %  \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
 %   \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.4.PNG}
  %  \caption{BPP = 0.4}
  %  \label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
  %\caption{Xu-Net Convusion (ResNet50+CNN) at different BPP.}
  %\label{fig:xu_hed_cnn_conf_all11}
%\end{figure}


%\begin{figure}[htbp]
 % \centering
  %\begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.1.PNG}
  %  \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
  %\end{subfigure}\hfill
  %\begin{subfigure}{0.32\textwidth}
   % \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.2.PNG}
   % \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
  %\begin{subfigure}{0.32\textwidth}
  %  \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.4.PNG}
   % \caption{BPP = 0.4}
   % \label{fig:xu_hed_cnn_conf_0_4}
 % \end{subfigure}
 % \caption{Xu-Net ROC (ResNet50+CNN) at different BPP.}
 % \label{fig:xu_hed_cnn_conf_all10}
%\end{figure}


%\subsection{Security Analysis Using Xu-Net,  ResNet50 and GAN }

%\begin{figure}[htbp]
%  \centering
%  \begin{subfigure}{0.32\textwidth}
%    \centering
 %   \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.1.PNG}
  %  \caption{BPP = 0.1}
   % \label{fig:xu_hed_cnn_conf_0_1}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
  %  \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.2.PNG}
   % \caption{BPP = 0.2}
   % \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
  %\begin{subfigure}{0.32\textwidth}
   % \centering
   % \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.4.PNG}
    %\caption{BPP = 0.4}
    %\label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
 % \caption{Xu-Net Convusion (ResNet50+GAN) at different BPP.}
  %\label{fig:xu_hed_cnn_conf_all11}
%\end{figure}


%\begin{figure}[htbp]
 % \centering
  %\begin{subfigure}{0.32\textwidth}
  %  \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.1.PNG}
  %  \caption{BPP = 0.1}
  %  \label{fig:xu_hed_cnn_conf_0_1}
%  \end{subfigure}\hfill
%  \begin{subfigure}{0.32\textwidth}
%    \centering
%    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.2.PNG}
%    \caption{BPP = 0.2}
%    \label{fig:xu_hed_cnn_conf_0_2}
 % \end{subfigure}\hfill
 % \begin{subfigure}{0.32\textwidth}
  %  \centering
  %  \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.4.PNG}
 %   \caption{BPP = 0.4}
 %   \label{fig:xu_hed_cnn_conf_0_4}
  %\end{subfigure}
 % \caption{Xu-Net ROC (ResNet50+GAN) at different BPP.}
 % \label{fig:xu_hed_cnn_conf_all10}
%\end{figure}



%\begin{table}[ht]
%\centering
%\begin{tabular}{lcc}
%\hline
%\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
%\hline
%BPP\_0.1\_ResNet50\_CNN  & 50.00\% & 0.5375 \\
%BPP\_0.2\_ResNet50\_CNN  & 50.00\% & 0.5500 \\
%BPP\_0.4\_ResNet50\_CNN  & 50.00\% & 0.5500 \\
%BPP\_0.1\_ResNet50\_GAN  & 50.00\% & 0.5375 \\
%BPP\_0.2\_ResNet50\_GAN  & 50.00\% & 0.5450 \\
%BPP\_0.4\_ResNet50\_GAN  & 50.00\% & 0.5450 \\
%\hline
%\end{tabular}
%\caption{Xu-Net Results Using ResNet50}
%\end{table}

%\begin{table}[ht]
%\centering
%\begin{tabular}{lcc}
%\hline
%\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
%\hline
%BPP\_0.1\_VGG16\_CNN  & 50.00\% & 0.5325 \\
%BPP\_0.2\_VGG16\_CNN  & 50.00\% & 0.5175 \\
%BPP\_0.4\_VGG16\_CNN  & 50.00\% & 0.5175 \\
%BPP\_0.1\_VGG16\_GAN  & 50.00\% & 0.5325 \\
%BPP\_0.2\_VGG16\_GAN  & 50.00\% & 0.5175 \\
%BPP\_0.4\_VGG16\_GAN  & 50.00\% & 0.5175 \\
%\hline
%\end{tabular}
%\caption{Xu-Net Results Using VGG16  }
%\end{table}


\subsection{Security Analysis Using Texture Maps}
Evaluating the security performance of a steganographic system is essential to determine its resistance against advanced steganalysis methods. In this subsection, the focus is on analyzing the security behavior of texture-based embedding models that utilize deep feature representations such as texture maps derived from convolutional neural networks (CNNs). These texture maps capture spatial and structural patterns that guide the embedding process, potentially influencing the detectability of hidden information. The security analysis examines how effectively the system can conceal embedded data without being detected by modern deep-learning steganalyzers such as Xu-Net and Ye-Net. By comparing different configurations involving texture feature extraction and optimization modules, this analysis aims to reveal the level of imperceptibility, resilience, and statistical indistinguishability achieved by texture-guided embedding methods under varying payload conditions.

\subsubsection{Security Analysis Using Xu-Net, VGG16 and CNN }

In this subsection, the Xu-Net steganalyzer is employed to evaluate the detectability of stego images generated using the VGG16+CNN embedding framework. The VGG16 network provides rich texture features that enhance spatial detail during embedding, while the CNN module performs adaptive encoding to preserve image fidelity. The goal of this analysis is to determine whether these combined components introduce detectable artifacts that Xu-Net can exploit to distinguish between cover and stego images. By analyzing confusion matrices and performance metrics such as accuracy, TPR, TNR, and balanced accuracy across multiple payload levels (BPP=0.1, 0.2, 0.4), this evaluation provides insight into the detectability, consistency, and resilience of the VGG16-based embedding approach under CNN-driven steganalysis.
Figure~\ref{fig:xu_hed_cnn_conf_all112} shows that the three confusion matrices for \textbf{Xu-Net (VGG16+CNN)} present identical results when evaluated at different payload rates \(\mathrm{BPP}\in\{0.1,0.2,0.4\}\) on a balanced test set containing 40 images with 20 cover and 20 stego images.The panels show identical results with TN=7 and FP=13 and FN=7 and TP=13 which produces an overall accuracy of 50\% and TPR=0.65 and TNR=0.35 and balanced accuracy of 0.50. The classifier shows a preference for stego predictions through its high false positive rates on cover images while achieving near-chance discrimination performance. The detection performance remains constant when BPP increases because this specific configuration does not enhance detectability. 

%The specific Xu--Net (VGG16+CNN) configuration fails to detect this steganographic method on this dataset but this result does not prove overall security because it needs verification through better detectors and separate datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (VGG16+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all112}
\end{figure}

The ROC curves in Figure~\ref{fig:xu_hed_cnn_conf_all102} show the performance of Xu-Net (VGG16+CNN) at different payload rates (BPP = 0.1, 0.2, 0.4) with AUC values of 0.53, 0.52, 0.53 which are close to the random-guess baseline of 0.5.The curves in all panels show a close proximity to the diagonal line with AUC values of 0.53 at 0.1 and 0.52 at 0.2 and 0.4. The near-chance values show that the system maintains a 50\% equal-error rate and lacks any operating point which achieves both high TPR and low FPR. The minimal change in detectability occurs when BPP increases under this configuration because the system does not improve its detection capabilities. The specific Xu--Net (VGG16+CNN) detector fails to detect this embedding on this dataset.
%but this result does not prove overall security because additional tests with different detectors and datasets are needed.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (VGG16+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all102}
\end{figure}

\subsubsection{Security Analysis Using Xu-Net,VGG16 and GAN }
In this subsection, the Xu-Net detector is applied to analyze the detectability of stego images generated using the VGG16+GAN embedding framework. The VGG16 network serves as a texture feature extractor that guides the embedding process by preserving fine structural details, while the GAN component introduces adversarial learning to enhance the naturalness and realism of the generated stego images. The primary objective of this analysis is to investigate whether Xu-Net can effectively distinguish between cover and stego images across different payload capacities (BPP=0.1, 0.2, 0.4). By examining the confusion matrices and key performance indicators such as accuracy, TPR, TNR, and balanced accuracy, this evaluation highlights the resilience and undetectability of the VGG16+GAN–based embedding scheme under deep learning-based steganalysis.

Figure~\ref{fig:xu_hed_cnn_conf_all113} shows the confusion matrices for \textbf{Xu-Net (VGG16+GAN)} show identical results at payload rates \(\mathrm{BPP}\in\{0.1,0.2,0.4\}\) when tested on a balanced dataset of 40 images with 20 cover and 20 stego images.The panels show identical results with TN=7 and FP=13 and FN=7 and TP=13 which leads to 50\% overall accuracy and 0.65 TPR and 0.35 TNR and 0.50 balanced accuracy. The classifier shows a bias toward predicting stego images because it produces high false positives on cover images which results in random discrimination with no effective detection threshold (EER ≈ 50\%). The detector fails to detect any changes in payload sizes because the results remain constant across all BPP values. 
%The specific Xu--Net (VGG16+GAN) detector fails to detect this steganographic method on this dataset but this result does not prove overall security because it needs verification through other detectors and different datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (VGG16+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all113}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_conf_all104} shows the ROC curves for \textbf{Xu-Net (VGG16+GAN)} at different payload rates \(\mathrm{BPP}\in\{0.1,0.2,0.4\}\) show a staircase pattern near the diagonal which results in an AUC value of 0.53 for all three conditions above the random-guess baseline of 0.5.The curves in all panels maintain a close position to the diagonal line with AUC values of 0.53 at 0.1 and 0.52 at 0.2 and 0.4. The near-chance values indicate that the equal-error rate reaches 50\% and there exists no operating point which achieves both high true positive rate and low false positive rate. The minimal change in payload detection performance indicates that this configuration does not benefit from higher BPP values.

%The specific Xu--Net (VGG16+GAN) detector fails to detect this steganographic method on this dataset but this result does not provide universal security assurance because it needs verification with other detectors and different datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_VGG16GAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (VGG16+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all104}
\end{figure}

\subsubsection{Security Analysis Using Xu-Net,ResNet50 and CNN }

In this subsection, the focus is on testing the Xu-Net detector to analyze its ability to identify hidden information embedded through texture-guided mapping techniques. Texture maps—derived from deep architectures such as VGG16, ResNet50, or GAN-based models—capture fine-grained structural and statistical patterns that guide adaptive embedding in visually complex regions. The objective of this analysis is to determine whether these texture-driven embedding strategies produce detectable artifacts that Xu-Net can exploit to distinguish between cover and stego images. By examining confusion matrices and performance metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy across multiple payload levels, this subsection provides a detailed assessment of the detectability, robustness, and security of texture-based embedding frameworks under CNN-driven steganalysis.

