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\chapter{Conclusion}
\label{yyyyy}
This thesis presented a comprehensive and optimized framework that integrates adaptive attention-guided image steganography with a Counting-Based Secret Sharing (CBSS) mechanism to improve confidentiality, robustness, and data-loss resilience. The proposed models — ALIS (Attention-map and Keyed LSB-based Image Steganography) and CBSS — collectively address the challenges of imperceptibility, payload capacity, and secure key management in modern multimedia communication systems. Together, they form a scalable and multi-layered architecture for secure and intelligent data hiding in cloud and distributed environments.

The ALIS framework successfully integrates deep learning–based attention mechanisms (derived from HED, RCF, VGG16, and ResNet50) with keyed whitening, keyed permutation, and genetic optimization to achieve adaptive bit allocation and enhanced embedding efficiency. By generating edge-driven and texture-driven attention maps, the system dynamically identifies perceptually significant regions suitable for high-capacity embedding while maintaining high imperceptibility. Experimental evaluations using CNN and WGAN models confirmed superior performance in PSNR and SSIM under various embedding conditions, demonstrating strong robustness and resistance to steganalysis. Although the framework currently operates on grayscale images and relies on pre-trained models, its design establishes a robust foundation for future expansion toward color and video steganography and the incorporation of transformer-based attention mechanisms to further improve context awareness and real-time adaptability.

The CBSS mechanism reinforced the overall system reliability and security by dividing each 64-bit secret key into multiple shares, enabling reconstruction only when a predefined threshold number of valid shares is combined. This threshold-based sharing prevents single-point of failure and ensures that no single participant can recover the secret independently. The proposed block-masking approach — using XOR operations and randomized segmentation — significantly enhanced confidentiality and minimized computational cost, making it suitable for cloud-based multi-user access control and distributed cryptographic storage. The integration of CBSS within ALIS resulted in a group-oriented, resilient, and scalable data protection framework, ensuring high confidentiality and consistent reconstruction performance across multiple environments.




This dissertation makes several significant contributions to the field of secure and intelligent image steganography:
\begin{enumerate} [label=\alph*)]
    \item Development of the ALIS Framework:
A novel Attention-map and Keyed LSB-based Image Steganography (ALIS) framework was designed and implemented. The system integrates edge-based and texture-based attention maps (HED, RCF, VGG16, and ResNet50) with keyed whitening, keyed permutation, and genetic optimization to achieve adaptive bit allocation and maximize imperceptibility, payload capacity, and robustness.
    \item Integration of Deep Attention Prediction Models:
Two predictive deep learning models—CNN and WGAN-GP—were developed to generate accurate attention maps for unseen cover images, allowing ALIS to generalize effectively across datasets and embedding scenarios. These models guided adaptive embedding and significantly improved image quality metrics (PSNR, SSIM).
      \item Optimization through Genetic Algorithm (GA):
A GA-based optimization module was introduced to automatically determine optimal threshold and bit-allocation parameters, achieving a better trade-off between visual fidelity, embedding capacity, and computational efficiency.

      \item Design of the Counting-Based Secret Sharing (CBSS) Mechanism:
A lightweight, block-masking-based Counting-Based Secret Sharing method was developed to divide a 64-bit key into multiple shares. The reconstructed key can only be recovered when a predefined number of valid shares meet the threshold, thereby ensuring reliability and group-based confidentiality.

     \item Integration of ALIS and CBSS for Multi-Layered Security:
The research is the first to integrate adaptive image steganography with counting-based secret sharing, producing a multi-layered security framework that resists steganalysis, prevents single-point failure, and supports distributed secret reconstruction.

     \item Comprehensive Experimental Evaluation:
Extensive experiments on benchmark datasets (BOSSbase and USC-SIPI) demonstrated the system’s superiority in imperceptibility, capacity, and robustness. The combined ALIS-CBSS system achieved consistent PSNR and SSIM performance under varying BPP levels and attack conditions while maintaining low computational cost.
\end{enumerate}

This dissertation also addressed six research questions that guided the development and evaluation of the proposed steganographic and secret-sharing frameworks. 
\begin{itemize}  
    \item 
RQ1 - investigated whether edge-based and texture-based attention mechanisms could effectively guide adaptive LSB embedding. The results demonstrate that attention maps derived from HED, RCF, VGG16, and ResNet50 successfully prioritize perceptually tolerant regions, enabling higher imperceptibility and lower detectability compared to uniform or purely statistical embedding. 
\item RQ2 - examined whether keyed adaptive bit allocation improves the trade-off between payload capacity and visual quality. Experimental results across multiple datasets confirm that adaptive allocation—driven by attention strength and keyed permutations—supports higher BPP targets (0.1, 0.2, 0.4) while maintaining strong PSNR and SSIM values, outperforming traditional embedding baselines. 

\item RQ3 - evaluated the role of Genetic Algorithm optimization in fine-tuning thresholds and per-tier bit distributions. The GA consistently found superior embedding configurations, increasing PSNR and SSIM relative to fixed thresholds and improving the overall rate–distortion balance. 

\item RQ4 - assessed the resistance of the system to modern steganalysis detectors. Experiments with Xu-Net, Ye-Net, and deep CNN-based steganalysis models show significantly lower detection rates for the proposed method, validating the security benefits of attention-guided variability, whitening, and keyed permutations. 

\item RQ5 - compared the proposed framework with existing adaptive and deep learning–based steganography. The ALIS system achieved better imperceptibility, higher payload capacity, and lower detectability, confirming its advantages over LSB, PVD, and prior DL-based models.  

\item RQ6 - examined whether integrating Counting-Based Secret Sharing (CBSS) enhances data reliability. The experiments show that CBSS provides strong fault tolerance and threshold-based recovery, enabling secure multi-user access even under partial share loss. Together, these answers validate the effectiveness, security, and robustness of the proposed attention-guided steganographic framework and its integration with CBSS for practical, high-security applications.
\end{itemize}

















\section*{Future Works}
The research can be further expanded along several promising directions:
\begin{enumerate}[label=\alph*)]
    \item Extension to color and video steganography – applying the proposed adaptive embedding framework to RGB images and video sequences to enhance multimedia communication security.

    \item Integration of transformer-based attention mechanisms – leveraging transformer architectures to improve contextual understanding and attention precision.
    
    \item Adversarially trained steganalysis networks – employing GAN-based or adversarially trained detection models for more rigorous robustness evaluation.
    \item Cross-Dataset Generalization and Human-Perception Alignment:
Future studies can focus on improving the generalization of attention map prediction models across different datasets and visual domains. Enhancing the perceptual alignment between machine attention and human vision will reduce suboptimal bit allocation in sensitive image areas
    \item Enhanced Mini-Attention Map and Robustness:
Further work can refine the mini-attention map mechanism to retain finer spatial details without significantly increasing payload size. This improvement could help maintain high extraction accuracy even under heavy compression, noise, or transmission loss
\end{enumerate}


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\chapter{Appendex}
\label{yyyyy}
\subsection{Statistical Capacity and Imperceptibility Analysis Summary}

{\color{blue}
In this section we computed the mean, std, variance, and confidence interval for all used cover images (N=200). Table~\ref{tab:Summary (Across all covers) } provides a detailed statistical analysis of the ablation study at BPP = 0.1 across 200 cover images, reporting the mean, 95\% confidence interval (CI), standard deviation, and variance for each performance metric. The narrow confidence intervals observed for GA-enabled configurations indicate a high level of statistical reliability and robustness. In particular, both \textit{HED=True, GA=True} and \textit{HED=False, GA=True} exhibit extremely tight CI ranges for PSNR (width $< 0.02$ dB) and SSIM (width $< 6\times10^{-5}$), demonstrating that the achieved imperceptibility is not only high on average but also highly consistent across the entire dataset. In contrast, configurations without GA optimization show substantially wider confidence intervals and higher variance. For example, \textit{HED=True, GA=False} exhibits a PSNR confidence interval spanning more than 0.43 dB, with a variance exceeding 2.39, while \textit{HED=False, GA=False} still shows noticeable dispersion despite a slightly narrower CI. This increase in variance reflects sensitivity to image content and confirms that fixed embedding parameters lead to unstable performance across different covers. The consistent reduction in variance when GA is enabled highlights the role of adaptive optimization in stabilizing embedding behavior. A similar trend is observed for SSIM and IF metrics. GA-enabled settings maintain very low standard deviations (on the order of $10^{-4}$ for SSIM and $10^{-3}$ for IF), whereas non-GA configurations exhibit noticeably higher dispersion. These results suggest that GA optimization effectively minimizes structural fluctuations and preserves information fidelity uniformly across images. The MSE metric further supports this observation, as GA-enabled configurations achieve near-zero variance, indicating uniformly minimal distortion. From an extraction standpoint, the statistical results are particularly strong: SSIM$(\text{secret}, \text{extracted})$ remains exactly 1.0 with zero variance across all ablation settings.
}