Figure~\ref{fig:xu_hed_cnn_conf_all115} shows the confusion matrices for \textbf{Xu-Net (ResNet (ResNet50+CNN)} show evaluation results at payload rates \(\mathrm{BPP}\in\{0.1,0.2,0.4\}\) on a balanced test set containing 20 cover images and 20 stego images. The classifier produces 4 true negatives and 16 false positives and 4 false negatives and 16 true positives when BPP equals 0.1 which results in 50\% accuracy and 0.80 TPR and 0.20 TNR and 0.50 balanced accuracy. The classifier predicts all samples as stego at BPP values of 0.2 and 0.4 which results in TN=0 and FP=20 FN=0 TP=20 and maintains 50\% accuracy with TPR=1.00 and TNR=0.00 (balanced accuracy = 0.50). The results show that the detector achieves near-chance performance and lacks an effective operating threshold and the detection performance does not improve with increased payload amounts. 
%The specific Xu--Net (ResNet50+CNN) detector fails to detect this steganographic embedding on this dataset but this result does not prove overall security because it needs verification through low ROC/AUC scores from multiple detectors on separate datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (ResNet50+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all115}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_conf_all106}The ROC curves for \textbf{Xu-Net (ResNet50+CNN)} at payload rates \(\mathrm{BPP}\in\{0.1,0.2,0.4\}\) show a diagonal pattern with a staircase structure which results in AUC values of \(\{0.54, 0.56, 0.55\}\) that are slightly higher than the random baseline (AUC=0.5).The AUC values remain near 0.54 at BPP=0.1 and 0.55 at BPP=0.2 and 0.4 across all panels. The ROC curves show weak discrimination because they stay near the diagonal line which indicates a 50\% equal-error rate and no operating point can achieve both high true-positive and low false-positive rates. The TPR shows improvement only when the FPR reaches high levels. The minimal change in performance with increasing BPP indicates that this configuration does not enhance detection capabilities.
%The embedding shows resistance to this particular detector on this particular dataset but this result does not prove overall security because it needs verification with more robust detectors and different datasets along with confidence interval analysis.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetCNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (ResNet50+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all106}
\end{figure}


\subsubsection{Security Analysis Using Xu-Net,ResNet50 and GAN }
In this subsection, the Xu-Net detector is employed to analyze the detectability of stego images generated using the ResNet50+GAN embedding framework. The ResNet50 component serves as a deep feature extractor that captures high-level texture and structural characteristics, while the GAN model contributes to producing visually natural and statistically consistent stego images through adversarial learning. The purpose of this evaluation is to determine whether Xu-Net can effectively discriminate between cover and stego images across multiple payload rates (BPP=0.1,0.2,0.4). By examining the confusion matrices and key performance metrics—such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy—this analysis provides insight into the detectability, bias, and overall security resilience of the ResNet50+GAN-based embedding method under CNN-based steganalysis.
Figure~\ref{fig:xu_hed_cnn_conf_all1166} shows the confusion matrices for Xu-Net (ResNet50+GAN) at payload rates \(\mathrm{BPP}\in\{0.1,0.2,0.4\}\) show identical results when tested on a balanced test set containing 20 cover images and 20 stego images.The confusion matrix entries in all panels show TN=4 and FP=16 and FN=4 and TP=16.
The accuracy reaches 50\% while TPR equals 0.80 and TNR equals 0.20 and balanced accuracy equals 0.50.The classifier shows a strong preference for predicting stego images because it produces many incorrect positive results on cover images thus resulting in random detection performance with no effective threshold.The results show no changes in detection performance when BPP increases under this configuration.

%The specific detector fails to detect this steganographic method when applied to this dataset although this result does not prove overall security because additional tests with different detectors and datasets are required.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net Confusion (ResNet50+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1166}
\end{figure}

Figure~\ref{fig:xu_hed_cnn_conf_all1088} shows the ROC curves performance at BPP values of 0.1, 0.2 and 0.4. The ROC curves maintain a close position to the diagonal line with AUC values of approximately 0.54 which indicates random discrimination and a 50\% equal-error rate. The true-positive rate shows significant growth only when false-positive rates reach high levels which prevents finding any operating point that achieves both high TPR and low FPR. The small changes in performance across different payload values indicate that BPP increases will not enhance detection capabilities under this system configuration. The steganographic embedding shows resistance to this particular detector when applied to this dataset.
%but this result does not ensure overall security because it needs verification through tests with different detectors and separate datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Xu_ResnetGAN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Xu-Net ROC (ResNet50+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1088}
\end{figure}

The table results demonstrate that Xu-Net with ResNet50 backbone achieves chance-level performance when using either a plain CNN head or a GAN component for all payload sizes. The Accuracy value stays at 50.00\% in all configurations while ROC AUC scores range between 0.5375 and 0.5500. The AUC values show minimal changes between 0.5375 and 0.5500 while the BPP increases from 0.1 to 0.4 and the GAN model performs identically to the CNN model. The small AUC variations between 0.0125 and 0.0125 do not indicate actual performance improvement because they fall within typical random variation. The current preprocessing and training architecture fails to extract embedding residuals because the detection performance remains constant when BPP increases from 0.1 to 0.4. 
%The detector fails to distinguish between stego and cover images which requires the implementation of residual front-ends (SRM/HPF) and ROC/AUC threshold calibration with confidence intervals and larger datasets and specialized steganalysis architectures (SRNet or Yedroudj-Net).


\begin{table}[ht]
\centering
\begin{tabular}{lcc}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
\hline
BPP\_0.1\_ResNet50\_CNN_GA  & 50.00\% & 0.5375 \\
BPP\_0.2\_ResNet50\_CNN_GA  & 50.00\% & 0.5500 \\
BPP\_0.4\_ResNet50\_CNN_GA  & 50.00\% & 0.5500 \\
BPP\_0.1\_ResNet50\_GAN_GA  & 50.00\% & 0.5375 \\
BPP\_0.2\_ResNet50\_GAN_GA  & 50.00\% & 0.5450 \\
BPP\_0.4\_ResNet50\_GAN _GA & 50.00\% & 0.5450 \\
\hline
\end{tabular}
\caption{Xu-Net Results Using ResNet50}
\end{table}

The table named \textit{Xu-Net Results Using VGG16} demonstrates that Xu-Net with VGG16 backbone achieves random performance when using either a plain CNN head or a GAN component for all payload sizes. The Accuracy remains constant at 50.00\% in all configurations while the ROC AUC values reach only slightly above random performance at 0.5325 for BPP=0.1 and 0.5175 for BPP=0.2 and BPP=0.4. The AUC values between the CNN and GAN variants show no significant difference because the small variations could stem from normal statistical fluctuations rather than actual performance improvements. The observed 50\% accuracy with AUC values between 0.52 and 0.53 indicates that this VGG16-based Xu-Net configuration does not use embedding residuals effectively to detect stego images from cover images under the present preprocessing and training conditions and architectural setup.
\begin{table}[ht]
\centering
\begin{tabular}{lcc}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
\hline
BPP\_0.1\_VGG16\_CNN_GA  & 50.00\% & 0.5325 \\
BPP\_0.2\_VGG16\_CNN_GA  & 50.00\% & 0.5175 \\
BPP\_0.4\_VGG16\_CNN_GA  & 50.00\% & 0.5175 \\
BPP\_0.1\_VGG16\_GAN_GA  & 50.00\% & 0.5325 \\
BPP\_0.2\_VGG16\_GAN_GA  & 50.00\% & 0.5175 \\
BPP\_0.4\_VGG16\_GAN_GA  & 50.00\% & 0.5175 \\
\hline
\end{tabular}
\caption{Xu-Net Results Using VGG16  }
\end{table}


\subsubsection{Security Analysis Using Ye-Net,VGG16 and CNN }

In this subsection, the Ye-Net detector is employed to evaluate the performance of the VGG16+CNN embedding framework, which combines deep texture feature extraction with adaptive embedding through convolutional layers. The VGG16 network captures fine spatial and structural details, while the CNN-based encoder ensures controlled data distribution within complex image textures. The purpose of this analysis is to investigate whether Ye-Net can effectively distinguish between cover and stego images across various payload capacities (BPP=0.1, 0.2, 0.4). By examining the resulting confusion matrices and metrics such as accuracy, TPR, TNR, and balanced accuracy, this evaluation provides insights into the detectability, statistical indistinguishability, and overall security robustness of the VGG16+CNN–based embedding method under deep-learning steganalysis.
The confusion matrices for Ye-Net (VGG16+CNN) appear in Figure~\ref{fig:xu_hed_cnn_conf_all111} at BPP values of 0.1, 0.2 and 0.4. The classifier produces identical results for all inputs by assigning every sample to the cover class which results in TN=20 and FP=0 and FN=20 and TP=0. The model achieves 50\% accuracy with 0\% true positive rate and 100\% true negative rate and 50\% balanced accuracy which indicates chance-level discrimination with expected AUC of 0.5 and EER of 50\%. The detector fails to detect any changes in the payload which indicates that the embedding method provides resistance against this particular detector on this specific dataset.
%but additional tests with different detectors and datasets are needed to confirm security.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (VGG16+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all111}
\end{figure}

The ROC curves for Ye-Net (VGG16+CNN) at different BPP values from 0.1 to 0.4 appear in Figure~\ref{fig:xu_hed_cnn_conf_all101}. The ROC curves show no improvement from the diagonal line because AUC equals 0.5000 at 0.1 and AUC equals 0.5088 at 0.2 and AUC equals 0.4837 at 0.4. The results show that the system performs at chance level with equal error rates of approximately 50\% because it lacks an operating point that achieves high true positives with low false positives. The detection performance does not enhance when the payload size increases under this specific configuration. The embedding shows resistance to this particular detectorز
%based on this dataset yet this result does not provide universal security assurance because it needs additional testing with robust detectors and separate datasets.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16CNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (VGG16+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all101}
\end{figure}

\subsubsection{Security Analysis Using Ye-Net,VGG16 and GAN }

In this subsection, the performance of the Ye-Net detector is analyzed against stego images generated using the VGG16+GAN embedding framework. In this configuration, VGG16 extracts fine-grained texture and structural patterns, while the GAN component produces a more natural and statistically consistent embedding distribution across textured regions. A balanced test set and multiple payload rates (BPP=0.1,0.2,0.4) are used to evaluate the detector’s sensitivity to changes in embedding capacity. Key performance indicators—such as accuracy, TPR, TNR, and balanced accuracy—are employed to measure the discrimination ability of the detector. This introduction sets the analytical context for interpreting the results, clarifying whether the Ye-Net vs. VGG16+GAN configuration provides meaningful detection capability or merely performs at chance level, and what these findings imply for its operational threshold and overall ROC/AUC performance.
The confusion matrices for Ye-Net (VGG16+GAN) appear in Figure~\ref{fig:xu_hed_cnn_conf_all111} at BPP values of 0.1, 0.2 and 0.4.
The model produces a complete misclassification of all samples by identifying them as cover images which results in TN=20 and FP=0 and FN=20 and TP=0.The model achieves 50\% accuracy with 0\% true positive rate and 100\% true negative rate and 50\% balanced accuracy which shows random discrimination with no useful threshold for operation.
The results show no change in detection performance when BPP increases because of this particular configuration.The specific detector fails to detect this steganographic embedding in this dataset.

%but this result does not prove overall security because it needs additional testing with different detectors and separate datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_Conv_0.4.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_Conv_0.2.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (VGG16+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all111}
\end{figure}

The ROC curves for Ye-Net (VGG16+GAN) at BPP=0.1, 0.2, 0.4 appear in Figure~\ref{fig:xu_hed_cnn_conf_all1011}. The curves show no deviation from the diagonal line because AUC equals 0.5000 at 0.1 and AUC equals 0.5088 at 0.2 and 0.4. The near-chance values indicate that the system maintains a 50\% error rate while failing to find any operating point that would produce high true positives with low false positives. The minimal AUC increase when BPP rises indicates that this configuration does not enhance detection capabilities in real-world scenarios. The embedding shows resistance to this particular detector on this dataset.
%but this finding does not provide universal security assurance because it needs verification through tests on different detectors and independent data sets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_ROC_0.4.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_VGG16GAN_ROC_0.2.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (VGG16+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1011}
\end{figure}

\subsubsection{Security Analysis Using Ye-Net,ResNet50 and CNN }
In this subsection, the Ye-Net detector is employed to assess the detectability of stego images generated using the ResNet50+CNN embedding framework. The ResNet50 network provides deep residual texture features that enhance the embedding’s spatial consistency, while the CNN component performs adaptive encoding to preserve visual fidelity. The primary objective of this analysis is to determine whether Ye-Net can successfully distinguish between cover and stego images at various payload capacities (BPP=0.1,0.2,0.4). By examining the confusion matrices and performance indicators such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy, this evaluation explores the detectability, discrimination capability, and resilience of the ResNet50+CNN–based steganographic model against a deep convolutional detector.
The confusion matrices for Ye-Net (ResNet+CNN) appear in Figure~\ref{fig:xu_hed_cnn_conf_all112}at BPP values of 0.1, 0.2 and 0.4.The classifier produces a 100\% false negative rate by identifying all images as cover images which results in TN=20 and FP=0 and FN=20 and TP=0.The results show 50\% accuracy with TPR=0.0 and TNR=1.0 and balanced accuracy=0.50 which indicates chance-level discrimination without any useful operating threshold.The results show no variation between different payload sizes because BPP increases does not enhance detectability in this particular setup.The embedding shows resistance to this particular detector when applied to this dataset.
%but this result does not provide universal security protection because it needs additional testing with different detectors and independent datasets.

\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (ResNet50+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all112}
\end{figure}

The ROC curves for Ye--Net (ResNet50+CNN) appear in Figure~\ref{fig:xu_hed_cnn_conf_all1012} when BPP equals 0.1, 0.2 and 0.4.
The ROC curve shows no discrimination ability because it runs along the diagonal line with AUC equal to 0.5000 and an equal error rate of 50\%.The detector fails to find any operating point which would achieve both high true positive rates and low false positive rates.The results show that BPP variations do not enhance detection performance under this specific configuration.The embedding shows resistance to this particular detector when applied to this dataset.
%yet this result does not provide universal security protection because it needs additional tests with different detectors and separate datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (ReNet50+CNN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1012}
\end{figure}


\subsubsection{Security Analysis Using Ye-Net,ResNet50 and GAN }

In this subsection, the Ye-Net detector is used to analyze the detection performance of stego images generated through the ResNet50+GAN embedding framework. The ResNet50 module provides deep residual texture representations that guide the embedding process, while the GAN component introduces adversarial learning to generate visually realistic and statistically consistent stego images. The objective of this evaluation is to determine whether Ye-Net can effectively discriminate between cover and stego images at various embedding capacities (BPP=0.1,0.2,0.4). By examining the resulting confusion matrices and performance metrics—such as accuracy, true positive rate (TPR), true negative rate (TNR), and balanced accuracy—this analysis provides insights into the resilience, indistinguishability, and detection limitations of the ResNet50+GAN–based steganographic model under deep learning–based steganalysis.
The confusion matrices for Ye-Net (ResNet+GAN) appear in Figure~\ref{fig:xu_hed_cnn_conf_all1112} at BPP values of 0.1, 0.2 and 0.4.
The model shows complete degeneration by predicting all images as cover at every payload level which results in TN=20 and FP=0 and FN=20 and TP=0.The model achieves 50\% overall accuracy with 0.0 TPR and 1.0 TNR and 0.50 balanced accuracy which represents chance-level discrimination and no useful operating threshold.The results show no variation in detection performance when BPP increases under this configuration.
The embedding shows resistance to this particular detector when applied to this dataset. %although this result does not ensure overall security because it needs verification with more robust detectors and separate testing datasets.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetGAN_Conv_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetGAN_Conv_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_Conv_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net Confusion (ResNet+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1112}
\end{figure}

The ROC curves for Ye-Net (ResNet50+GAN) appear in Figure~\ref{fig:xu_hed_cnn_conf_all1014} at different BPP values of 0.1, 0.2 and 0.4.
The ROC curves maintain a position near the diagonal line for all payload values while achieving AUC scores of 0.51 at 0.1 and 0.505 at 0.2 and 0.500 at 0.4.
The detection results show a 50\% equal-error rate because the operating point fails to achieve both high true-positive detection and low false-positive detection rates.The detector shows no significant improvement in detectability when BPP increases under this configuration because the results remain almost identical.The embedding shows resistance to this particular detector when applied to this dataset. 
%but additional security assessments must be performed to confirm universal protection.
\begin{figure}[H]
  \centering
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetGAN_ROC_0.1.PNG}
    \caption{BPP = 0.1}
    \label{fig:xu_hed_cnn_conf_0_1}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetGAN_ROC_0.2.PNG}
    \caption{BPP = 0.2}
    \label{fig:xu_hed_cnn_conf_0_2}
  \end{subfigure}\hfill
  \begin{subfigure}{0.32\textwidth}
    \centering
    \includegraphics[width=\linewidth]{Figs/Textures_Ye_ResnetCNN_ROC_0.4.PNG}
    \caption{BPP = 0.4}
    \label{fig:xu_hed_cnn_conf_0_4}
  \end{subfigure}
  \caption{Ye-Net ROC (ResNet50+GAN) at different BPP.}
  \label{fig:xu_hed_cnn_conf_all1014}
\end{figure}

% Table 1: ResNet50 results

The evaluation results for ResNet50-based detectors appear in Table~\ref{tab:roc_resnet5050} which presents ROC performance results for three different payloads (BPP = 0.1, 0.2, 0.4) using two different heads (CNN and GAN). The accuracy remains constant at 50\% while AUC values remain at random guessing levels of 0.5000 for CNN head and 0.5050 to 0.5100 for the GAN head. The ROC curves follow the diagonal line because the AUC values show no improvement from random guessing at 0.5000 for CNN and 0.5050 to 0.5100 for GAN. The ROC curves show no reliable operating point that achieves both high true-positive and low false-positive rates because the equal-error rate approaches 50\%. The GAN variant shows minimal AUC differences which amount to less than 0.01 but these differences are statistically insignificant when considering standard test set sizes in this research. The detection performance remains unaffected by the amount of embedded data because the detectors fail to detect steganographic content regardless of the BPP value between 0.1 and 0.4. The ResNet50 detectors fail to detect a discriminative steganographic signal in this dataset according to the results but additional testing with different architectures and independent data sets is needed to confirm overall security.
\begin{table}[htbp]
\centering
\caption{ROC performance per dataset (ResNet50 variants)}
\label{tab:roc_resnet5050}
\begin{tabular}{lcc}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
\hline
BPP\_0.1\_ResNet50\_CNN_GA & 50.00\% & 0.5000 \\
BPP\_0.2\_ResNet50\_CNN_GA & 50.00\% & 0.5000 \\
BPP\_0.4\_ResNet50\_CNN_GA & 50.00\% & 0.5000 \\
BPP\_0.1\_ResNet50\_GAN_GA & 50.00\% & 0.5100 \\
BPP\_0.2\_ResNet50\_GAN_GA & 50.00\% & 0.5050 \\
BPP\_0.4\_ResNet50\_GAN_GA & 50.00\% & 0.5050 \\
\hline
\end{tabular}
\end{table}

% Table 2: VGG16 results
The results from Table~\ref{tab:roc_vgg1616} demonstrate ROC performance of VGG16-based detectors is presented for three different payloads at BPP levels of 0.1, 0.2, and 0.4 and two different heads (CNN and GAN). The AUC values remain at or near chance level while the accuracy remains constant at 50\% for all scenarios. The AUC values for the CNN head are 0.5000, 0.5088, and 0.4837 when BPP equals 0.1, 0.2, and 0.4 respectively. The AUC value of 0.4837 is less than 0.5 which indicates a reversed ranking because reversing the scores would produce an AUC value greater than 0.5. The GAN head produces AUC values of 0.5000, 0.5088, and 0.5088 which indicate non-discriminative behavior while being slightly above chance level. The results show ROC curves that follow the diagonal line and an equal-error rate of 50\% with no operational point that achieves both high true-positive and low false-positive rates. The small AUC differences (less than 0.0088 above 0.5) have no practical value because they would not reach statistical significance in typical test scenarios. The performance shows no significant change when the BPP value increases from 0.1 to 0.4 under this specific configuration. The embedding shows resistance to these particular VGG16 variants when tested on this datasetز
%but additional security assessments with different detectors and datasets and AUC confidence intervals are needed to establish general security claims.
\begin{table}[htbp]
\centering
\caption{ROC performance per dataset (VGG16 variants)}
\label{tab:roc_vgg1616}
\begin{tabular}{lcc}
\hline
\textbf{Dataset} & \textbf{Accuracy} & \textbf{AUC} \\
\hline
BPP\_0.1\_VGG16\_CNN_GA & 50.00\% & 0.5000 \\
BPP\_0.2\_VGG16\_CNN_GA & 50.00\% & 0.5088 \\
BPP\_0.4\_VGG16\_CNN_GA & 50.00\% & 0.4837 \\
BPP\_0.1\_VGG16\_GAN_GA & 50.00\% & 0.5000 \\
BPP\_0.2\_VGG16\_GAN_GA & 50.00\% & 0.5088 \\
BPP\_0.4\_VGG16\_GAN_GA & 50.00\% & 0.5088 \\
\hline
\end{tabular}
\end{table}

\subsection{Robustness Analysis Using Texture Maps}

Evaluating the robustness of a steganographic system is a fundamental step in determining its ability to preserve hidden information when subjected to various image distortions and attacks. In this subsection, the robustness of the texture map–based embedding framework, specifically the ResNet50–CNN hybrid configuration, is examined under common degradation scenarios such as JPEG compression, Gaussian noise, and Salt & Pepper noise. The purpose of this analysis is to assess whether the system can maintain a stable extraction performance and accurate recovery of the hidden secret despite the presence of external perturbations. By analyzing quantitative indicators such as the bit error rate (BER) and relative reconstruction error (Re) across different attack intensities, this section investigates the level of attack resistance, synchronization stability, and overall resilience of the texture-guided embedding and extraction modules within the proposed steganographic model.

The Mini embedding configuration of the ResNet50-CNN hybrid steganographic system underwent robustness testing through various common image distortions which Table~\ref{tab:mini-ber-re-5} demonstrates. The system shows no significant change in bit error rate (\textit{BER\_secret}) and relative reconstruction error (\textit{Re\_secret}) throughout all attack conditions. The system produces \(\mathrm{BER}=0.4966\) and \(\mathrm{Re}=0.7364\) when no attack is applied which matches the statistical pattern of random behavior (\(\mathrm{BER}\approx0.5\). The system maintains identical performance levels when subjected to JPEG compression at different quality settings (\(q=90\!-\!30\)) and when exposed to Gaussian noise with different standard deviations (\(\sigma=0.005\!-\!0.02\)) and Salt\&Pepper noise with varying amounts (\(0.005\!-\!0.02\)). The extraction stage produces random outputs that remain unchanged from the attack level and type because the extraction stage fails to extract meaningful secret information. The system shows no actual resistance to attacks because its extraction and recovery modules lack proper synchronization.
%The model needs to demonstrate perfect baseline recovery with \(\mathrm{BER}\ll0.5\) and low \(\mathrm{Re}\) values before performing tests for compression and noise resistance.
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using ResNet50 and CNN}
  \label{tab:mini-ber-re-5}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.496582 & 0.736363 \\
    Mini & JPEG       & q=90                   & 0.500671 & 0.750305 \\
    Mini & JPEG       & q=70                   & 0.502441 & 0.752016 \\
    Mini & JPEG       & q=50                   & 0.497467 & 0.746293 \\
    Mini & JPEG       & q=30                   & 0.498138 & 0.743772 \\
    Mini & Gaussian   & sigma=0.005            & 0.503082 & 0.763648 \\
    Mini & Gaussian   & sigma=0.01             & 0.501892 & 0.743975 \\
    Mini & Gaussian   & sigma=0.02             & 0.500336 & 0.752425 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.496765 & 0.736136 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.501709 & 0.731580 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.498474 & 0.745356 \\
    \bottomrule
  \end{tabular}
\end{table}


 The robustness assessment of the proposed ResNet50-GAN hybrid model appears in Table~\ref{tab:mini-ber-re-6} which demonstrates its performance under various distortion scenarios in the Mini embedding mode. The extraction performance shows no significant change in bit error rate (\textit{BER\_secret}) and relative reconstruction error (\textit{Re\_secret}) when the attack intensity varies. The baseline system without any attack generates \(\mathrm{BER}=0.4962\) and \(\mathrm{Re}=0.7453\) which stay within a small deviation range (\(\pm0.01\)) when subjected to JPEG compression at \(q=90\!-\!30\) and Gaussian noise with \(\sigma=0.005\!-\!0.02\) and Salt\&Pepper noise with amount \(=0.005\!-\!0.02\). The system shows no indication of robustness because the \(\mathrm{BER}\) values stay near 0.5 which indicates the recovered secret data maintain random statistical properties instead of accurate reconstruction. The system shows consistent results between different attacks because it fails to perform proper decoding and synchronization between embedding and extraction stages instead of demonstrating actual system resistance. 
 