{\color{blue}
This confirms perfect reconstruction consistency and indicates that the proposed embedding mechanism is deterministic and statistically stable at this payload rate. Regarding computational performance, the runtime metrics also exhibit tight confidence intervals and low variance, especially for non-GA configurations. Although GA optimization increases the mean embedding and extraction times, the relative variability remains low, indicating predictable and stable computational cost. Importantly, the added overhead does not introduce significant runtime variance, reinforcing the practicality of the proposed approach. Overall, the statistical evidence clearly demonstrates that GA optimization significantly reduces performance variance and narrows confidence intervals across all key metrics. This confirms that the observed gains in imperceptibility and fidelity are statistically meaningful and reproducible, rather than being driven by a small subset of favorable samples. At BPP = 0.1, the dominant contributor to statistical stability is GA optimization, while HED-based edge guidance provides only marginal additional benefit under low-payload conditions.
}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size=64*64.}
\label{tab:Summary (Across all covers) }
\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline
HED=True, GA=True & PSNR(cover,stego)      & 61.161764 & 61.153265 & 61.170262 & 0.060950 / 0.003715 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.999485  & 0.999454  & 0.999516  & 0.000223 / 0.000000 \\
HED=True, GA=True & IF(cover,stego)        & 0.997223  & 0.996710  & 0.997737  & 0.003683 / 0.000014 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.053807  & 0.051423  & 0.056190  & 0.017096 / 0.000292 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.034303  & 0.032885  & 0.035721  & 0.010168 / 0.000103 \\
\hline
HED=True, GA=False & PSNR(cover,stego)      & 56.081179 & 55.865390 & 56.296967 & 1.547552 / 2.394916 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.998776  & 0.998718  & 0.998834  & 0.000418 / 0.000000 \\
HED=True, GA=False & IF(cover,stego)        & 0.995142  & 0.994407  & 0.995877  & 0.005273 / 0.000028 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000003  & 0.000002  & 0.000003  & 0.000001 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.030118  & 0.028735  & 0.031501  & 0.009915 / 0.000098 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.021063  & 0.020190  & 0.021935  & 0.006259 / 0.000039 \\
\hline
HED=False, GA=True & PSNR(cover,stego)      & 61.172317 & 61.167702 & 61.176931 & 0.033095 / 0.001095 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
HED=False, GA=True & IF(cover,stego)        & 0.997225  & 0.996711  & 0.997739  & 0.003686 / 0.000014 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.049008  & 0.047028  & 0.050988  & 0.014199 / 0.000202 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.030426  & 0.029285  & 0.031567  & 0.008182 / 0.000067 \\
\hline
HED=False, GA=False & PSNR(cover,stego)      & 57.401460 & 57.359376 & 57.443545 & 0.301814 / 0.091092 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.998939  & 0.998880  & 0.998998  & 0.000425 / 0.000000 \\
HED=False, GA=False & IF(cover,stego)        & 0.995760  & 0.994993  & 0.996527  & 0.005497 / 0.000030 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.027252  & 0.026213  & 0.028291  & 0.007451 / 0.000056 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.018840  & 0.018219  & 0.019461  & 0.004452 / 0.000020 \\
\hline
\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:bpp02_summary_64x64} summarizes the ablation results at BPP = 0.2 across $N=200$ cover images (secret size $64\times64$), reporting the mean, 95\% confidence interval (CI), standard deviation, and variance for each metric. Compared to the lower payload setting (e.g., BPP=0.1), the overall imperceptibility decreases slightly, which is expected due to the increased embedding rate; nevertheless, the proposed system maintains high visual fidelity, particularly when GA optimization is enabled.From a statistical stability perspective, the GA-enabled configurations exhibit extremely tight confidence intervals and low dispersion across all perceptual metrics. For instance, both \textit{HED=True, GA=True} and \textit{HED=False, GA=True} achieve PSNR around 60.16~dB with CI widths below $\approx 0.02$~dB, indicating highly consistent performance across diverse cover images. Similarly, SSIM remains above 0.99935 with very narrow CI bounds and near-zero variance, reinforcing that the reported averages are representative of the overall dataset rather than being driven by a subset of favorable samples. The IF metric follows the same pattern, where GA-enabled settings maintain higher means (around 0.99688) with low standard deviation, supporting stable information fidelity across all covers.
}

{\color{blue}
In contrast, disabling GA results in a notable reduction in imperceptibility and a substantial increase in variability. Specifically, \textit{HED=True, GA=False} shows a PSNR mean near 54.97~dB with a much wider CI (spanning more than 0.43~dB) and a very large variance ($\approx 2.40$). This high dispersion indicates sensitivity to cover content and confirms that fixed parameter settings do not generalize uniformly across images. A similar increase in dispersion is observed in SSIM and IF, where the standard deviations are consistently larger in GA-disabled configurations, reflecting less predictable embedding quality.Importantly, secret recovery remains perfect in all ablation settings, where SSIM$(\text{secret}, \text{extracted})=1.0$ with zero variance. This demonstrates deterministic and fully reversible extraction at BPP=0.2, independent of the chosen guidance/optimization combination. Regarding runtime, GA introduces a consistent computational overhead: embedding time increases from approximately 0.034~s (GA=False) to 0.062~s (GA=True), and extraction time increases from approximately 0.023~s to 0.038~s. However, these costs remain low in absolute terms and exhibit tight CI bounds, suggesting stable and predictable computational behavior suitable for practical use.Overall, the statistical evidence confirms that GA optimization is the primary contributor to performance robustness at BPP=0.2, as it systematically reduces variance and narrows confidence intervals across key imperceptibility and fidelity metrics. Meanwhile, the marginal differences between HED-enabled and HED-disabled cases under GA indicate that, at this payload, optimization dominates the stability gains, while edge guidance has a secondary effect.
}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size=$64\times64$.}
\label{tab:bpp02_summary_64x64}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 60.155272 & 60.145982 & 60.164562 & 0.066624 / 0.004439 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.999352  & 0.999312  & 0.999391  & 0.000282 / 0.000000 \\
HED=True, GA=True & IF(cover,stego)        & 0.996882  & 0.996303  & 0.997460  & 0.004148 / 0.000017 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.062482  & 0.059788  & 0.065177  & 0.019324 / 0.000373 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.038764  & 0.037214  & 0.040314  & 0.011114 / 0.000124 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 54.973572 & 54.757643 & 55.189502 & 1.548567 / 2.398059 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.998408  & 0.998331  & 0.998486  & 0.000554 / 0.000000 \\
HED=True, GA=False & IF(cover,stego)        & 0.994480  & 0.993642  & 0.995317  & 0.006008 / 0.000036 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000004  & 0.000001 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.033672  & 0.032288  & 0.035056  & 0.009926 / 0.000099 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.022785  & 0.021953  & 0.023617  & 0.005967 / 0.000036 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 60.164466 & 60.160063 & 60.168870 & 0.031582 / 0.000997 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.999353  & 0.999313  & 0.999392  & 0.000283 / 0.000000 \\
HED=False, GA=True & IF(cover,stego)        & 0.996884  & 0.996305  & 0.997462  & 0.004149 / 0.000017 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.061766  & 0.059129  & 0.064404  & 0.018917 / 0.000358 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.037623  & 0.036170  & 0.039077  & 0.010426 / 0.000109 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 56.393780 & 56.351745 & 56.435815 & 0.301457 / 0.090877 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.998649  & 0.998572  & 0.998726  & 0.000551 / 0.000000 \\
HED=False, GA=False & IF(cover,stego)        & 0.995235  & 0.994370  & 0.996101  & 0.006205 / 0.000039 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.034573  & 0.033167  & 0.035978  & 0.010079 / 0.000102 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.023179  & 0.022305  & 0.024053  & 0.006268 / 0.000039 \\
\hline

\end{tabular}%
}
\end{table}

Table~\ref{tab:bpp04_summary_64x64} summarizes the performance of the proposed steganographic framework at a high embedding rate (BPP = 0.4) using a \(64 \times 64\) secret image across 200 cover images. The results clearly show that the Genetic Algorithm (GA) is the dominant factor in improving cover image quality and stability. When GA is enabled, the system achieves consistently high PSNR values of approximately 60~dB, SSIM values close to 0.99935, and MSE on the order of \(10^{-6}\), regardless of whether HED is used. In contrast, disabling GA leads to a noticeable degradation in visual quality and increased variability across images. The contribution of HED is secondary and becomes marginal when GA is active, although it provides modest improvement in the absence of GA. All configurations achieve perfect secret recovery with \(\mathrm{SSIM} = 1.0000\), confirming lossless extraction even at high payloads. Although GA increases embedding and extraction times, the additional computational cost remains low (below 0.07~s), making the proposed approach practical for real-world applications. Overall, the results demonstrate that GA-driven adaptive embedding enables high-capacity steganography with excellent visual fidelity, stability, and reliable extraction.

\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size=$64\times64$.}
\label{tab:bpp04_summary_64x64}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 60.155272 & 60.145982 & 60.164562 & 0.066624 / 0.004439 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.999352  & 0.999312  & 0.999391  & 0.000282 / 0.000000 \\
HED=True, GA=True & IF(cover,stego)        & 0.996882  & 0.996303  & 0.997460  & 0.004148 / 0.000017 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.062401  & 0.059783  & 0.065019  & 0.018776 / 0.000353 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.039320  & 0.037786  & 0.040854  & 0.011002 / 0.000121 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 54.973572 & 54.757643 & 55.189502 & 1.548567 / 2.398059 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.998408  & 0.998331  & 0.998486  & 0.000554 / 0.000000 \\
HED=True, GA=False & IF(cover,stego)        & 0.994480  & 0.993642  & 0.995317  & 0.006008 / 0.000036 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000004  & 0.000001 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.034212  & 0.032641  & 0.035782  & 0.011263 / 0.000127 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.022797  & 0.021896  & 0.023697  & 0.006456 / 0.000042 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 60.164466 & 60.160063 & 60.168870 & 0.031582 / 0.000997 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.999353  & 0.999313  & 0.999392  & 0.000283 / 0.000000 \\
HED=False, GA=True & IF(cover,stego)        & 0.996884  & 0.996305  & 0.997462  & 0.004149 / 0.000017 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.062075  & 0.059379  & 0.064771  & 0.019335 / 0.000374 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.038346  & 0.036815  & 0.039877  & 0.010979 / 0.000121 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 56.393780 & 56.351745 & 56.435815 & 0.301457 / 0.090877 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.998649  & 0.998572  & 0.998726  & 0.000551 / 0.000000 \\
HED=False, GA=False & IF(cover,stego)        & 0.995235  & 0.994370  & 0.996101  & 0.006205 / 0.000039 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.034085  & 0.032589  & 0.035580  & 0.010727 / 0.000115 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.023007  & 0.022133  & 0.023880  & 0.006263 / 0.000039 \\
\hline

\end{tabular}%
}
\end{table}


{\color{blue}
Table~\ref{tab:bpp01_summary_128x128} reports a detailed statistical summary of the ablation study conducted at BPP = 0.1 across $N=200$ cover images, using a larger secret image size of $128\times128$. The reported mean values, 95\% confidence intervals (CI), standard deviations, and variances provide insight into both the average performance and its statistical stability under increased payload dimensionality.For GA-enabled configurations, both \textit{HED=True, GA=True} and \textit{HED=False, GA=True} maintain very high imperceptibility, with PSNR values exceeding 61~dB and SSIM values above 0.99948. Although the standard deviation of PSNR increases slightly compared to smaller secret sizes, the corresponding confidence intervals remain narrow (width $< 0.04$~dB), indicating that performance remains statistically consistent across all cover images. The IF metric follows a similar pattern, with high mean values (approximately 0.99722) and low variance, confirming stable information fidelity despite the increased secret size.
}