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using ResNet50 and GAN}
  \label{tab:mini-ber-re-6}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.496185 & 0.745267 \\
    Mini & JPEG       & q=90                   & 0.495850 & 0.740568 \\
    Mini & JPEG       & q=70                   & 0.503662 & 0.745260 \\
    Mini & JPEG       & q=50                   & 0.499847 & 0.745397 \\
    Mini & JPEG       & q=30                   & 0.499176 & 0.734256 \\
    Mini & Gaussian   & sigma=0.005            & 0.501648 & 0.749499 \\
    Mini & Gaussian   & sigma=0.01             & 0.506653 & 0.747139 \\
    Mini & Gaussian   & sigma=0.02             & 0.502319 & 0.741347 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.496033 & 0.744975 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.507751 & 0.746075 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.501801 & 0.748125 \\
    \bottomrule
  \end{tabular}
\end{table}


 The Mini embedding mode of the hybrid VGG16--CNN steganographic model undergoes robustness assessment through various image distortion tests which Table~\ref{tab:mini-ber-re-7} demonstrates. The bit error rate (\textit{BER\_secret}) and relative reconstruction error (\textit{Re\_secret}) maintain a small range of values between 0.49 and 0.50 for BER and 0.74 and 0.75 for Re across all tested attacks. The baseline measurement without any attack shows \(\mathrm{BER}=0.4942\) and \(\mathrm{Re}=0.7493\) while the same results appear when using JPEG compression at \(q=90\!-\!30\) and when adding Gaussian noise with \(\sigma=0.005\!-\!0.02\) and Salt\&Pepper noise with amount \(=0.005\!-\!0.02\). The extraction process shows no signs of degradation because the metrics remain stable between all tested conditions. The system produces outputs that match random noise statistics because the BER values stay at 0.5 which indicates no actual robustness. The system operates in a desynchronized state between embedding and extraction phases which results in uniform non-informative reconstruction for all tested conditions.
 

\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using VGG16 and CNN}
  \label{tab:mini-ber-re-7}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.494232 & 0.749280 \\
    Mini & JPEG       & q=90                   & 0.501190 & 0.745536 \\
    Mini & JPEG       & q=70                   & 0.501617 & 0.749880 \\
    Mini & JPEG       & q=50                   & 0.500458 & 0.743316 \\
    Mini & JPEG       & q=30                   & 0.502716 & 0.737526 \\
    Mini & Gaussian   & sigma=0.005            & 0.500763 & 0.744185 \\
    Mini & Gaussian   & sigma=0.01             & 0.496490 & 0.737585 \\
    Mini & Gaussian   & sigma=0.02             & 0.494690 & 0.746854 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.494659 & 0.749983 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.494080 & 0.743057 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.493988 & 0.752851 \\
    \bottomrule
  \end{tabular}
\end{table}


 The Mini embedding configuration of the proposed VGG16-GAN hybrid steganographic framework undergoes robustness testing through multiple image distortion types as shown in Table~\ref{tab:mini-ber-re-8}. The bit error rate (\textit{BER\_secret}) and relative reconstruction error (\textit{Re\_secret}) show minimal changes between all tested scenarios because \(\mathrm{BER}\) stays between 0.49 and 0.51 and \(\mathrm{Re}\) stays between 0.73 and 0.75. The baseline test without any attack produced \(\mathrm{BER}=0.4942\) and \(\mathrm{Re}=0.7493\) while the system maintained these values when subjected to JPEG compression at \(q=90\!-\!30\) and Gaussian noise with \(\sigma=0.005\!-\!0.02\) and Salt\&Pepper noise with amount \(=0.005\!-\!0.02\). The system extraction performance shows no change when attack intensity varies. The BER values stay near 0.5 which results in random secret information recovery instead of accurate reconstruction because the embedding and extraction stages lose synchronization. The system shows identical metric values because it fails to extract information correctly from the input data. 
 %The system needs to achieve successful baseline recovery with \(\mathrm{BER}\ll0.5\) and low \(\mathrm{Re}\) values before performing attack-based evaluations to establish VGG16 encoder and GAN decoder alignment.
\begin{table}[h!]
  \centering
  \caption{Robustness Analysis Using VGG16 and GAN}
  \label{tab:mini-ber-re-8}
  \begin{tabular}{
    @{} l l l
    S[table-format=1.6]
    S[table-format=1.6] @{}
  }
    \toprule
    Mode & Attack & Param & {BER\_secret} & {Re\_secret} \\
    \midrule
    Mini & none       & -                      & 0.494232 & 0.749280 \\
    Mini & JPEG       & q=90                   & 0.511190 & 0.745536 \\
    Mini & JPEG       & q=70                   & 0.511617 & 0.749880 \\
    Mini & JPEG       & q=50                   & 0.510458 & 0.743316 \\
    Mini & JPEG       & q=30                   & 0.512716 & 0.737526 \\
    Mini & Gaussian   & sigma=0.005            & 0.510763 & 0.744185 \\
    Mini & Gaussian   & sigma=0.01             & 0.496490 & 0.737585 \\
    Mini & Gaussian   & sigma=0.02             & 0.494690 & 0.746854 \\
    Mini & SaltPepper & amount=0.005, svp=0.5  & 0.504659 & 0.749983 \\
    Mini & SaltPepper & amount=0.01,  svp=0.5  & 0.504080 & 0.743057 \\
    Mini & SaltPepper & amount=0.02,  svp=0.5  & 0.493988 & 0.752851 \\
    \bottomrule
  \end{tabular}
\end{table}

\section{Summary of Our Experiment Results}

\textcolor{blue}{In this section we will mention a summary of our work in term of comparing our methods and choose the best ones. Then we will test the generalization of our system an the security against different secret image sizes. 
Table~\ref{tab:edge_results_summary} presents a qualitative comparison of edge-based embedding strategies across multiple evaluation metrics, including PSNR, SSIM, IF, SSIMx, and MSE. The results clearly show that approaches combining \textit{RCF} edge features with \textit{Genetic Algorithm (GA)} optimization achieve the most consistent performance. In particular, both \textit{RCF\_GA\_CNN} and \textit{RCF\_GA\_WGAN} obtain positive outcomes (Y) across all evaluation metrics, indicating a strong balance between imperceptibility, structural similarity, and embedding accuracy.}


\textcolor{blue}{For the HED-based methods, the inclusion of GA optimization significantly improves performance. While \textit{HED\_Only\_CNN} and \textit{HED\_Only\_WGAN} fail across all metrics, their GA-optimized counterparts exhibit notable gains. Specifically, \textit{HED\_GA\_CNN} improves SSIM and MSE but does not achieve gains in PSNR, IF, or SSIMx, whereas \textit{HED\_GA\_WGAN} achieves positive results across all five metrics. This contrast highlights the complementary effect of adversarial learning when combined with GA optimization for HED-based edge guidance.Traditional edge-based methods show limited effectiveness without optimization, as both \textit{Traditional\_Only\_CNN} and \textit{Traditional\_Only\_WGAN} fail in all reported metrics. When GA optimization is applied, partial improvements are observed. \textit{Traditional\_GA\_CNN} shows gains in PSNR, IF, and MSE but remains weak in SSIM and SSIMx, while \textit{Traditional\_GA\_WGAN} further improves SSIM in addition to PSNR, IF, and MSE, though SSIMx remains negative. These results indicate that GA optimization enhances traditional methods but does not fully compensate for the lack of advanced edge representations.Overall, the table demonstrates that \textit{GA optimization is a critical component} for achieving reliable performance, and that \textit{RCF-based edge extraction} consistently outperforms both HED and traditional approaches across CNN and WGAN frameworks. Moreover, the superior performance of WGAN-based models in GA-optimized settings underscores the advantage of adversarial learning for preserving edge structures while minimizing embedding distortion.}




\begin{table}[htbp]
\centering
\caption{Edge Results Summary}
\label{tab:edge_results_summary}

\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}

\begin{tabular}{|l|c|c|c|c|c|}
\hline
\textbf{Method} & \textbf{PSNR} & \textbf{SSIM} & \textbf{IF} & \textbf{SSIMx} & \textbf{MSE} \\ \hline

HED\_GA\_CNN            & N & Y & N & N & Y \\ \hline
HED\_Only\_CNN          & N & N & N & N & N \\ \hline
RCF\_GA\_CNN            & Y & Y & Y & Y & Y \\ \hline
RCF\_Only\_CNN          & N & N & N & N & N \\ \hline
Traditional\_GA\_CNN    & Y & N & Y & N & Y \\ \hline
Traditional\_Only\_CNN  & N & N & N & N & N \\ \hline

HED\_GA\_WGAN           & Y & Y & Y & Y & Y \\ \hline
HED\_Only\_WGAN         & N & N & N & N & N \\ \hline
RCF\_GA\_WGAN           & Y & Y & Y & Y & Y \\ \hline
RCF\_Only\_WGAN         & N & N & N & N & Y \\ \hline
Traditional\_GA\_WGAN   & Y & Y & Y & N & Y \\ \hline
Traditional\_Only\_WGAN & N & N & N & N & N \\ \hline

\end{tabular}
\end{table}
\textcolor{blue}{Table~\ref{tab:texture_results_summary} presents a qualitative evaluation of texture-based embedding strategies using multiple performance metrics, including PSNR, SSIM, IF, SSIMx, and MSE. The results indicate that integrating \textit{Genetic Algorithm (GA)} optimization with deep texture feature extractors significantly enhances performance compared to non-optimized configurations. In particular, \textit{VGG16\_GA\_CNN} and \textit{VGG16\_GA\_WGAN} achieve positive outcomes across all evaluation metrics, demonstrating a strong balance between imperceptibility, structural similarity, and embedding fidelity. This confirms the effectiveness of VGG16-based texture representations when combined with adaptive GA-driven parameter optimization.ResNet50-based approaches also benefit from GA optimization, although with slightly less consistency. While \textit{ResNet50\_GA\_CNN} improves PSNR, SSIM, IF, and MSE, it fails to achieve gains in SSIMx. In contrast, \textit{ResNet50\_GA\_WGAN} achieves positive results across all five metrics, highlighting the advantage of adversarial learning in preserving texture characteristics and reducing embedding distortion. As expected, all non-optimized variants (\textit{ResNet50\_Only} and \textit{VGG16\_Only}) fail across all metrics, emphasizing the limitations of fixed, non-adaptive embedding strategies.}

\textcolor{blue}{Traditional texture-based methods exhibit similar trends. Without GA optimization, both CNN- and WGAN-based traditional approaches fail across all metrics. When GA optimization is applied, partial improvements are observed. Specifically, \textit{Traditional\_GA\_CNN} and \textit{Traditional\_GA\_WGAN} show gains in PSNR, SSIM, IF, and MSE, but consistently fail in SSIMx, indicating that although optimization improves overall fidelity, it does not fully preserve fine structural similarity compared to deep texture extractors.Overall, the results demonstrate that \textit{GA optimization is a crucial factor} for achieving reliable texture-guided embedding performance. Moreover, the superior and consistent performance of \textit{VGG16\_GA} configurations across both CNN and WGAN frameworks suggests that mid-level texture representations extracted by VGG16 are particularly well-suited for adaptive steganographic embedding. The findings further indicate that combining GA optimization with WGAN-based modeling provides additional robustness and perceptual quality improvements in texture-driven steganographic systems.}



\begin{table}[htbp]
\centering
\caption{Texture Results Summary}
\label{tab:texture_results_summary}

\setlength{\tabcolsep}{10pt}
\renewcommand{\arraystretch}{1.15}

\begin{tabular}{|l|c|c|c|c|c|}
\hline
\textbf{Method} & \textbf{PSNR} & \textbf{SSIM} & \textbf{IF} & \textbf{SSIMx} & \textbf{MSE} \\ \hline

ResNet50\_GA\_CNN           & Y & Y & Y & N & Y \\ \hline
ResNet50\_Only\_CNN         & N & N & N & N & N \\ \hline
VGG16\_GA\_CNN              & Y & Y & Y & Y & Y \\ \hline
VGG16\_Only\_CNN            & N & N & N & N & N \\ \hline
Traditional\_GA\_CNN        & Y & Y & Y & N & Y \\ \hline
Traditional\_Only\_CNN      & N & N & N & N & N \\ \hline

ResNet50\_GA\_WGAN          & Y & Y & Y & Y & Y \\ \hline
ResNet50\_Only\_WGAN        & N & N & N & N & N \\ \hline
VGG16\_GA\_WGAN             & Y & Y & Y & Y & Y \\ \hline
VGG16\_Only\_WGAN           & N & N & N & N & N \\ \hline
Traditional\_GA\_WGAN       & Y & Y & Y & N & Y \\ \hline
Traditional\_Only\_WGAN     & N & N & N & N & N \\ \hline

\end{tabular}

%\vspace{2mm}
%\textit{Note: VGG16\_GA and Traditional\_GA achieve the best overall performance in both CNN- and WGAN-based texture models.}
\end{table}



\subsection{Statistical Security Analysis Summary 
%\textcolor{red}{move this section after Statistical Capacity and Imperceptibility Analysis Summary}
}


\textcolor{blue}{In this subsection we choose the best models (RCF and VGG16) from our imperceptibility and capcity tests and then we test the security of these methods using different secret image size (128*128). Although the classification accuracy remains at 50\% across all configurations using secret image size (64*64), this does not indicate identical model behavior. Accuracy is computed at a fixed decision threshold (0.5), while AUC evaluates the ranking capability of the classifier across all possible thresholds. The observed variation in AUC values (0.517–0.533) suggests that Xu-Net captures weak discriminative cues between cover and stego images, although these cues are insufficient to shift predictions beyond the default threshold. This behavior is typical in steganalysis tasks under low embedding rates and indicates strong resistance to detection rather than model failure. Accuracy is computed at a single fixed decision threshold (typically 0.5), whereas AUC evaluates the classifier’s discrimination capability across all possible thresholds. Therefore, identical accuracy values may coexist with different AUC scores.}

{\color{blue}
It can be observed from security results that most of the reported accuracies are close to 50\%, with AUC values around 0.5. 
Since the steganalysis task is a balanced binary classification problem (cover versus stego), an accuracy of approximately 50\% corresponds to chance-level performance. 
This indicates that the Xu-Net classifier is unable to reliably distinguish between cover and stego images generated by the proposed method.
Such behavior reflects a high level of security, as the embedding strategy effectively suppresses detectable artifacts even at higher payloads (BPP = 0.2 and 0.4).
The reported AUC values further confirm this observation, as values close to 0.5 indicate random guessing rather than successful discrimination. Although the reported accuracy values remain close to 50\% in most cases, the corresponding AUC values exhibit slight variations. This behavior can be explained by the fundamental difference between the two metrics.
Accuracy is computed based on a fixed decision threshold and reflects the final classification outcome, which in a balanced binary steganalysis setting results in chance-level performance when the classifier fails to make confident decisions.
In contrast, the AUC metric evaluates the ranking ability of the classifier across all possible thresholds.
Therefore, even weak discriminative signals that are insufficient to alter the final classification accuracy may still lead to small variations in AUC values.
This observation indicates that while the Xu-Net classifier captures minor statistical differences, these differences are not strong enough to enable reliable detection, thus confirming the robustness of the proposed steganographic method.

}

\subsubsection{Statistical Security Analysis Summary}


\begin{figure}[H]
    \centering
    \includegraphics[width=0.45\textwidth]{V1.PNG}
    \caption{Confusion Matrix of XuNet classifier}
    \label{fig:confusionConfusion}
\end{figure}


Figure~\ref{fig:confusionConfusion} illustrates the confusion matrix obtained from the XuNet classifier. The confusion matrix can be expressed as:

\[
\text{Confusion Matrix} =
\begin{bmatrix}
20 & 0 \\
16 & 4
\end{bmatrix}
\]

In this matrix:
\begin{itemize}
    \item Rows represent the \textbf{true labels}
    \item Columns represent the \textbf{predicted labels}
    \item Class 0 corresponds to \emph{cover images}
    \item Class 1 corresponds to \emph{stego images}
\end{itemize}

Based on the confusion matrix, the classification outcomes are defined as follows:

\[
\begin{aligned}
\text{True Negatives (TN)} &= 20, \\
\text{False Positives (FP)} &= 0, \\
\text{False Negatives (FN)} &= 16, \\
\text{True Positives (TP)} &= 4.
\end{aligned}
\]

The total number of test samples is computed by:

\[
N = TP + TN + FP + FN
\]

\[
N = 4 + 20 + 0 + 16 = 40.
\]

Accuracy is defined as the ratio of correctly classified samples to the total number of samples:

\[
\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}.
\]

By substituting the obtained values:

\[
\text{Accuracy} = \frac{4 + 20}{40} = \frac{24}{40} = 0.6.
\]

\textbf{Final Result}

\[
\boxed{\text{Accuracy} = 60\%}
\]

{\color{blue}
It can be observed that the classifier achieves perfect performance in identifying cover images (TN = 20), while its capability to correctly detect stego images is limited (TP = 4). This imbalance results in a moderate overall accuracy of 60\%. Despite a high Area Under the Curve (AUC) value obtained from the ROC analysis, the fixed decision threshold biases the classifier toward the cover class, thereby reducing the detection rate of stego images. Figure~\ref{fig:confusion1} reports the confusion matrices of the XuNet classifier for three embedding payloads, namely $\mathrm{BPP}=0.1$, $0.2$, and $0.4$, using a secret image size of $128\times128$. Each matrix summarizes the classifier's performance in discriminating \textit{cover} images (class~0) from \textit{stego} images (class~1). At $\mathrm{BPP}=0.1$, the classifier attains an overall accuracy of approximately $55\%$, which is only marginally above chance level. The matrix indicates that all cover samples are correctly recognized (true negatives $=20$), whereas most stego samples are incorrectly labeled as cover (false negatives $=18$), with only a small portion detected as stego (true positives $=2$). This behavior suggests that, at low payloads, the embedding artifacts are highly subtle and remain difficult for XuNet to identify, reflecting strong resistance to deep-learning-based steganalysis. When the payload increases to $\mathrm{BPP}=0.2$, the overall accuracy rises to around $60\%$. While cover images continue to be classified correctly, the number of correctly detected stego samples increases (true positives $=4$) and the corresponding false negatives decrease ($=16$). This improvement implies that higher payloads introduce more detectable statistical traces, allowing XuNet to extract more discriminative features between cover and stego classes. A similar pattern is observed at $\mathrm{BPP}=0.4$, where the accuracy remains at approximately $60\%$ and the stego detection rate is comparable to the $\mathrm{BPP}=0.2$ case. This indicates that, although increasing payload generally elevates detectability, the proposed embedding strategy still limits XuNet's classification capability, preventing a substantial gain in stego detection even at relatively higher payloads.

}


\begin{figure}[h]
    \centering
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V11.PNG}
        Bpp=0.1
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V1.PNG}
        Bpp=0.2
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V1.PNG}
        Bpp=0.4
    \end{minipage}

    \caption{Confusion Matrices for RCF-CNN and XuNet Classifier using Secret Image Size 128*128}
    \label{fig:confusion1}
\end{figure}

{\color{blue}
Figure~\ref{fig:confusion2} illustrates the confusion matrices obtained using the YeNet steganalysis classifier for three different embedding payloads, namely $\mathrm{BPP}=0.1$, $0.2$, and $0.4$, with a secret image size of $128\times128$. The confusion matrices provide a detailed view of the classifier’s ability to distinguish between cover images (class~0) and stego images (class~1) under varying embedding rates. At a low payload of $\mathrm{BPP}=0.1$, YeNet achieves an overall classification accuracy of approximately $57.5\%$. All cover images are correctly classified (true negatives $=20$), whereas the majority of stego images are misclassified as cover images (false negatives $=17$), with only a small number of stego samples correctly detected (true positives $=3$). This result indicates that the embedding artifacts introduced at this payload are highly imperceptible, making stego images difficult to detect even for a deep-learning-based steganalyzer such as YeNet. When the payload increases to $\mathrm{BPP}=0.2$, the classification accuracy improves to approximately $60\%$. While cover images continue to be perfectly recognized, the number of correctly identified stego images increases to $4$, and the number of false negatives decreases to $16$. This trend suggests that increasing the embedding rate introduces more detectable distortions in the cover images, enabling the classifier to extract more discriminative features. At a higher payload of $\mathrm{BPP}=0.4$, YeNet attains its highest reported accuracy of approximately $70\%$. The confusion matrix shows a further improvement in stego image detection, with $8$ stego images correctly classified and $12$ misclassified as cover images. Although this represents a noticeable increase in detectability, a substantial proportion of stego samples still remain indistinguishable from cover images, highlighting the robustness of the proposed embedding scheme even at higher payloads.


}


\begin{figure}[h]
    \centering
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V57.PNG}
       Bpp=0.1
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V1.PNG}
        Bpp=0.2
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V5.PNG}
       Bpp=0.4
    \end{minipage}

    \caption{Confusion Matrices for RCF-CNN and YeNet Classifier using Secret Image Size 128*128}
    \label{fig:confusion2}
\end{figure}

{\color{blue}
Figure~\ref{fig:confusion3} presents the confusion matrices obtained using the XuNet steganalysis classifier when evaluating stego images generated by the proposed RCF-GAN-based embedding scheme. The analysis is conducted for three embedding payloads, namely $\mathrm{BPP}=0.1$, $0.2$, and $0.4$, with a secret image size of $128\times128$.At the lowest payload of $\mathrm{BPP}=0.1$, XuNet achieves an overall classification accuracy of approximately $55\%$. The confusion matrix indicates that all cover images are correctly classified (true negatives $=20$), while the vast majority of stego images are misclassified as cover images (false negatives $=18$), with only two stego samples correctly detected (true positives $=2$). This result highlights the high imperceptibility of the RCF-GAN embedding strategy at low payloads, making stego images nearly indistinguishable from cover images even for a deep-learning-based steganalyzer. When the payload increases to $\mathrm{BPP}=0.2$, the classification accuracy rises to approximately $60\%$. Although cover image classification remains perfect, the number of correctly detected stego images increases to $4$, and the number of false negatives decreases to $16$. This improvement suggests that increasing the payload introduces more detectable embedding artifacts; however, these artifacts remain relatively weak due to the adversarial nature of the RCF-GAN framework.At $\mathrm{BPP}=0.4$, XuNet reaches an accuracy of approximately $65\%$. The confusion matrix shows a further increase in stego detection, with $6$ stego images correctly classified and $14$ still misclassified as cover images. Despite this improvement, a substantial portion of stego samples continues to evade detection, indicating that the proposed RCF-GAN-based approach effectively suppresses discriminative traces even at higher embedding rates.