{\color{blue}
In contrast, disabling GA optimization results in a substantial degradation in imperceptibility and statistical robustness. Specifically, \textit{HED=True, GA=False} exhibits a PSNR mean near 56.08~dB with a very wide confidence interval (spanning more than 0.43~dB) and a large variance ($>2.4$), highlighting strong sensitivity to image content. A comparable trend is observed for \textit{HED=False, GA=False}, where PSNR remains below 57.4~dB and variability is significantly higher than in GA-enabled cases. These results demonstrate that fixed parameter configurations do not generalize reliably when the secret size increases.Across all ablation settings, perfect extraction accuracy is preserved, as evidenced by SSIM$(\text{secret}, \text{extracted}) = 1.0$ with zero variance. This confirms that the proposed embedding and extraction processes remain deterministic and lossless at BPP = 0.1, even for larger secret images. From a computational standpoint, GA optimization introduces additional overhead, increasing embedding time from approximately 0.026~s to 0.050~s and extraction time from approximately 0.018~s to 0.031~s. However, these costs remain low in absolute terms and exhibit tight confidence intervals, indicating predictable and stable runtime behavior.Overall, the statistical evidence indicates that GA optimization remains the dominant factor for reducing variance and narrowing confidence intervals at BPP = 0.1, even when the secret image size is increased to $128\times128$. The marginal differences between HED-enabled and HED-disabled configurations under GA further suggest that adaptive optimization mitigates most content-dependent variability, ensuring robust and consistent performance across all covers.
}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size=$128\times128$.}
\label{tab:bpp01_summary_128x128}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 61.147545 & 61.129571 & 61.165518 & 0.128899 / 0.016615 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.999484  & 0.999453  & 0.999515  & 0.000223 / 0.000000 \\
HED=True, GA=True & IF(cover,stego)        & 0.997218  & 0.996701  & 0.997734  & 0.003704 / 0.000014 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.049962  & 0.047758  & 0.052167  & 0.015811 / 0.000250 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.030676  & 0.029474  & 0.031877  & 0.008614 / 0.000074 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 56.083924 & 55.867008 & 56.300840 & 1.555641 / 2.420018 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.998776  & 0.998718  & 0.998835  & 0.000419 / 0.000000 \\
HED=True, GA=False & IF(cover,stego)        & 0.995142  & 0.994405  & 0.995879  & 0.005284 / 0.000028 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000003  & 0.000002  & 0.000003  & 0.000001 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.026653  & 0.025306  & 0.028000  & 0.009662 / 0.000093 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.018347  & 0.017527  & 0.019167  & 0.005883 / 0.000035 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 61.171940 & 61.167321 & 61.176558 & 0.033123 / 0.001097 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
HED=False, GA=True & IF(cover,stego)        & 0.997224  & 0.996708  & 0.997740  & 0.003701 / 0.000014 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.047874  & 0.045923  & 0.049825  & 0.013993 / 0.000196 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.028953  & 0.027883  & 0.030024  & 0.007677 / 0.000059 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 57.399868 & 57.357285 & 57.442452 & 0.305392 / 0.093264 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.998937  & 0.998878  & 0.998997  & 0.000426 / 0.000000 \\
HED=False, GA=False & IF(cover,stego)        & 0.995757  & 0.994988  & 0.996527  & 0.005519 / 0.000030 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.026246  & 0.025188  & 0.027304  & 0.007589 / 0.000058 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.017833  & 0.017198  & 0.018468  & 0.004552 / 0.000021 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:bpp02_summary_128x128} presents a statistical summary of the ablation study conducted at BPP = 0.2 across $N=200$ cover images, using a secret image size of $128\times128$. Compared to lower payload or smaller secret settings, the increased embedding rate and secret dimensionality lead to a noticeable reduction in imperceptibility metrics; however, the proposed framework continues to demonstrate stable and statistically reliable performance, particularly when GA optimization is enabled. For GA-enabled configurations, both \textit{HED=True, GA=True} and \textit{HED=False, GA=True} achieve PSNR values around 58.22~dB and SSIM values close to 0.9990. Despite the higher payload, the corresponding 95\% confidence intervals remain narrow (PSNR CI width $\approx 0.025$~dB), and the standard deviations are relatively small, indicating strong consistency across all cover images. The IF metric follows a similar trend, maintaining mean values above 0.996 with low variance, which confirms that information fidelity is preserved uniformly even under more demanding embedding conditions.
}

{\color{blue}
In contrast, disabling GA optimization results in a substantial degradation in both average performance and statistical stability. Specifically, \textit{HED=True, GA=False} exhibits a PSNR mean of approximately 52.89~dB with a wide confidence interval exceeding 0.42~dB and a large variance ($>2.3$). A similar pattern is observed for \textit{HED=False, GA=False}, where PSNR drops to around 54.45~dB and the dispersion of results increases significantly. These findings highlight that fixed embedding parameters become increasingly sensitive to cover content as payload and secret size increase, leading to inconsistent performance.Across all ablation settings, perfect extraction accuracy is maintained, as evidenced by SSIM$(\text{secret}, \text{extracted}) = 1.0$ with zero variance. This confirms that the extraction process remains deterministic and fully reversible even at higher payloads. From a computational perspective, GA optimization introduces additional overhead, increasing embedding time from approximately 0.048--0.049~s (GA=False) to about 0.090~s (GA=True), and extraction time from approximately 0.030--0.031~s to about 0.054--0.055~s. Although these runtimes increase with payload and secret size, the tight confidence intervals indicate predictable and stable computational behavior.Overall, the statistical evidence demonstrates that GA optimization is essential for maintaining both imperceptibility and robustness at BPP = 0.2 with larger secret images. While HED-based edge guidance contributes marginally to mean performance, the dominant factor in reducing variance and narrowing confidence intervals is adaptive GA-driven optimization, which enables consistent behavior under higher embedding stress.
}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size=$128\times128$.}
\label{tab:bpp02_summary_128x128}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 58.214558 & 58.201923 & 58.227193 & 0.090615 / 0.008211 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.998999  & 0.998937  & 0.999060  & 0.000440 / 0.000000 \\
HED=True, GA=True & IF(cover,stego)        & 0.996100  & 0.995375  & 0.996825  & 0.005202 / 0.000027 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.090045  & 0.086333  & 0.093757  & 0.026620 / 0.000709 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.054985  & 0.052835  & 0.057136  & 0.015422 / 0.000238 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 52.886953 & 52.673667 & 53.100239 & 1.529608 / 2.339700 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.997398  & 0.997269  & 0.997528  & 0.000930 / 0.000001 \\
HED=True, GA=False & IF(cover,stego)        & 0.992986  & 0.991922  & 0.994050  & 0.007629 / 0.000058 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000005  & 0.000005  & 0.000006  & 0.000002 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.047650  & 0.045592  & 0.049708  & 0.014759 / 0.000218 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.030552  & 0.029357  & 0.031747  & 0.008571 / 0.000073 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 58.228887 & 58.224250 & 58.233524 & 0.033255 / 0.001106 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.999001  & 0.998940  & 0.999062  & 0.000440 / 0.000000 \\
HED=False, GA=True & IF(cover,stego)        & 0.996105  & 0.995380  & 0.996830  & 0.005200 / 0.000027 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.089845  & 0.085992  & 0.093697  & 0.027628 / 0.000763 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.053574  & 0.051496  & 0.055653  & 0.014905 / 0.000222 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 54.445384 & 54.402733 & 54.488034 & 0.305875 / 0.093559 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.997812  & 0.997688  & 0.997935  & 0.000887 / 0.000001 \\
HED=False, GA=False & IF(cover,stego)        & 0.994036  & 0.992954  & 0.995118  & 0.007758 / 0.000060 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.049343  & 0.047161  & 0.051525  & 0.015648 / 0.000245 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.031026  & 0.029814  & 0.032238  & 0.008695 / 0.000076 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:bpp04_summary_128x128} reports the statistical behavior of the ablation configurations at a high payload (BPP = 0.4) across $N=200$ cover images using a secret size of $128\times128$. As expected, increasing the embedding rate to 0.4 introduces stronger imperceptibility pressure, reflected by lower PSNR/SSIM values and higher dispersion, particularly for non-optimized settings. Nevertheless, GA-enabled configurations retain clear advantages in both mean performance and statistical reliability.For the GA-enabled runs, \textit{HED=True, GA=True} achieves a mean PSNR of 54.92~dB with SSIM of 0.9980 and IF of 0.9943. Importantly, the 95\% CI ranges remain relatively controlled (PSNR CI width $\approx 0.213$~dB), indicating that performance remains statistically stable even under high embedding stress. A similar trend is observed for \textit{HED=False, GA=True}, which attains an even higher PSNR mean of 55.15~dB with a notably tighter CI (width $\approx 0.006$~dB) and substantially lower variance (PSNR variance $\approx 4.67\times10^{-4}$). This suggests that the GA-selected embedding parameters generalize strongly across diverse covers and reduce sensitivity to image content, yielding consistent imperceptibility.
}

{\color{blue}
By contrast, disabling GA leads to a marked decline in both central tendency and stability. In \textit{HED=True, GA=False}, PSNR drops sharply to 49.50~dB and SSIM to 0.9946, accompanied by a wide CI (PSNR width $\approx 0.394$~dB) and a large variance (PSNR variance $\approx 1.99$). This indicates that fixed thresholds/bits become increasingly mismatched to local cover characteristics at high payload, producing highly variable distortion across samples. Although \textit{HED=False, GA=False} performs better than the HED counterpart in mean PSNR (51.38~dB), its SSIM/IF remain lower than GA settings and its variance is still higher than the GA-optimized condition, confirming that optimization is the dominant stabilizing factor at BPP = 0.4.Across all ablation settings, extraction remains perfect (SSIM$(\text{secret},\text{extracted})=1.0$ with zero variance), confirming reversibility and robustness of recovery even at high payload. However, the computational overhead becomes more pronounced: GA increases embedding time from roughly 0.09--0.095~s (GA=False) to $\approx$0.177--0.179~s (GA=True), and increases extraction time from $\approx$0.056--0.059~s to $\approx$0.105--0.106~s. Despite this overhead, the CIs for timing remain narrow, indicating predictable runtime behavior.Overall, the statistical evidence at BPP = 0.4 demonstrates that GA optimization is essential for sustaining high imperceptibility and controlling variance under large payloads. While HED-based guidance influences the mean performance in some cases, the strongest and most consistent improvements are primarily driven by GA, which reduces dispersion (variance) and tightens confidence intervals, thereby improving the reliability of the embedding quality across all covers.
}