}

\begin{figure}[h]
    \centering
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V11.PNG}
       Bpp=0.1
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{v2.PNG}
        Bpp=0.2
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{v22.PNG}
        Bpp=0.4
    \end{minipage}

    \caption{Confusion Matrices for RCF-GAN and XuNet Classifier using Secret Image Size 128*128}
    \label{fig:confusion3}
\end{figure}

{\color{blue}
We have later considered the YeNet steganalysis classifier to evaluate stego images generated by the proposed RCF-GAN-based embedding method, and Figure~\ref{fig:confusion4} presents the obtained confusion matrices. The analysis is conducted for three embedding payloads, namely $\mathrm{BPP}=0.1$, $0.2$, and $0.4$, using a secret image size of $128\times128$. At $\mathrm{BPP}=0.1$, YeNet achieves an overall classification accuracy of approximately $60\%$. The confusion matrix shows that all cover images are correctly classified (true negatives $=20$), whereas a significant portion of stego images is misclassified as cover images (false negatives $=16$), with only $4$ stego samples correctly detected. This result indicates that, even at low payloads, the RCF-GAN embedding strategy effectively conceals stego artifacts, limiting YeNet’s detection capability. When the payload increases to $\mathrm{BPP}=0.2$, the overall accuracy remains at approximately $60\%$, with no notable change in the classification pattern. Cover images continue to be perfectly identified, while stego detection performance remains moderate (true positives $=4$, false negatives $=16$). This stability suggests that the adversarial training mechanism of RCF-GAN suppresses additional detectable traces despite the increase in payload. At a higher payload of $\mathrm{BPP}=0.4$, YeNet’s accuracy increases to approximately $65\%$. The confusion matrix reveals an improvement in stego image detection, with $6$ stego samples correctly classified and $14$ misclassified as cover images. Although higher payloads generally enhance detectability, a considerable number of stego images still evade detection, demonstrating the robustness of the proposed RCF-GAN approach against YeNet-based steganalysis.


}



\begin{figure}[h]
    \centering
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V2.PNG}
       Bpp=0.1
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{v2.PNG}
       Bpp=0.2
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{v22.PNG}
       Bpp=0.4)
    \end{minipage}

    \caption{Confusion Matrices for RCF-GAN and YeNet Classifier using Secret Image Size 128*128}
    \label{fig:confusion4}
\end{figure}

{\color{blue}
We have finally evaluated the stego images generated by the VGG16-CNN-based embedding approach using the XuNet steganalysis classifier, and Figure~\ref{fig:confusion5} illustrates the obtained confusion matrices. The analysis is conducted for three embedding payloads, namely $\mathrm{BPP}=0.1$, $0.2$, and $0.4$, with a fixed secret image size of $128\times128$. At $\mathrm{BPP}=0.1$, XuNet achieves an overall classification accuracy of approximately $60\%$. The confusion matrix indicates that all cover images are correctly classified (true negatives $=20$), while most stego images are misclassified as cover images (false negatives $=16$), with only $4$ stego samples correctly detected (true positives). This result suggests that, at low payloads, the embedding distortions introduced by the VGG16-CNN remain largely imperceptible to XuNet, providing a reasonable level of resistance against steganalysis. When the payload increases to $\mathrm{BPP}=0.2$, the overall accuracy improves slightly to approximately $62.5\%$. The number of correctly detected stego images increases to $5$, while false negatives decrease to $15$. This behavior indicates that increasing the embedding rate introduces additional statistical artifacts, which gradually enhance XuNet’s ability to discriminate between cover and stego images. At the highest payload of $\mathrm{BPP}=0.4$, XuNet attains an accuracy of approximately $65\%$. The confusion matrix shows a further improvement in stego detection, with $6$ stego samples correctly classified and $14$ misclassified as cover images. Although this reflects increased detectability at higher payloads, a substantial proportion of stego images still evade detection, highlighting the effectiveness of the VGG16-CNN-based embedding strategy in mitigating steganalysis cues.



}

\begin{figure}[h]
    \centering
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V2.PNG}
       Bpp=0.1
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{v3.PNG}
        Bpp=0.2
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{v22.PNG}
       Bpp=0.4
    \end{minipage}

    \caption{Confusion Matrices for VGG16-CNN and XuNet Classifier using Secret Image Size 128*128}
    \label{fig:confusion5}
\end{figure}

{\color{blue}
Figure~\ref{fig:confusion5} illustrates the confusion matrices obtained using the YeNet steganalysis classifier to evaluate stego images generated by the VGG16-CNN-based embedding approach. The analysis is conducted for three embedding payloads, namely $\mathrm{BPP}=0.1$, $0.2$, and $0.4$, using a secret image size of $128\times128$. At $\mathrm{BPP}=0.1$, YeNet achieves an overall classification accuracy of approximately $60\%$. The confusion matrix shows that all cover images are correctly classified (true negatives $=20$), while a large proportion of stego images are misclassified as cover images (false negatives $=16$), with only $4$ stego samples correctly detected. This indicates that, at low payloads, the VGG16-CNN-based embedding introduces highly subtle distortions that remain difficult for YeNet to detect. When the payload increases to $\mathrm{BPP}=0.2$, the classification accuracy rises to approximately $65\%$. The number of correctly detected stego images increases to $6$, while the number of false negatives decreases to $14$. This trend reflects the growing detectability of stego artifacts as the embedding rate increases, enabling YeNet to capture more discriminative features between cover and stego images. At the highest payload of $\mathrm{BPP}=0.4$, YeNet attains its maximum accuracy of approximately $75\%$. The confusion matrix reveals a balanced classification of stego images, with $10$ stego samples correctly detected and $10$ misclassified as cover images. This substantial improvement in detection performance indicates that, at higher payloads, the embedding artifacts introduced by the VGG16-CNN become more pronounced and increasingly detectable by YeNet.



}


\begin{figure}[h]
    \centering
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V2.PNG}
       Bpp=0.1
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{v22.PNG}
      Bpp=0.2
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V75.PNG}
      Bpp=0.4
    \end{minipage}

    \caption{Confusion Matrices for VGG16-CNN and YeNet Classifier using Secret Image Size 128*128}
    \label{fig:confusion5}
\end{figure}

{\color{blue}
Figure~\ref{fig:confusion11} presents the confusion matrices obtained using the XuNet steganalysis classifier to evaluate stego images generated by the VGG16-GAN-based embedding approach. The evaluation is performed for three embedding payloads, namely $\mathrm{BPP}=0.1$, $0.2$, and $0.4$, using a secret image size of $128\times128$. At the lowest payload of $\mathrm{BPP}=0.1$, XuNet achieves an overall classification accuracy of $50\%$, which corresponds to random guessing. The confusion matrix shows that $15$ cover images are correctly classified, while $5$ are misclassified as stego images. Similarly, only $5$ stego images are correctly detected, whereas $15$ stego samples are misclassified as cover images. This result indicates that the VGG16-GAN embedding strategy effectively conceals stego artifacts at low payloads, rendering XuNet unable to distinguish between cover and stego images. When the payload increases to $\mathrm{BPP}=0.2$, the classification accuracy improves to approximately $62.5\%$. All cover images are correctly identified (true negatives $=20$), while the number of correctly detected stego images increases to $5$, with $15$ stego samples still misclassified as cover images. This behavior suggests that increasing the embedding rate introduces more detectable traces, although a significant portion of stego images remains indistinguishable from cover images. At the highest payload of $\mathrm{BPP}=0.4$, XuNet attains an accuracy of approximately $65\%$. The confusion matrix indicates a further improvement in stego detection, with $6$ stego images correctly classified and $14$ misclassified as cover images. Despite this increase in detectability, the majority of stego images continue to evade detection, highlighting the robustness of the VGG16-GAN-based embedding approach even at higher payloads.



}



\begin{figure}[h]
    \centering
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V44.PNG}
       Bpp=0.1
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{v3.png}
        Bpp=0.2
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V22.PNG}
       Bpp=0.4
    \end{minipage}

    \caption{Confusion Matrices for VGG16-GAN and XuNet Classifier using Secret Image Size 128*128}
    \label{fig:confusion11}
\end{figure}

{\color{blue}
Figure~\ref{fig:confusion6} illustrates the confusion matrices obtained using the YeNet steganalysis classifier to evaluate stego images generated by the VGG16-GAN-based embedding approach. The analysis is conducted for three embedding payloads, namely $\mathrm{BPP}=0.1$, $0.2$, and $0.4$, using a secret image size of $128\times128$.At $\mathrm{BPP}=0.1$, YeNet achieves an overall classification accuracy of approximately $57.5\%$. The confusion matrix shows that all cover images are correctly classified (true negatives $=20$), whereas the majority of stego images are misclassified as cover images (false negatives $=17$), with only $3$ stego samples correctly detected. This result indicates that, at low payloads, the adversarial embedding strategy of VGG16-GAN effectively conceals stego artifacts, significantly limiting YeNet’s detection capability. When the payload increases to $\mathrm{BPP}=0.2$, the overall accuracy rises to approximately $60\%$. The confusion matrix reveals a slight improvement in stego detection, with $4$ stego images correctly classified and $16$ misclassified as cover images, while cover image classification remains perfect. This trend suggests that increasing the embedding rate introduces additional detectable traces; however, these traces remain relatively weak due to the adversarial training incorporated in the VGG16-GAN framework. At the highest payload of $\mathrm{BPP}=0.4$, YeNet attains an accuracy of approximately $65\%$. The number of correctly detected stego images increases to $6$, while $14$ stego samples are still misclassified as cover images. Although detectability increases with payload, a substantial proportion of stego images continues to evade detection, highlighting the robustness of the VGG16-GAN-based embedding method even at higher embedding rates.



}



\begin{figure}[h]
    \centering
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V57.PNG}
        Bpp=0.1
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{v1.png}
       Bpp=0.2
    \end{minipage}
    \hfill
    \begin{minipage}{0.32\textwidth}
        \centering
        \includegraphics[width=\linewidth]{V22.PNG}
      Bpp=0.4
    \end{minipage}

    \caption{Confusion Matrices for VGG16-GAN and YeNet Classifier using Secret Image Size 128*128}
    \label{fig:confusion6}
\end{figure}

{\color{blue}
% في الـ preamble:
% \usepackage{xcolor}

\textcolor{blue}{
In this section we choose the best algorithms that give the highest imperceptibility and capacity.So,Table~\ref{tab:mean_std_methods} reports the average accuracy at a resolution of $128 \times 128$ together with the corresponding standard deviation for different combinations of feature extractors and steganalysis networks. The results reveal clear performance differences across the evaluated methods, highlighting the influence of both the backbone architecture and the adopted learning paradigm.

Among all configurations, \textit{VGG16-CNN-YeNet} achieves the highest average accuracy (66.6\%), indicating strong discriminative capability for the considered steganalysis task. Nevertheless, this performance gain is accompanied by a relatively high standard deviation (7.6\%), suggesting higher sensitivity to data variability and reduced stability across runs.

In contrast, \textit{RCF-CNN-XuNet} and \textit{RCF-GAN-YeNet} yield lower average accuracies (58.3\% and 61.6\%, respectively) while maintaining comparatively low standard deviations (2.8\%), which reflects more consistent and reliable behavior. This stability may be attributed to the robustness of the learned representations under these configurations.