\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size=$128\times128$.}
\label{tab:bpp04_summary_128x128}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 54.918008 & 54.811499 & 55.024517 & 0.763842 / 0.583455 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.998045  & 0.997913  & 0.998176  & 0.000944 / 0.000001 \\
HED=True, GA=True & IF(cover,stego)        & 0.994310  & 0.993243  & 0.995377  & 0.007652 / 0.000059 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000001 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.178809  & 0.171154  & 0.186463  & 0.054897 / 0.003014 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.105979  & 0.101684  & 0.110274  & 0.030803 / 0.000949 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 49.498530 & 49.301661 & 49.695400 & 1.411874 / 1.993389 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.994559  & 0.994285  & 0.994832  & 0.001961 / 0.000004 \\
HED=True, GA=False & IF(cover,stego)        & 0.989668  & 0.988123  & 0.991213  & 0.011081 / 0.000123 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000012  & 0.000011  & 0.000012  & 0.000004 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.090033  & 0.085816  & 0.094250  & 0.030243 / 0.000915 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.055746  & 0.053198  & 0.058294  & 0.018272 / 0.000334 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 55.151308 & 55.148294 & 55.154323 & 0.021621 / 0.000467 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.998131  & 0.998015  & 0.998247  & 0.000832 / 0.000001 \\
HED=False, GA=True & IF(cover,stego)        & 0.994447  & 0.993411  & 0.995483  & 0.007428 / 0.000055 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.176789  & 0.169401  & 0.184177  & 0.052985 / 0.002807 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.105347  & 0.101185  & 0.109509  & 0.029849 / 0.000891 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 51.381385 & 51.339265 & 51.423505 & 0.302069 / 0.091246 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.995625  & 0.995369  & 0.995880  & 0.001833 / 0.000003 \\
HED=False, GA=False & IF(cover,stego)        & 0.991505  & 0.989954  & 0.993056  & 0.011121 / 0.000124 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000007  & 0.000007  & 0.000007  & 0.000001 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.095085  & 0.091155  & 0.099015  & 0.028184 / 0.000794 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.058578  & 0.056157  & 0.061000  & 0.017367 / 0.000302 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:bpp01_ablation_hed_ga} presents a $2\times2$ ablation study analyzing the individual and combined effects of HED-based edge guidance and Genetic Algorithm (GA) optimization at BPP = 0.1 across $N=200$ cover images. The reported mean values, 95\% confidence intervals (CI), standard deviations, and variances provide insight into both average performance and statistical stability.When GA optimization is enabled, both \textit{HED=True, GA=True} and \textit{HED=False, GA=True} achieve very high imperceptibility, with PSNR values exceeding 61~dB and SSIM values above 0.99948. Notably, \textit{HED=False, GA=True} attains the highest PSNR mean (61.17~dB) with the narrowest confidence interval and lowest variance among all configurations, indicating exceptional stability and minimal sensitivity to cover content. The slightly larger variance observed in \textit{HED=True, GA=True} suggests that while HED contributes to edge-aware guidance, its impact on stability at low payload is secondary compared to GA-driven optimization.In contrast, disabling GA leads to a pronounced degradation in both imperceptibility and robustness. For \textit{HED=True, GA=False}, PSNR drops to approximately 56.09~dB with a wide confidence interval (spanning over 0.43~dB) and a large variance ($>2.4$), indicating inconsistent behavior across different covers. A similar, though slightly improved, trend is observed for \textit{HED=False, GA=False}, where PSNR increases to 57.42~dB but remains substantially lower and more variable than GA-enabled configurations. These results clearly demonstrate that fixed embedding parameters lack generalization capability even at low payload rates.
}

{\color{blue}
Across all ablation settings, secret extraction remains perfect, as evidenced by SSIM$(\text{secret},\text{extracted})=1.0$ with zero variance, confirming deterministic and lossless recovery. From a computational perspective, GA optimization introduces moderate overhead, increasing embedding and extraction times by approximately a factor of two compared to GA-disabled cases. However, the confidence intervals and standard deviations of runtime metrics remain small, indicating predictable and stable computational cost.Overall, the $2\times2$ ablation study confirms that GA optimization is the dominant factor in achieving high imperceptibility and statistical robustness at BPP = 0.1. While HED-based edge guidance provides complementary benefits in mean performance, its effect is comparatively minor when GA optimization is present. The combination of GA with or without HED yields the most consistent and reliable embedding performance across all covers.
}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under the $2\times2$ ablation (HED $\times$ GA).}
\label{tab:bpp01_ablation_hed_ga}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 61.154309 & 61.140268 & 61.168350 & 0.100700 / 0.010140 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.999484  & 0.999453  & 0.999515  & 0.000223 / 0.000000 \\
HED=True, GA=True & IF(cover,stego)        & 0.997220  & 0.996704  & 0.997736  & 0.003699 / 0.000014 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.061920  & 0.059354  & 0.064485  & 0.018399 / 0.000339 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.036847  & 0.035392  & 0.038302  & 0.010435 / 0.000109 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 56.092113 & 55.875303 & 56.308923 & 1.554880 / 2.417651 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.998778  & 0.998719  & 0.998836  & 0.000419 / 0.000000 \\
HED=True, GA=False & IF(cover,stego)        & 0.995146  & 0.994411  & 0.995881  & 0.005274 / 0.000028 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000003  & 0.000002  & 0.000003  & 0.000001 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.032242  & 0.030758  & 0.033726  & 0.010643 / 0.000113 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.021971  & 0.021046  & 0.022896  & 0.006637 / 0.000044 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 61.173279 & 61.168837 & 61.177721 & 0.031854 / 0.001015 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
HED=False, GA=True & IF(cover,stego)        & 0.997225  & 0.996709  & 0.997740  & 0.003697 / 0.000014 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.060113  & 0.057601  & 0.062625  & 0.018015 / 0.000325 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.036228  & 0.034771  & 0.037685  & 0.010449 / 0.000109 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 57.419442 & 57.377407 & 57.461477 & 0.301458 / 0.090877 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.998940  & 0.998881  & 0.999000  & 0.000425 / 0.000000 \\
HED=False, GA=False & IF(cover,stego)        & 0.995766  & 0.994998  & 0.996534  & 0.005510 / 0.000030 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.031925  & 0.030561  & 0.033290  & 0.009784 / 0.000096 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.021597  & 0.020777  & 0.022416  & 0.005878 / 0.000035 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:bpp02_ablation_hed_ga} presents a $2\times2$ ablation study that evaluates the individual and combined effects of HED-based edge guidance and Genetic Algorithm (GA) optimization at BPP = 0.2 across $N=200$ cover images. Compared to lower payload settings, the increase in embedding rate results in a noticeable reduction in imperceptibility; however, the statistical results indicate that GA optimization continues to play a dominant role in maintaining both performance and stability.For GA-enabled configurations, both \textit{HED=True, GA=True} and \textit{HED=False, GA=True} achieve PSNR values around 58.22~dB with SSIM values close to 0.9990. Importantly, the 95\% confidence intervals remain narrow (PSNR CI width $\approx 0.02$~dB), and the standard deviations are small, indicating consistent performance across all cover images. Among these, \textit{HED=False, GA=True} achieves the highest PSNR mean and slightly tighter CI, suggesting that at this payload level GA-driven adaptation dominates stability gains, while the additional edge guidance from HED has a marginal impact on overall consistency.

}

{\color{blue}
In contrast, disabling GA optimization leads to a significant degradation in both mean performance and statistical robustness. Specifically, \textit{HED=True, GA=False} exhibits a PSNR mean of approximately 52.90~dB with a wide confidence interval exceeding 0.42~dB and a large variance ($>2.3$), reflecting strong sensitivity to image content. A similar but slightly improved trend is observed for \textit{HED=False, GA=False}, where PSNR increases to approximately 54.47~dB; nevertheless, both non-GA configurations remain substantially inferior to GA-enabled cases in terms of imperceptibility and stability.Across all ablation settings, secret recovery remains perfect, as indicated by SSIM$(\text{secret}, \text{extracted}) = 1.0$ with zero variance. This confirms deterministic and lossless extraction even under increased payload. From a computational standpoint, GA optimization introduces additional overhead, increasing embedding time from approximately 0.053--0.055~s (GA=False) to about 0.100~s (GA=True), and extraction time from approximately 0.034--0.035~s to about 0.060~s. Despite this increase, the narrow confidence intervals of runtime metrics indicate predictable and stable computational behavior.Overall, the $2\times2$ ablation study at BPP = 0.2 confirms that GA optimization is the primary factor for sustaining imperceptibility and reducing variance under higher payload conditions. While HED-based edge guidance slightly influences mean performance, its contribution is secondary compared to the stabilizing effect of adaptive GA optimization.

}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under the $2\times2$ ablation (HED $\times$ GA).}
\label{tab:bpp02_ablation_hed_ga}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 58.216104 & 58.204466 & 58.227743 & 0.083468 / 0.006967 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.998998  & 0.998937  & 0.999060  & 0.000441 / 0.000000 \\
HED=True, GA=True & IF(cover,stego)        & 0.996101  & 0.995376  & 0.996825  & 0.005196 / 0.000027 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.100311  & 0.096513  & 0.104110  & 0.027240 / 0.000742 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.059762  & 0.057457  & 0.062068  & 0.016533 / 0.000273 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 52.899193 & 52.686274 & 53.112111 & 1.526971 / 2.331640 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.997404  & 0.997274  & 0.997533  & 0.000928 / 0.000001 \\
HED=True, GA=False & IF(cover,stego)        & 0.992997  & 0.991935  & 0.994058  & 0.007613 / 0.000058 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000005  & 0.000005  & 0.000006  & 0.000002 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.052906  & 0.050510  & 0.055302  & 0.017180 / 0.000295 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.033814  & 0.032419  & 0.035209  & 0.010002 / 0.000100 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 58.227709 & 58.223290 & 58.232129 & 0.031697 / 0.001005 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.999000  & 0.998939  & 0.999062  & 0.000440 / 0.000000 \\
HED=False, GA=True & IF(cover,stego)        & 0.996105  & 0.995381  & 0.996829  & 0.005193 / 0.000027 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.100726  & 0.096883  & 0.104570  & 0.027565 / 0.000760 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.059538  & 0.057207  & 0.061868  & 0.016714 / 0.000279 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 54.465649 & 54.422913 & 54.508385 & 0.306486 / 0.093934 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.997822  & 0.997699  & 0.997945  & 0.000882 / 0.000001 \\
HED=False, GA=False & IF(cover,stego)        & 0.994052  & 0.992976  & 0.995129  & 0.007723 / 0.000060 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.054492  & 0.052004  & 0.056979  & 0.017838 / 0.000318 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.035009  & 0.033501  & 0.036517  & 0.010812 / 0.000117 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:bpp04_secret512_summary} reports the statistical summary (mean, 95\% CI, standard deviation, and variance) over $N=200$ cover images at a high payload setting (BPP = 0.4) while embedding a full-size secret image ($512\times512$). As expected under increased payload pressure, imperceptibility decreases compared to lower BPP cases; nevertheless, the results clearly show that GA optimization remains the dominant factor in improving both average performance and statistical reliability.For GA-enabled settings, both \textit{HED=True, GA=True} and \textit{HED=False, GA=True} achieve high imperceptibility, with mean PSNR $\approx$ 54.94--55.15~dB and SSIM $\approx$ 0.9981. However, the stability characteristics differ substantially between the two. In particular, \textit{HED=False, GA=True} exhibits an extremely tight PSNR confidence interval (CI width $\approx 0.0063$~dB) and very low variance ($\approx 5.0\times10^{-4}$), indicating highly consistent behavior across all covers. By contrast, \textit{HED=True, GA=True} shows a much wider PSNR CI (width $\approx 0.1983$~dB) and a markedly higher variance ($\approx 0.5056$), suggesting that incorporating HED at this high payload introduces greater sensitivity to the specific edge content of each cover image, even though the mean imperceptibility remains comparable.