When comparing CNN-based and GAN-based strategies, CNN-based methods generally achieve better average accuracy than their GAN-based counterparts. For example, \textit{VGG16-CNN-XuNet} and \textit{VGG16-CNN-YeNet} outperform \textit{VGG16-GAN-XuNet} and \textit{VGG16-GAN-YeNet}, indicating that direct convolutional learning is more effective than adversarial generation in the current experimental setting.


}

\begin{table}[h]
\centering
\caption{Average Accuracy and Standard Deviation for Different Methods}
\label{tab:mean_std_methods}
\begin{tabular}{lcc}
\hline
\textbf{Method} & 
\textbf{Avg at Sec (128$\times$128)} & 
\textbf{Std (\%)} \\
\hline
RCF-CNN-XuNet   & 58.3 & 2.8 \\
RCF-CNN-YeNet   & 62.3 & 8.6 \\
RCF-GAN-XuNet   & 60.0 & 5.0 \\
RCF-GAN-YeNet   & 61.6 & 2.8 \\
VGG16-CNN-XuNet & 62.3 & 2.5 \\
VGG16-CNN-YeNet & 66.6 & 7.6 \\
VGG16-GAN-XuNet & 59.1 & 7.9 \\
VGG16-GAN-YeNet & 60.6 & 4.0 \\
\hline
\end{tabular}
\end{table}
In the previous sections we calculate the security of our system against seccret image size (64*64), but in this section we used (128*128) secret image size so we find that a variation of results instead of fixed values (50\%) because we tested a biger secret image size so that security will be affected and the values of thhe stegnalizer will be increased.


\subsection{Comparison with Previous Studies }

{\color{blue}
It should be noted that the results of the compared methods were reported from their respective publications, where different test sets or data splits may have been used.
Therefore, the comparison is indicative rather than strictly fair. Table~\ref{tab:Comparison between our work and Related works based on USC-SIPI Dataset} provides a comprehensive comparison between the proposed method and several recent state-of-the-art steganographic approaches evaluated on the BOSSbase dataset, with respect to imperceptibility, embedding capacity, security against steganalysis, and robustness. In terms of imperceptibility, earlier works primarily report PSNR values ranging from 35 to 55 dB, with SSIM occasionally exceeding 0.99. While these results indicate acceptable visual quality, they remain substantially lower than the proposed method, which achieves an MSE as low as $1.5\times10^{-4}$, a PSNR of 61.39 dB, and a perfect SSIM value of 1.0, reflecting near-lossless visual fidelity. Regarding embedding capacity, most existing approaches operate within relatively limited payload ranges, typically between 0.01 and 0.5 bpnzAC, or fixed BPP intervals such as 0.1--0.4. In contrast, the proposed framework supports a significantly wider payload range, from 0.096 up to 0.989 BPP, while maintaining high imperceptibility. This demonstrates the effectiveness of the adaptive embedding strategy in exploiting image content more efficiently without introducing noticeable distortion.

}

{\color{blue}
From a security perspective, prior works mainly evaluate resistance using feature-based steganalyzers such as DCTR, GFR, and in some cases, deep-learning-based detectors including SRNet and Ye-Net. Although these methods report secure behavior against specific detectors, the proposed approach is validated against modern deep steganalysis models, namely Xu-Net and Ye-Net, where it consistently achieves secure (near-chance) detection performance. This highlights the robustness of the proposed design against advanced deep-learning-based attacks. Finally, robustness analysis in existing studies is often limited to error rates under JPEG compression at selected quality factors, whereas several recent GAN-based methods omit robustness evaluation altogether. While robustness under compression is not the primary focus of the proposed framework, its superior imperceptibility and security metrics indicate strong potential for resilience in practical scenarios. Overall, the comparison clearly demonstrates that the proposed method advances the state of the art by jointly achieving superior imperceptibility, substantially higher capacity, and strong security against modern steganalysis techniques.

}


\begin{table*}[!htbp]   
\caption{Comparison between our work and Related works based on USC-SIPI Dataset}
\label{tab:Comparison between our work and Related works based on USC-SIPI Dataset}
\fontsize{9pt}{9pt}\selectfont
\begin{tabular}{|p{1.5cm}|p{2.5cm}|p{2.25cm}|p{2cm}|p{4cm}|p{3cm}|}
\hline
      %  \multirow{2}{*}{Header 1} & \multicolumn{2}{c|}{Header 2} \\
\cline{2-3}
\bf Paper & \bf Dataset & \bf Impercep & \bf Capacity & \bf Security & \bf Robustness \\
\hline
\cite{rustad2022optimization} & BOSSbase & PSNR>35 & 0.05-0.5 bpnzAC & DCTR=secure, GFR=secure \newline &Error rate from JPEG compression Q 75,Q95  \\
%& & & & & & \\
\hline
\cite{zhang2021image} & BOSSbase & PSNR>50 & 0.01-0.1 bpnzAC & DCTR=secure,\newline GFR=secure& Error rate from JPEG compression Q 65,Q75,Q 85 \\
%& & & & & & \\
\hline
\cite{lu2020secure} & BOSSbase & PSNR>52 & 0.05-0.3 bpnzAC & DCTR=secure,\newline GFR=secure& Error rate from JPEG compression Q 75,Q85,Q 90  \\
%& & & & & & \\
\hline
\cite{zeng2022improving} & BOSSbase & NA & 0.1-0.5 bpnzAC & DCTR=secure, \newline SR net=secure & Error rate from JPEG compression Q 70  \\
%& & & & & & \\
\hline
\cite{wang2024gan}&BOSSbase&NA&NA&Achieved the lowest detection accuracy across five steganalyzers (SRM, Deng-Net, SRNet, Yedroudj-Net, Ye-Net), improving security by up to 5.39\% over prior methods&NA\\
\hline
\cite{yuan2022gan}& BOSSbase & PSNR=50-55, SSIM=0.9991 & 655,144 pixels,\newline  BPP=0.1-0.4 & XU-Net=NA, \newline Ye-Net=NA,\newline RS=NA& NA \\
\hline
\cite{chahar2025explainable}& BOSSbase &  PSNR=41.2, SSIM=0.9821 & BPP=0.1-0.4 & SR-Net=Secure, \newline Ye-Net=NA,\newline RS=NA& NA \\
\hline
Proposed Work & BOSSbase & MSE_Max=0.00015,\newline  PSNR=61.39, SSIM=1.0,\newline  IF=0.999998 & 262,144 pixels,\newline  BPP=0.096-0.989 & XU-Net=Secure, \newline Ye-Net=Secure& NA \\
%& & & & & & \\
\hline
\end{tabular}
\end{table*}
\section{CBSS Experiments and Results}
We conducted experiments with our CBSS proposed strategy, and this section discusses the related findings of it in enhancing the security and reliability as a CBSS scheme. The experiment starts with evaluating the outcome of the statistical Monobit test on a set of sample secret keys generated by RNG (as discussed before). But, our experiment finally targets to assess the security levels of the M-blocks models (i.e. 8-bits Block, 16-bits  Block, and 32-bits Block) in share generation and also to determine the best model that will produce Sh$_x$ which are not correlated among themselves and also not similar to the S$_K$. 

\subsection{Selecting Reliable Secret Key based on Monobit Frequency Test}


We observed the results of the statistical Monobit test performed on S$_K$ to assess its reliability as a secret key to proceed further in the process of secret share generation. In this analysis, we measured the ratios of one and zero %based on statistical tests. Therefore, the analysis of S$_K$ in this study has been based 
on random samples of S$_K$ (where a series of pseudo-random bits are produced %Random Function implemented in our key generation protocol that produces 
by RNG for the S$_K$). The pseudo-random samples of S$_K$ have been tested with to investigate their quality of randomness and an example scenario is shown in  Table ~\ref{tab:Monobit test for some random secret key generation}. The test calculates the P-value of each sample S$_K$ to determine if the sequence for S$_K$ is properly random or not.  The random S$_K$ listed as Key1 - Key20 (i.e. S$_{K1}$ to S$_{K20}$) and the corresponding P values have been displayed in . In this example, SK$_{8}$, SK$_{9}$, SK$_{11}$, SK$_{15}$ SK$_{20}$ are not capable of producing reliable randomness whereas the remainings are the good candidate to be considered as S$_K$. %which are ranked SK11, SK16, SK8, SK9, and SK21 have been generated by the same python Random Function and are considered as non-random SKs.

\begin{table*}[h]
\centering
\caption{Monobit test for some random secret key generation}
\label{tab:Monobit test for some random secret key generation}
\begin{tabular}{|p{2cm}|p{5cm}|p{2cm}|p{3cm}|}
\hline
\textbf{Key} & \textbf{Hexadecimal} & \textbf{P-value} & \textbf{Mono-bit Test} \\
\hline
Key1 & d8be859692baec2 & 0.80259 & Random \\
\hline
Key2 & 6da2e525d47d6698 & 0.80259 & Random \\
\hline
Key3 & 9c90b286008a7df3 & 0.31731 & Random \\
\hline
Key4 & 192654aeb6821a5a & 0.31731 & Random \\
\hline
Key5 & 7733340d68a61551 & 0.45325 & Random \\
\hline
Key6 & f10c61fb10ada7e1 & 1.00000 & Random \\
\hline
Key7 & 4d6902e8c1f0f4f2 & 0.61708 & Random \\
\hline
Key8 & 136efcf6d46bb366 & 0.13361 & Not Random \\
\hline
Key9 & adcbc8ffbb7fb205 & 0.04550 & Not Random \\
\hline
Key10 & e980bf69a9feddc & 0.31731 & Random \\
\hline
Key11 & 6ffadb3dc87915d9 & 0.08012 & Not Random \\
\hline
Key12 & fe0ed028443c5d08 & 0.21130 & Random \\
\hline
Key13 & dbb273757e6c4051 & 0.61708 & Random \\
\hline
Key14 & e091aae9a491292c & 0.21130 & Random \\
\hline
Key15 & 7767afe54fb2e0e7 & 0.04550 & Not Random \\
\hline
Key16 & 40b140fff14ced52 & 0.80259 & Random \\
\hline
Key17 & c022073b0b38e743 & 0.21130 & Random \\
\hline
Key18 & 749c45b24d85b154 & 0.45325 & Random \\
\hline
Key19 & c24a805fb33d03c0 & 0.21130 & Random \\
\hline
Key20 & ff3d5d78ee2e02a0 & 0.01242 & Not Random \\
\hline
\end{tabular}
\end{table*}

The ratio of ones and zeros in S$_K$ should be closer to one in a good random sequence. The Monobit frequency test focuses on the count of ones and zeros on the entire S$_K$ sequence. Therefore, this test computes the absolute value of the sum of ones and zeros within S$_K$ sequence, as S$_n$, where ones are represented as +1 and zeros as -1. Then the resulting absolute sum of ones and zeros in S$_K$ is divided by the square root of the size of S$_K$ sequence, n which is 64 bits (in our case). So, we get the test statistics according to Equation~\ref{eq:pvalue1}. %that count the summation of ones and zeros and take the absolute value, then divide the results by square root of 64.


\begin{equation}
S_{\text{obs}} = S_{n}\div \sqrt{n}
\label{eq:pvalue1}
\end{equation} 
The level of significance, $ \alpha$ is determined in the Monobit frequency test as 0.01. Equation~\ref{eq:pvalue} shows that the computation of P\text{-value} which is actually calculated through \texttt{erfc} (denoting the complementary error function) \cite{gutub2020secure}, considering the result of Equation~5.12 and a division of the square-root of 2.