}

{\color{blue}
Disabling GA causes a pronounced drop in both mean quality and robustness. \textit{HED=True, GA=False} degrades to PSNR $\approx 49.49$~dB and SSIM $\approx 0.9946$, with large variability (PSNR variance $\approx 1.997$), reflecting substantial dependence on image characteristics. The non-GA baseline without HED (\textit{HED=False, GA=False}) is slightly better in PSNR (mean $\approx 51.38$~dB) and shows improved stability relative to \textit{HED=True, GA=False}, yet it remains clearly inferior to GA-enabled configurations in both imperceptibility and statistical consistency.Across all ablations, secret recovery remains perfect (SSIM$(\text{secret},\text{extracted}) = 1.0$ with zero variance), confirming lossless extraction despite the higher payload and full-size secret. From a computational standpoint, GA increases runtime substantially: embedding time rises from $\approx$0.10--0.11~s (GA=False) to $\approx$0.206~s (GA=True), and extraction time increases from $\approx$0.065--0.068~s to $\approx$0.118~s. Notably, the runtime confidence intervals remain narrow, indicating predictable execution even with GA enabled.Overall, the statistical evidence at BPP = 0.4 demonstrates that GA optimization is essential for sustaining high imperceptibility and reducing variability under full-size secret embedding. Meanwhile, at this payload level, the HED component appears to trade stability for edge-guided adaptivity, improving mean quality only marginally while increasing variance in certain cases.


}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:bpp04_secret512_summary}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 54.944699 & 54.845551 & 55.043848 & 0.711053 / 0.505596 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.998055  & 0.997925  & 0.998185  & 0.000931 / 0.000001 \\
HED=True, GA=True & IF(cover,stego)        & 0.994326  & 0.993261  & 0.995391  & 0.007636 / 0.000058 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000001 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.206095  & 0.197307  & 0.214883  & 0.063027 / 0.003972 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.118786  & 0.113792  & 0.123781  & 0.035821 / 0.001283 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 49.494222 & 49.297172 & 49.691272 & 1.413170 / 1.997048 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.994555  & 0.994281  & 0.994828  & 0.001961 / 0.000004 \\
HED=True, GA=False & IF(cover,stego)        & 0.989664  & 0.988120  & 0.991208  & 0.011073 / 0.000123 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000012  & 0.000011  & 0.000012  & 0.000004 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.103142  & 0.098439  & 0.107845  & 0.033728 / 0.001138 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.065299  & 0.062169  & 0.068428  & 0.022445 / 0.000504 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 55.151402 & 55.148270 & 55.154534 & 0.022464 / 0.000505 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.998131  & 0.998015  & 0.998247  & 0.000833 / 0.000001 \\
HED=False, GA=True & IF(cover,stego)        & 0.994447  & 0.993412  & 0.995482  & 0.007423 / 0.000055 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.206170  & 0.197776  & 0.214564  & 0.060197 / 0.003624 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.118354  & 0.113471  & 0.123237  & 0.035019 / 0.001226 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 51.376735 & 51.334470 & 51.419000 & 0.303111 / 0.091876 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.995619  & 0.995363  & 0.995875  & 0.001836 / 0.000003 \\
HED=False, GA=False & IF(cover,stego)        & 0.991499  & 0.989947  & 0.993052  & 0.011135 / 0.000124 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000007  & 0.000007  & 0.000007  & 0.000001 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.111096  & 0.106329  & 0.115863  & 0.034186 / 0.001169 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.068421  & 0.065372  & 0.071471  & 0.021871 / 0.000478 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_rcf_ga_bpp0111} presents a $2\times2$ ablation study examining the individual and combined effects of RCF-based feature guidance and Genetic Algorithm (GA) optimization at BPP = 0.1 across $N=200$ cover images. The reported mean values, confidence intervals, and dispersion measures highlight both average performance and statistical stability.When GA optimization is enabled, both \textit{RCF=True, GA=True} and \textit{RCF=False, GA=True} achieve very high imperceptibility, with PSNR values exceeding 61~dB and SSIM values above 0.99948. Notably, the two configurations exhibit nearly identical mean values and extremely tight confidence intervals, indicating that GA-driven adaptation is the dominant factor in stabilizing embedding quality. The marginal difference between enabling and disabling RCF under GA suggests that, at this low payload, optimization largely compensates for variations in local texture or edge structure.


}

{\color{blue}
In contrast, disabling GA leads to a clear degradation in both mean performance and robustness. For \textit{RCF=True, GA=False}, PSNR drops to approximately 57.62~dB with a relatively wide confidence interval and increased variance, reflecting sensitivity to cover content despite the presence of RCF features. A similar trend is observed for \textit{RCF=False, GA=False}, where PSNR further decreases to around 57.40~dB with comparable dispersion. These results confirm that fixed embedding parameters lack sufficient adaptability even when RCF guidance is employed.Across all ablation settings, secret extraction remains perfect, as evidenced by SSIM$(\text{secret},\text{extracted}) = 1.0$ with zero variance, demonstrating deterministic and lossless recovery. From a computational standpoint, GA optimization introduces a moderate increase in runtime: embedding time rises from approximately 0.031--0.035~s (GA=False) to about 0.054--0.055~s (GA=True), and extraction time increases from approximately 0.021--0.023~s to about 0.033~s. Importantly, the runtime confidence intervals remain narrow, indicating predictable computational behavior.Overall, the $2\times2$ ablation study confirms that GA optimization is the primary contributor to imperceptibility and statistical robustness at BPP = 0.1. While RCF-based feature guidance provides modest improvements when GA is disabled, its contribution becomes secondary once adaptive GA optimization is applied.


}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_rcf_ga_bpp0111}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

RCF=True, GA=True & PSNR(cover,stego)      & 61.171969 & 61.167302 & 61.176636 & 0.033471 / 0.001120 \\
RCF=True, GA=True & SSIM(cover,stego)      & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
RCF=True, GA=True & IF(cover,stego)        & 0.997225  & 0.996711  & 0.997739  & 0.003686 / 0.000014 \\
RCF=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
RCF=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=True & Embedding\_Time(s)     & 0.054245  & 0.051967  & 0.056523  & 0.016335 / 0.000267 \\
RCF=True, GA=True & Extraction\_Time(s)    & 0.033115  & 0.031735  & 0.034495  & 0.009896 / 0.000098 \\
\hline

RCF=True, GA=False & PSNR(cover,stego)      & 57.624928 & 57.480711 & 57.769144 & 1.034267 / 1.069708 \\
RCF=True, GA=False & SSIM(cover,stego)      & 0.999021  & 0.998966  & 0.999076  & 0.000392 / 0.000000 \\
RCF=True, GA=False & IF(cover,stego)        & 0.995862  & 0.995181  & 0.996543  & 0.004885 / 0.000024 \\
RCF=True, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=False & Embedding\_Time(s)     & 0.035260  & 0.033697  & 0.036823  & 0.011212 / 0.000126 \\
RCF=True, GA=False & Extraction\_Time(s)    & 0.022953  & 0.021960  & 0.023945  & 0.007117 / 0.000051 \\
\hline

RCF=False, GA=True & PSNR(cover,stego)      & 61.172317 & 61.167702 & 61.176931 & 0.033095 / 0.001095 \\
RCF=False, GA=True & SSIM(cover,stego)      & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
RCF=False, GA=True & IF(cover,stego)        & 0.997225  & 0.996711  & 0.997739  & 0.003686 / 0.000014 \\
RCF=False, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
RCF=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=True & Embedding\_Time(s)     & 0.055325  & 0.052983  & 0.057666  & 0.016793 / 0.000282 \\
RCF=False, GA=True & Extraction\_Time(s)    & 0.033416  & 0.031951  & 0.034880  & 0.010505 / 0.000110 \\
\hline

RCF=False, GA=False & PSNR(cover,stego)      & 57.401460 & 57.359376 & 57.443545 & 0.301814 / 0.091092 \\
RCF=False, GA=False & SSIM(cover,stego)      & 0.998939  & 0.998880  & 0.998998  & 0.000425 / 0.000000 \\
RCF=False, GA=False & IF(cover,stego)        & 0.995760  & 0.994993  & 0.996527  & 0.005497 / 0.000030 \\
RCF=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=False & Embedding\_Time(s)     & 0.031345  & 0.029963  & 0.032726  & 0.009907 / 0.000098 \\
RCF=False, GA=False & Extraction\_Time(s)    & 0.020543  & 0.019619  & 0.021467  & 0.006627 / 0.000044 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_hed_ga_bpp02} presents a $2\times2$ ablation study analyzing the effect of HED-based edge guidance and Genetic Algorithm (GA) optimization at BPP = 0.2 across $N=200$ cover images with a secret size of $64\times64$. Compared to the lower payload setting (BPP = 0.1), the increase in embedding rate leads to a moderate degradation in imperceptibility; however, the statistical results indicate that GA optimization remains the dominant factor in preserving performance stability.For GA-enabled configurations, both \textit{HED=True, GA=True} and \textit{HED=False, GA=True} achieve PSNR values around 58.22~dB and SSIM values close to 0.9990. Importantly, the corresponding 95\% confidence intervals are narrow (PSNR CI width $\approx 0.02$~dB), and the standard deviations remain low, indicating consistent behavior across all cover images. Among these, \textit{HED=False, GA=True} attains a slightly higher PSNR mean with a tighter CI, suggesting that at this payload level the contribution of GA-driven adaptive optimization outweighs that of explicit edge guidance.


}

{\color{blue}
In contrast, disabling GA optimization results in a pronounced decline in both mean performance and statistical robustness. Specifically, \textit{HED=True, GA=False} exhibits a PSNR mean of approximately 52.90~dB with a wide confidence interval exceeding 0.42~dB and a large variance ($>2.3$), reflecting strong sensitivity to image content. Although \textit{HED=False, GA=False} performs somewhat better in mean PSNR (approximately 54.47~dB), it still exhibits significantly higher variability compared to GA-enabled cases, confirming that fixed embedding parameters do not generalize reliably under increased payload.Across all ablation settings, secret extraction remains perfect, as indicated by SSIM$(\text{secret},\text{extracted}) = 1.0$ with zero variance, confirming deterministic and lossless recovery at BPP = 0.2. From a computational standpoint, GA optimization approximately doubles the embedding and extraction times, increasing embedding time from about 0.053--0.054~s to approximately 0.100~s, and extraction time from about 0.034--0.035~s to around 0.060~s. Nevertheless, the narrow confidence intervals of runtime metrics indicate predictable and stable computational behavior.Overall, the $2\times2$ ablation study at BPP = 0.2 confirms that GA optimization is the primary contributor to both imperceptibility and statistical stability. While HED-based edge guidance provides marginal improvements in mean performance, its effect is secondary once adaptive GA optimization is applied.


}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_hed_ga_bpp02}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 58.216104 & 58.204466 & 58.227743 & 0.083468 / 0.006967 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.998998  & 0.998937  & 0.999060  & 0.000441 / 0.000000 \\
HED=True, GA=True & IF(cover,stego)        & 0.996101  & 0.995376  & 0.996825  & 0.005196 / 0.000027 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.100311  & 0.096513  & 0.104110  & 0.027240 / 0.000742 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.059762  & 0.057457  & 0.062068  & 0.016533 / 0.000273 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 52.899193 & 52.686274 & 53.112111 & 1.526971 / 2.331640 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.997404  & 0.997274  & 0.997533  & 0.000928 / 0.000001 \\
HED=True, GA=False & IF(cover,stego)        & 0.992997  & 0.991935  & 0.994058  & 0.007613 / 0.000058 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000005  & 0.000005  & 0.000006  & 0.000002 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.052906  & 0.050510  & 0.055302  & 0.017180 / 0.000295 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.033814  & 0.032419  & 0.035209  & 0.010002 / 0.000100 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 58.227709 & 58.223290 & 58.232129 & 0.031697 / 0.001005 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.999000  & 0.998939  & 0.999062  & 0.000440 / 0.000000 \\
HED=False, GA=True & IF(cover,stego)        & 0.996105  & 0.995381  & 0.996829  & 0.005193 / 0.000027 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.100726  & 0.096883  & 0.104570  & 0.027565 / 0.000760 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.059538  & 0.057207  & 0.061868  & 0.016714 / 0.000279 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 54.465649 & 54.422913 & 54.508385 & 0.306486 / 0.093934 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.997822  & 0.997699  & 0.997945  & 0.000882 / 0.000001 \\
HED=False, GA=False & IF(cover,stego)        & 0.994052  & 0.992976  & 0.995129  & 0.007723 / 0.000060 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.054492  & 0.052004  & 0.056979  & 0.017838 / 0.000318 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.035009  & 0.033501  & 0.036517  & 0.010812 / 0.000117 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_hed_ga_bpp04} reports a $2\times2$ ablation study evaluating the contribution of HED-based edge guidance and Genetic Algorithm (GA) optimization at a higher payload (BPP = 0.4) over $N=200$ cover images. As expected, increasing the embedding rate from lower BPP settings results in a noticeable reduction in imperceptibility; nevertheless, the statistical evidence indicates that GA remains the key factor for maintaining both high mean quality and strong stability.When GA is enabled, both \textit{HED=True, GA=True} and \textit{HED=False, GA=True} achieve PSNR values around 55~dB and SSIM values close to 0.998. Beyond the mean, the dispersion behavior is critical: the \textit{HED=False, GA=True} configuration exhibits an extremely tight PSNR confidence interval (CI width $\approx 0.0063$~dB) and a very small standard deviation (Std $\approx 0.022$), indicating highly consistent imperceptibility across all covers. In contrast, \textit{HED=True, GA=True} shows a substantially larger variability (Std $\approx 0.711$; Var $\approx 0.506$) and a much wider CI (width $\approx 0.198$~dB), suggesting that at BPP = 0.4 the explicit edge-guided selection may introduce content-dependent behavior across images, leading to heterogeneous outcomes.

}

{\color{blue}
Disabling GA produces a marked degradation in both mean quality and statistical robustness. Specifically, \textit{HED=True, GA=False} yields the lowest PSNR (mean $\approx 49.49$~dB) with high dispersion (Std $\approx 1.41$, Var $\approx 1.997$), confirming that fixed embedding without GA-driven adaptation is highly sensitive to cover characteristics at this payload. Meanwhile, \textit{HED=False, GA=False} improves the mean PSNR (to $\approx 51.38$~dB) but still maintains non-negligible variability, remaining clearly inferior to GA-enabled settings.Across all cases, secret recovery remains perfect, with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance, demonstrating that increased payload does not compromise extraction correctness in the proposed pipeline. From a computational perspective, GA substantially increases runtime: embedding time rises from $\approx 0.103$--$0.111$~s (GA=False) to $\approx 0.206$~s (GA=True), and extraction time increases from $\approx 0.065$--$0.068$~s to $\approx 0.118$~s. Importantly, the runtime confidence intervals remain relatively narrow, indicating stable computational overhead.Overall, the ablation results at BPP = 0.4 confirm that GA optimization is the dominant contributor to imperceptibility and reliability. Moreover, the comparison between GA-enabled variants suggests that, at high payload, the configuration without explicit HED guidance (\textit{HED=False, GA=True}) can yield superior statistical stability, while HED guidance may introduce higher variance due to content-dependent edge distributions.

}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_hed_ga_bpp04}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

HED=True, GA=True & PSNR(cover,stego)      & 54.944699 & 54.845551 & 55.043848 & 0.711053 / 0.505596 \\
HED=True, GA=True & SSIM(cover,stego)      & 0.998055  & 0.997925  & 0.998185  & 0.000931 / 0.000001 \\
HED=True, GA=True & IF(cover,stego)        & 0.994326  & 0.993261  & 0.995391  & 0.007636 / 0.000058 \\
HED=True, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000001 / 0.000000 \\
HED=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=True & Embedding\_Time(s)     & 0.206095  & 0.197307  & 0.214883  & 0.063027 / 0.003972 \\
HED=True, GA=True & Extraction\_Time(s)    & 0.118786  & 0.113792  & 0.123781  & 0.035821 / 0.001283 \\
\hline

HED=True, GA=False & PSNR(cover,stego)      & 49.494222 & 49.297172 & 49.691272 & 1.413170 / 1.997048 \\
HED=True, GA=False & SSIM(cover,stego)      & 0.994555  & 0.994281  & 0.994828  & 0.001961 / 0.000004 \\
HED=True, GA=False & IF(cover,stego)        & 0.989664  & 0.988120  & 0.991208  & 0.011073 / 0.000123 \\
HED=True, GA=False & MSE(cover,stego)       & 0.000012  & 0.000011  & 0.000012  & 0.000004 / 0.000000 \\
HED=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=True, GA=False & Embedding\_Time(s)     & 0.103142  & 0.098439  & 0.107845  & 0.033728 / 0.001138 \\
HED=True, GA=False & Extraction\_Time(s)    & 0.065299  & 0.062169  & 0.068428  & 0.022445 / 0.000504 \\
\hline

HED=False, GA=True & PSNR(cover,stego)      & 55.151402 & 55.148270 & 55.154534 & 0.022464 / 0.000505 \\
HED=False, GA=True & SSIM(cover,stego)      & 0.998131  & 0.998015  & 0.998247  & 0.000833 / 0.000001 \\
HED=False, GA=True & IF(cover,stego)        & 0.994447  & 0.993412  & 0.995482  & 0.007423 / 0.000055 \\
HED=False, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
HED=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=True & Embedding\_Time(s)     & 0.206170  & 0.197776  & 0.214564  & 0.060197 / 0.003624 \\
HED=False, GA=True & Extraction\_Time(s)    & 0.118354  & 0.113471  & 0.123237  & 0.035019 / 0.001226 \\
\hline

HED=False, GA=False & PSNR(cover,stego)      & 51.376735 & 51.334470 & 51.419000 & 0.303111 / 0.091876 \\
HED=False, GA=False & SSIM(cover,stego)      & 0.995619  & 0.995363  & 0.995875  & 0.001836 / 0.000003 \\
HED=False, GA=False & IF(cover,stego)        & 0.991499  & 0.989947  & 0.993052  & 0.011135 / 0.000124 \\
HED=False, GA=False & MSE(cover,stego)       & 0.000007  & 0.000007  & 0.000007  & 0.000001 / 0.000000 \\
HED=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
HED=False, GA=False & Embedding\_Time(s)     & 0.111096  & 0.106329  & 0.115863  & 0.034186 / 0.001169 \\
HED=False, GA=False & Extraction\_Time(s)    & 0.068421  & 0.065372  & 0.071471  & 0.021871 / 0.000478 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_rcf_ga_bpp01} presents a $2\times2$ ablation study investigating the impact of RCF-based feature guidance and Genetic Algorithm (GA) optimization at BPP = 0.1 across $N=200$ cover images, using a larger secret image size of $128\times128$. The reported mean values, confidence intervals, and dispersion measures provide insight into both average imperceptibility and statistical robustness under increased secret dimensionality.When GA optimization is enabled, both \textit{RCF=True, GA=True} and \textit{RCF=False, GA=True} achieve very high imperceptibility, with PSNR values around 61.16--61.17~dB and SSIM values exceeding 0.99948. The corresponding confidence intervals are narrow and the standard deviations remain small, indicating highly consistent performance across all cover images. Notably, the difference between enabling and disabling RCF under GA optimization is marginal, suggesting that at low payload the adaptive GA mechanism dominates stability, while RCF guidance provides only limited additional benefit.

}

{\color{blue}
In contrast, disabling GA results in a clear degradation in both mean performance and statistical reliability. Specifically, \textit{RCF=True, GA=False} exhibits a PSNR mean of approximately 57.62~dB with a wide confidence interval and a large variance, indicating sensitivity to cover content despite the presence of RCF features. A similar trend is observed for \textit{RCF=False, GA=False}, where PSNR further decreases to around 57.40~dB with comparable dispersion. These findings confirm that fixed embedding parameters lack sufficient adaptability even when advanced RCF-based guidance is applied.Across all ablation configurations, secret recovery remains perfect, as evidenced by SSIM$(\text{secret}, \text{extracted}) = 1.0$ with zero variance. This confirms deterministic and lossless extraction despite the increased secret image size. From a computational perspective, GA optimization introduces a moderate overhead, increasing embedding time from approximately 0.031--0.034~s (GA=False) to about 0.055--0.056~s (GA=True), and extraction time from approximately 0.020--0.022~s to about 0.034~s. Importantly, the narrow confidence intervals of runtime metrics indicate predictable and stable computational behavior.Overall, the $2\times2$ ablation study confirms that GA optimization is the primary contributor to both imperceptibility and statistical robustness at BPP = 0.1 with secret size $128\times128$. While RCF-based feature guidance provides marginal improvements in mean performance when GA is disabled, its effect becomes secondary once adaptive GA optimization is applied.