%\begin{equation}
%\text{P-value} = \text{erfc} \left(\frac{S_{\text{obs}}}{\sqrt{2}}\right)
%\end{equation}

\begin{equation}
\text{P-value} = \text{erfc}\!\left(\frac{S_{\text{obs}}}{\sqrt{2}}\right)
\label{eq:pvalue}
\end{equation}


If the P-value is greater than or equal $\alpha$, then S$_K$ can be considered reliably random. Our test has been applied to the 20 samples of S$_K$, where the P-value has been calculated per S$_K$ based on the proportion of ones and zeros in the entire S$_K$ sequence. When the number of ones and zeros is close to equal, they will cancel each other. Thus the test statistics become almost 0, and the P-value becomes equal to one. That means that this S$_K$ is perfectly random based on this test and valid to use as the reliable S$_K$ in the CBSS. 
 

\begin{figure*}
  \centering
\includegraphics[width=0.9\textwidth]{Figs/ppp (1).JPG}
\caption{P-value Distribution of Randomness Applying Monotib Test }
\label{fig:example}
\end{figure*}


Generally,as shown in Figure~\ref{fig:example} if the P-value is greater than a threshold value, $\alpha$, then the S$_K$ is considered to have good randomness. In our experiment we have considered the alpha value as 0.8, and we have assumed that if the P-value is greater than or equal to  0.8 (i.e. when the number of zeros or ones within S$_K$ is either 31, 32, or 33),  then the random S$_K$ has a high level of reliability to be used. But if the P-value is less than 0.2, then the secret keys are of low reliability is not recommended for use. The  S$_K$ with  P-value between 0.8 and 0.2 provides a medium level of reliability and can be used too.

 \subsection{Selecting appropriate M-block model for Shares Generation}

The average similarity coefficient, V$_{sh}$ in each M-bits block model is calculated by averaging the similarity  rates for all Sh$_x$. This coefficient provides an indicator of mutual resemblance in a cryptographic or data-sharing system. This statistic is calculated by comparing the Sh$_x$ among each other, and then dividing the  counts of similarity match by the total number of Sh$_x$.  Again, the similarity rate, V$_{sk}$ determines the measure of how much S$_K$ digits are similar to the digits of Sh$_x$.
 
This comparison is useful to evaluate the concordance between S$_K$ bits (in binary) and Sh$_x$ bits to gain insight into the fidelity of data representation of the cryptographic framework. Both V$_{sh}$ and V$_{sk}$ are important metrics to be used when checking the  consistency and effectiveness of cryptographic method and data sharing mechanisms.

The performance comparison is made for all the considered models i.e. 8-bits Block, 16-bits  Block, and 32-bits Block. All of these models were operated on the same 64-bits S$_K$. The formula used to calculate the percentage frequency (F{\text{sh}) of each share independently as shown in Equation~\ref{fig:example2} that calculates the similarity between the Sh$_x$ bits themselves and the similarity between Sh$_x$ bits and S$_K$ bits. We note that if we want to calculate the similarity, S we need to consider the similarity occurrence between the shares and secret key, and also among the shares themselves. \\

%\hl{What is the meaning of F$_{sh}$ and why it is used?}
\begin{equation}
S = \sum F_{\text{sh}}
\label{fig:example2}
\end{equation}
In case of a random S$_K$= B2 35 6D 31 36 33 66 72, we calculated the  V$_{sh}$ and V$_{sk}$, and the results are shown in Table~\ref{tab:avg_similarity_comparison}. We evaluated the performance of our methods by comparing with Adel et al.'s work and, for that, we denoted $\Delta Vsk$ as Vsk performance in the work of Adel et al. and $\Delta Vsh$  as Vsh's performance in the work of Adel et al.\cite{gutub2020secure} 

\begin{table}[htbp]
\centering
\caption{Comparison of average similarity results between our work and Adel et al.'s work based on $S_K=\text{B2 35 6D 31 36 33 66 72}$ \cite{gutub2020secure}}
\label{tab:avg_similarity_comparison}
\begin{tabular}{|l|l|l|l|l|}
\hline
Model & Vsk & $\Delta Vsk$ & Vsh & $\Delta Vsh$ \\
\hline
8-bits Block  & 56\% & 28.75\% & 48\% & 34.38\% \\
              &      &         &      &         \\
\hline
16-bits Block & 48\% & 41.84\% & 32\% & 58.85\% \\
              &      &         &      &         \\
\hline
32-bits Block & 32\% & 62.13\% & 0\%  & 77.34\% \\
\hline
\end{tabular}
\end{table}

 
\subsubsection{Similarity in 8-bits block when  S$_K$ =B2 35 6D 31 36 33 66 72}
With the 8-bits Block model in our work, the similarity rate, V$_{sh}$ decreases below the average with a percentage of 48\%, while the average of similarity rate V$_{sk}$ between any share and S$_K$ is found to be higher approximately 56\%, as shown in Table~\ref{tab:avg_similarity_comparison}. While Adel et al.'s \cite{gutub2020secure}\ results show that the similarity rate, V$_{sh}$ becomes less than our work with a difference equal to 13.7\%. In term of the average of similarity rate, V$_{sk}$, the difference between our work and Adel et al.'s work reached to 27.3\% and it is a very high percentage. In the 8-bits  Block model, there is the lowest expected security as compared to all other M-bits block models as shown in Figure~\ref{fig:example22}.

\begin{figure*}[h]
  \centering
\includegraphics[width=0.9\textwidth,height=5cm]{Figs/Nana.JPG}
\caption{ Comparing between Our work and other work within 8 bits Blocks Models}
\label{fig:example22}
\end{figure*}



\subsubsection{Similarity in 16-bits  block when
S$_K$=B2 35 6D 31 36 33 66 72}

For the 16-bits  block model, we can see that it has better performance in comparison to the previous 8-bits Block model. The share similarity rate is also calculated and is seen to have reduced the rate V$_{sh}$ to 32\% and the average similarity rate V$_{sk}$ is 48\%. In this model, Adel et al.'s work outperforms our work in terms of V$_{sk}$, but we outperform it in terms of Vsh. In our scenario method we note that when the number of block decreased that mean each block has more bits to modify which mean the similarity decreases and the security increases as shown in Figure~\ref{fig:example11}. 
\begin{figure*}[htbp]
  \centering
\includegraphics[width=0.9\textwidth,height=5cm]{Figs/Nana2.JPG}
\caption{Comparing between Our work and other work within 16 bits-Blocks Models}
\label{fig:example11}
\end{figure*}


\subsubsection{Similarity in 32-bits  block when SK=B2 35 6D 31 36 33 66 72}

In this study, the most secure model is the 32-bits block model which provides the best results when compared to the rates of the previous models namely 8-bits  block and 16-bits block as shown in Table~\ref{tab:avg_similarity_comparison}. This 32 bits block model had the average of similarity rate V$_{sh}$ and V$_{sk}$ fixed as 32\%, and 0\% respectively. The model security level is therefore considered to be the highest of the two previous models. When we compare our work with Adel et al.'s work for this model, we note that Adel's work achieve high average similarity, reaching to 62.1\% and 77.3\%~\ref{fig:example22}.

\begin{figure*}[h]
  \centering
\includegraphics[width=0.9\textwidth,height=5cm]{Figs/Nana3.JPG}
\caption{Comparing between Our work and other work within 32 bits -Blocks}
\label{fig:example22}
\end{figure*}
In Figure~\ref{fig:example33}and Figure~\ref{fig:example44}, the average V$_{sk}$ values show an increasing trend as the number of bits in a block increases, which indicates a possible enhancement in the reliability or security of the S$_K$ verification process based on the size of the block.  Adel's work has higher average V$_{sk}$ values for different block sizes than our work, which implies that the verification of the S$_K$ may be less reliable in Adel et al.'s work. From our work, it can be noted that a rising trend of the average V$_{sh}$ values with the increasing number of blocks, and the performance of Adel's work is lower than our work in terms of the average V$_{sh}$ values which implies that their share verification mechanism is less efficient.
\begin{figure*}[h]
  \centering
\includegraphics[width=0.9\textwidth]{Figs/Adel1.JPG}
\caption{ Comparing between Our work and Adel et al. work within M-Blocks Models according to Vsh }
\label{fig:example33}
\end{figure*}

\begin{figure*}[h]
  \centering
\includegraphics[width=0.9\textwidth]{Figs/Adel2.JPG}
\caption{ Comparing between Our work and Adel et al. work within M-Blocks Models according to Vsk }
\label{fig:example44}
\end{figure*}

\subsubsection{Similarity within 8-bits block, 4-bits block, 2-bits Bblock for different key size }


Our work and Adel et al.’s works(as shown in table~\ref{tab:Comparison of average similarity results based on different secret keys.}) show very different trends for the averages values of V$_{sk}$ and V$_{sh}$. In our study, the average V$_{sk}$ and V$_{sh}$ values increase, as 56\%  (V$_{sk}$)  and 48\% (V$_{sh}$) for 8-bits blocks.  Adel's work, however, displays decreasing averages with larger block sizes, 38.2\%  (V$_{sk}$) and 31.7\% (V$_{sh}$) for 8-bits blocks.  These discrepancies highlight the potential methodological differences or data variations between the two studies. In this scenario we note that when the number of block decreased the security is increased which add additional benefit our our work over Adel's work.
 
\begin{table}[h]
\centering
\caption{Comparison of average similarity results based on different secret keys. }
\label{tab:Comparison of average similarity results based on different secret keys.}
\begin{tabular}{|l|l|l|l|}
\hline
Method & 32-bits Block &  16 bits  Block &  8-bits block \\
\hline
 Vsk, our work & 32\% & 48\% & 56\% \\
 &  &  &  \\
 \hline
 Vsh, our work & 0\% & 32\% & 48\% \\
 &  &  &  \\
 \hline
$\Delta Vsh$, Adel's work & 75.3\% & 57.2\% & 38.2\% \\
 &  &  &  \\
 \hline
$\Delta Vsk$, Adel's work & 61.9\% & 46.3\% & 31.7\% \\
\hline
\end{tabular}
\end{table}
\section{Threats to validity}
In the context of a counting based secret sharing algorithm where a secret key is divided into blocks and  then XORed with a sequence of ones based on the block size, validity considerations are very important.  These are: Conclusion validity to avoid making biased conclusions; Internal validity to check the dependability of the  key splitting and the XOR operation without jeopardizing the security of the system; Construct validity to check if  the results are consistent with theoretical principles of secure key sharing; and External validity to ensure that the findings  are applicable beyond the specific implementations. These aspects are crucial to demoralize the credibility of the  conclusion, to show that the causal relationships are correctly assigned, to align with theoretical constructs, and to extend  the applicability to other contexts in order to maintain the security and efficiency of the secret sharing  scheme.

In the context of a secret sharing algorithm, dividing a secret key into blocks and XORing them  with a sequence of ones is explained. Key validity concepts are illustrated by examples. Conclusion validity is good  if correct conclusions are drawn from comparing the reconstructed and original keys. Internal validity, new biases or errors are avoided during  the key reconstruction, which is consistent throughout the multiple trials. Construct validity is demonstrated by the relationship between  the security of the algorithm and the cryptographic principles of the secure key distribution. The external validity is shown  by applying the test of the algorithm in various settings and finding that it performs similarly every time. These  examples highlight the significance of conclusion, internal, construct, and external validity in sustaining the security and efficiency  of the secret sharing scheme in any environment.
{\color{blue}
The experimental evaluation was conducted on benchmark datasets such as BOSSbase 
and USC-SIPI. While these datasets provide diverse textures and edge characteristics, 
the generalization of the proposed framework to other domains (e.g., medical or 
domain-specific images) may require further validation.
The integration of deep learning models and genetic optimization introduces additional 
computational overhead. Although the framework is suitable for offline or batch processing,
real-time deployment may be limited without hardware acceleration or further optimization.
The proposed system was evaluated under controlled experimental conditions. 
Practical deployment may face constraints related to processing time, memory availability, 
and variability in image acquisition pipelines, which could affect robustness in real-world scenarios.

}