}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $128\times128$.}
\label{tab:summary_rcf_ga_bpp01}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

RCF=True, GA=True & PSNR(cover,stego)      & 61.159744 & 61.146043 & 61.173444 & 0.098254 / 0.009654 \\
RCF=True, GA=True & SSIM(cover,stego)      & 0.999485  & 0.999453  & 0.999516  & 0.000224 / 0.000000 \\
RCF=True, GA=True & IF(cover,stego)        & 0.997221  & 0.996705  & 0.997737  & 0.003701 / 0.000014 \\
RCF=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
RCF=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=True & Embedding\_Time(s)     & 0.055324  & 0.052866  & 0.057782  & 0.017626 / 0.000311 \\
RCF=True, GA=True & Extraction\_Time(s)    & 0.034007  & 0.032522  & 0.035492  & 0.010651 / 0.000113 \\
\hline

RCF=True, GA=False & PSNR(cover,stego)      & 57.621840 & 57.477256 & 57.766424 & 1.036900 / 1.075161 \\
RCF=True, GA=False & SSIM(cover,stego)      & 0.999020  & 0.998965  & 0.999074  & 0.000392 / 0.000000 \\
RCF=True, GA=False & IF(cover,stego)        & 0.995861  & 0.995182  & 0.996540  & 0.004870 / 0.000024 \\
RCF=True, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=False & Embedding\_Time(s)     & 0.033912  & 0.032355  & 0.035469  & 0.011168 / 0.000125 \\
RCF=True, GA=False & Extraction\_Time(s)    & 0.021849  & 0.020887  & 0.022810  & 0.006895 / 0.000048 \\
\hline

RCF=False, GA=True & PSNR(cover,stego)      & 61.171940 & 61.167321 & 61.176558 & 0.033123 / 0.001097 \\
RCF=False, GA=True & SSIM(cover,stego)      & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
RCF=False, GA=True & IF(cover,stego)        & 0.997224  & 0.996708  & 0.997740  & 0.003701 / 0.000014 \\
RCF=False, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
RCF=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=True & Embedding\_Time(s)     & 0.056302  & 0.053777  & 0.058828  & 0.018114 / 0.000328 \\
RCF=False, GA=True & Extraction\_Time(s)    & 0.034387  & 0.032835  & 0.035939  & 0.011129 / 0.000124 \\
\hline

RCF=False, GA=False & PSNR(cover,stego)      & 57.399868 & 57.357285 & 57.442452 & 0.305392 / 0.093264 \\
RCF=False, GA=False & SSIM(cover,stego)      & 0.998937  & 0.998878  & 0.998997  & 0.000426 / 0.000000 \\
RCF=False, GA=False & IF(cover,stego)        & 0.995757  & 0.994988  & 0.996527  & 0.005519 / 0.000030 \\
RCF=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=False & Embedding\_Time(s)     & 0.031030  & 0.029700  & 0.032361  & 0.009541 / 0.000091 \\
RCF=False, GA=False & Extraction\_Time(s)    & 0.019882  & 0.019051  & 0.020713  & 0.005957 / 0.000035 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_rcf_ga_bpp0222} presents a $2\times2$ ablation study analyzing the impact of RCF-based feature guidance and Genetic Algorithm (GA) optimization at BPP = 0.2 across $N=200$ cover images with a secret image size of $128\times128$. Compared to the lower payload setting (BPP = 0.1), increasing the embedding rate results in a moderate reduction in imperceptibility; however, the statistical indicators demonstrate that the proposed framework maintains strong stability, particularly when GA optimization is enabled.For GA-enabled configurations, both \textit{RCF=True, GA=True} and \textit{RCF=False, GA=True} achieve PSNR values around 58.22~dB and SSIM values close to 0.9990. The corresponding 95\% confidence intervals are narrow (PSNR CI width $\approx 0.016$--$0.018$~dB), and the standard deviations remain low, indicating consistent performance across all cover images. The very small difference between enabling and disabling RCF under GA suggests that, at this payload level, GA-driven adaptive optimization dominates stability, while explicit RCF-based feature guidance provides only marginal additional benefit.

}

{\color{blue}
In contrast, disabling GA optimization leads to a noticeable degradation in both mean imperceptibility and statistical robustness. Specifically, \textit{RCF=True, GA=False} exhibits a PSNR mean of approximately 54.41~dB with a wider confidence interval and higher variance, indicating increased sensitivity to cover content despite the use of RCF features. A similar trend is observed for \textit{RCF=False, GA=False}, where PSNR remains around 54.45~dB with comparable dispersion. These results confirm that fixed embedding parameters lack sufficient adaptability under higher payload conditions.Across all ablation configurations, secret recovery remains perfect, as evidenced by SSIM$(\text{secret},\text{extracted}) = 1.0$ with zero variance, confirming deterministic and lossless extraction even at BPP = 0.2 and larger secret size. From a computational perspective, GA optimization introduces a moderate overhead, increasing embedding time from approximately 0.051--0.055~s (GA=False) to about 0.094~s (GA=True), and extraction time from approximately 0.031--0.034~s to about 0.055~s. Importantly, the narrow confidence intervals for runtime metrics indicate predictable and stable computational behavior.Overall, the $2\times2$ ablation study confirms that GA optimization is the primary contributor to both imperceptibility and statistical stability at BPP = 0.2. While RCF-based feature guidance offers some improvement when GA is disabled, its contribution becomes secondary once adaptive GA optimization is applied.

}




\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $128\times128$.}
\label{tab:summary_rcf_ga_bpp0222}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

RCF=True, GA=True & PSNR(cover,stego)      & 58.223883 & 58.216045 & 58.231722 & 0.056215 / 0.003160 \\
RCF=True, GA=True & SSIM(cover,stego)      & 0.999000  & 0.998938  & 0.999061  & 0.000441 / 0.000000 \\
RCF=True, GA=True & IF(cover,stego)        & 0.996103  & 0.995378  & 0.996828  & 0.005200 / 0.000027 \\
RCF=True, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=True & Embedding\_Time(s)     & 0.094472  & 0.090959  & 0.097985  & 0.025192 / 0.000635 \\
RCF=True, GA=True & Extraction\_Time(s)    & 0.055342  & 0.053243  & 0.057440  & 0.015049 / 0.000226 \\
\hline

RCF=True, GA=False & PSNR(cover,stego)      & 54.412283 & 54.323052 & 54.501515 & 0.639934 / 0.409515 \\
RCF=True, GA=False & SSIM(cover,stego)      & 0.997893  & 0.997777  & 0.998009  & 0.000834 / 0.000001 \\
RCF=True, GA=False & IF(cover,stego)        & 0.994024  & 0.993001  & 0.995048  & 0.007342 / 0.000054 \\
RCF=True, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000001 / 0.000000 \\
RCF=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=False & Embedding\_Time(s)     & 0.055266  & 0.052935  & 0.057596  & 0.016712 / 0.000279 \\
RCF=True, GA=False & Extraction\_Time(s)    & 0.033696  & 0.032200  & 0.035191  & 0.010727 / 0.000115 \\
\hline

RCF=False, GA=True & PSNR(cover,stego)      & 58.228887 & 58.224250 & 58.233524 & 0.033255 / 0.001106 \\
RCF=False, GA=True & SSIM(cover,stego)      & 0.999001  & 0.998940  & 0.999062  & 0.000440 / 0.000000 \\
RCF=False, GA=True & IF(cover,stego)        & 0.996105  & 0.995380  & 0.996830  & 0.005200 / 0.000027 \\
RCF=False, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=True & Embedding\_Time(s)     & 0.093829  & 0.090447  & 0.097210  & 0.024251 / 0.000588 \\
RCF=False, GA=True & Extraction\_Time(s)    & 0.055130  & 0.053122  & 0.057138  & 0.014402 / 0.000207 \\
\hline

RCF=False, GA=False & PSNR(cover,stego)      & 54.445384 & 54.402733 & 54.488034 & 0.305875 / 0.093559 \\
RCF=False, GA=False & SSIM(cover,stego)      & 0.997812  & 0.997688  & 0.997935  & 0.000887 / 0.000001 \\
RCF=False, GA=False & IF(cover,stego)        & 0.994036  & 0.992954  & 0.995118  & 0.007758 / 0.000060 \\
RCF=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
RCF=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=False & Embedding\_Time(s)     & 0.050800  & 0.048944  & 0.052656  & 0.013311 / 0.000177 \\
RCF=False, GA=False & Extraction\_Time(s)    & 0.031170  & 0.030020  & 0.032320  & 0.008246 / 0.000068 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_rcf_ga_bpp04} reports a $2\times2$ ablation study evaluating the contribution of RCF guidance and GA-based optimization at a high payload (BPP = 0.4) over $N=200$ cover images with a secret size of $128\times128$. As expected under this higher embedding rate, imperceptibility decreases compared to lower BPP settings; nonetheless, the results highlight that GA remains the dominant factor in preserving both visual quality and statistical stability.Across the GA-enabled configurations, imperceptibility improves substantially relative to their GA-disabled counterparts. Specifically, \textit{RCF=True, GA=True} achieves PSNR $\approx 55.00$~dB and SSIM $\approx 0.99809$, whereas disabling GA under the same RCF setting (\textit{RCF=True, GA=False}) reduces PSNR to $\approx 51.17$~dB and SSIM to $\approx 0.99566$. A similar gap is observed when RCF is disabled: \textit{RCF=False, GA=True} attains PSNR $\approx 55.15$~dB and SSIM $\approx 0.99813$, while \textit{RCF=False, GA=False} remains near PSNR $\approx 51.38$~dB and SSIM $\approx 0.99563$. The consistent $\sim$4~dB gain in PSNR and the clear SSIM/IF improvements indicate that GA provides effective adaptive control over embedding placement and intensity, which becomes more critical as payload increases.

}

{\color{blue}
From a statistical reliability perspective, GA also markedly reduces dispersion. For example, under \textit{RCF=False, GA=True}, PSNR exhibits an extremely tight 95\% CI (width $\approx 0.006$~dB) and a very small standard deviation ($\approx 0.022$), reflecting highly stable performance across covers. In contrast, \textit{RCF=True, GA=True} shows a wider CI and higher standard deviation (PSNR std $\approx 0.390$), suggesting that incorporating RCF features at this payload introduces additional variability that depends on cover content characteristics. Importantly, despite this variability, the mean performance remains comparable to the non-RCF GA setting, implying that RCF contributes marginally in mean quality but may increase sensitivity across diverse covers at BPP = 0.4.Regarding robustness of decoding, SSIM$(\text{secret},\text{extracted})$ remains exactly $1.0$ with zero variance for all settings, confirming lossless recovery even under high payload conditions. Computationally, GA introduces a clear overhead: embedding time increases from $\approx 0.11$~s (GA=False) to $\approx 0.20$~s (GA=True), and extraction time increases from $\approx 0.07$~s to $\approx 0.115$~s. Nevertheless, the associated confidence intervals for runtime remain relatively narrow, indicating predictable computational cost.Overall, the ablation results at BPP = 0.4 demonstrate that GA is the primary mechanism sustaining imperceptibility and reducing statistical spread under high payload embedding. RCF guidance does not significantly improve mean performance under GA, and may slightly increase cross-cover variability, suggesting that its contribution becomes secondary when strong adaptive optimization is already applied.

}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $128\times128$.}
\label{tab:summary_rcf_ga_bpp04}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

RCF=True, GA=True & PSNR(cover,stego)      & 55.002498 & 54.948186 & 55.056810 & 0.389507 / 0.151716 \\
RCF=True, GA=True & SSIM(cover,stego)      & 0.998089  & 0.997968  & 0.998211  & 0.000872 / 0.000001 \\
RCF=True, GA=True & IF(cover,stego)        & 0.994369  & 0.993331  & 0.995407  & 0.007446 / 0.000055 \\
RCF=True, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
RCF=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=True & Embedding\_Time(s)     & 0.202217  & 0.193151  & 0.211283  & 0.065019 / 0.004227 \\
RCF=True, GA=True & Extraction\_Time(s)    & 0.117345  & 0.112135  & 0.122555  & 0.037362 / 0.001396 \\
\hline

RCF=True, GA=False & PSNR(cover,stego)      & 51.168469 & 51.100412 & 51.236526 & 0.488079 / 0.238222 \\
RCF=True, GA=False & SSIM(cover,stego)      & 0.995659  & 0.995413  & 0.995905  & 0.001765 / 0.000003 \\
RCF=True, GA=False & IF(cover,stego)        & 0.991335  & 0.989826  & 0.992843  & 0.010817 / 0.000117 \\
RCF=True, GA=False & MSE(cover,stego)       & 0.000008  & 0.000008  & 0.000008  & 0.000001 / 0.000000 \\
RCF=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=False & Embedding\_Time(s)     & 0.115532  & 0.110131  & 0.120933  & 0.038735 / 0.001500 \\
RCF=True, GA=False & Extraction\_Time(s)    & 0.071766  & 0.068156  & 0.075375  & 0.025886 / 0.000670 \\
\hline

RCF=False, GA=True & PSNR(cover,stego)      & 55.151308 & 55.148294 & 55.154323 & 0.021621 / 0.000467 \\
RCF=False, GA=True & SSIM(cover,stego)      & 0.998131  & 0.998015  & 0.998247  & 0.000832 / 0.000001 \\
RCF=False, GA=True & IF(cover,stego)        & 0.994447  & 0.993411  & 0.995483  & 0.007428 / 0.000055 \\
RCF=False, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
RCF=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=True & Embedding\_Time(s)     & 0.197547  & 0.189547  & 0.205548  & 0.057380 / 0.003292 \\
RCF=False, GA=True & Extraction\_Time(s)    & 0.114673  & 0.109834  & 0.119512  & 0.034703 / 0.001204 \\
\hline

RCF=False, GA=False & PSNR(cover,stego)      & 51.381385 & 51.339265 & 51.423505 & 0.302069 / 0.091246 \\
RCF=False, GA=False & SSIM(cover,stego)      & 0.995625  & 0.995369  & 0.995880  & 0.001833 / 0.000003 \\
RCF=False, GA=False & IF(cover,stego)        & 0.991505  & 0.989954  & 0.993056  & 0.011121 / 0.000124 \\
RCF=False, GA=False & MSE(cover,stego)       & 0.000007  & 0.000007  & 0.000007  & 0.000001 / 0.000000 \\
RCF=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=False & Embedding\_Time(s)     & 0.109995  & 0.105169  & 0.114820  & 0.034607 / 0.001198 \\
RCF=False, GA=False & Extraction\_Time(s)    & 0.069435  & 0.065950  & 0.072920  & 0.024993 / 0.000625 \\
\hline

\end{tabular}%
}
\end{table}

Table~\ref{tab:summary_rcf_ga_bpp01_fast222} summarizes the performance of the proposed framework at a low embedding rate (BPP = 0.1) using a large secret image of size \(512 \times 512\) across 200 cover images. The results clearly indicate that the Genetic Algorithm (GA) is the primary factor driving performance improvements. When GA is enabled, the system achieves consistently high visual fidelity, with PSNR values around 61~dB, SSIM values exceeding 0.99948, and MSE on the order of \(10^{-6}\), regardless of whether RCF is used. In contrast, disabling GA results in a noticeable drop in PSNR to approximately 57.4--57.6~dB and increased variability across images. The contribution of RCF is comparatively marginal, as its effect becomes negligible when GA is active, although it provides a slight benefit in the absence of GA. All configurations achieve perfect secret recovery with \(\mathrm{SSIM(secret,extracted)} = 1.0000\), confirming lossless extraction even for large secret images. While GA introduces additional computational cost, the embedding and extraction times remain low (below 0.04~s and 0.03~s, respectively), demonstrating that the proposed approach is both effective and computationally efficient at low payload rates.

\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:summary_rcf_ga_bpp01_fast222}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

RCF=True, GA=True & PSNR(cover,stego)      & 61.158827 & 61.145404 & 61.172250 & 0.096266 / 0.009267 \\
RCF=True, GA=True & SSIM(cover,stego)      & 0.999484  & 0.999453  & 0.999516  & 0.000225 / 0.000000 \\
RCF=True, GA=True & IF(cover,stego)        & 0.997219  & 0.996703  & 0.997736  & 0.003704 / 0.000014 \\
RCF=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
RCF=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=True & Embedding\_Time(s)     & 0.034532  & 0.033419  & 0.035646  & 0.007987 / 0.000064 \\
RCF=True, GA=True & Extraction\_Time(s)    & 0.020508  & 0.019781  & 0.021235  & 0.005213 / 0.000027 \\
\hline

RCF=True, GA=False & PSNR(cover,stego)      & 57.636240 & 57.492088 & 57.780392 & 1.033804 / 1.068751 \\
RCF=True, GA=False & SSIM(cover,stego)      & 0.999022  & 0.998968  & 0.999077  & 0.000391 / 0.000000 \\
RCF=True, GA=False & IF(cover,stego)        & 0.995867  & 0.995187  & 0.996547  & 0.004876 / 0.000024 \\
RCF=True, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=False & Embedding\_Time(s)     & 0.020761  & 0.019838  & 0.021684  & 0.006619 / 0.000044 \\
RCF=True, GA=False & Extraction\_Time(s)    & 0.013543  & 0.013002  & 0.014084  & 0.003878 / 0.000015 \\
\hline

RCF=False, GA=True & PSNR(cover,stego)      & 61.173279 & 61.168837 & 61.177721 & 0.031854 / 0.001015 \\
RCF=False, GA=True & SSIM(cover,stego)      & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
RCF=False, GA=True & IF(cover,stego)        & 0.997225  & 0.996709  & 0.997740  & 0.003697 / 0.000014 \\
RCF=False, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
RCF=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=True & Embedding\_Time(s)     & 0.032336  & 0.031558  & 0.033114  & 0.005577 / 0.000031 \\
RCF=False, GA=True & Extraction\_Time(s)    & 0.018933  & 0.018411  & 0.019455  & 0.003745 / 0.000014 \\
\hline

RCF=False, GA=False & PSNR(cover,stego)      & 57.419442 & 57.377407 & 57.461477 & 0.301458 / 0.090877 \\
RCF=False, GA=False & SSIM(cover,stego)      & 0.998940  & 0.998881  & 0.999000  & 0.000425 / 0.000000 \\
RCF=False, GA=False & IF(cover,stego)        & 0.995766  & 0.994998  & 0.996534  & 0.005510 / 0.000030 \\
RCF=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=False & Embedding\_Time(s)     & 0.017535  & 0.017019  & 0.018050  & 0.003699 / 0.000014 \\
RCF=False, GA=False & Extraction\_Time(s)    & 0.011401  & 0.011064  & 0.011738  & 0.002417 / 0.000006 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_rcf_ga_bpp023310} presents a $2\times2$ ablation study analyzing the effect of RCF-based feature guidance and Genetic Algorithm (GA) optimization at BPP = 0.1 across $N=200$ cover images, while embedding a full-size secret image of $512\times512$. Despite the significantly increased secret dimensionality, the results demonstrate that the proposed framework maintains excellent imperceptibility and statistical stability, particularly when GA optimization is enabled.For GA-enabled configurations, both \textit{RCF=True, GA=True} and \textit{RCF=False, GA=True} achieve very high visual quality, with PSNR values around 61.16--61.17~dB and SSIM values exceeding 0.99948. The associated 95\% confidence intervals are narrow and the standard deviations remain low, indicating that high imperceptibility is consistently preserved across all cover images. The negligible difference between enabling and disabling RCF under GA optimization suggests that, at this low payload, GA-driven adaptive optimization dominates performance stability, while explicit RCF guidance provides only marginal additional benefit.

}

{\color{blue}
In contrast, disabling GA optimization leads to a noticeable reduction in mean imperceptibility and increased variability. Specifically, \textit{RCF=True, GA=False} and \textit{RCF=False, GA=False} exhibit PSNR values around 57.6~dB with substantially larger confidence intervals and higher variance, reflecting sensitivity to cover content even when advanced RCF features are employed. These findings confirm that fixed embedding parameters are insufficient to guarantee stable performance when embedding large secret images.Across all ablation configurations, secret recovery remains perfect, as indicated by SSIM$(\text{secret}, \text{extracted}) = 1.0$ with zero variance, confirming deterministic and lossless extraction even for full-size secrets. From a computational perspective, the results highlight an important trade-off: while GA optimization introduces moderate overhead compared to GA-disabled cases, the absolute runtime remains low. Embedding time increases from approximately 0.018--0.021~s (GA=False) to about 0.032--0.035~s (GA=True), and extraction time increases from approximately 0.011--0.014~s to about 0.019--0.021~s. The narrow confidence intervals of runtime metrics indicate predictable and stable computational behavior.Overall, the $2\times2$ ablation study confirms that GA optimization is the primary contributor to imperceptibility and statistical robustness at BPP = 0.1, even when embedding a full-size secret image. While RCF-based feature guidance yields modest improvements when GA is disabled, its role becomes secondary once adaptive GA optimization is applied.

}

\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:summary_rcf_ga_bpp023310}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

RCF=True, GA=True & PSNR(cover,stego)      & 58.221913 & 58.213921 & 58.229905 & 0.057314 / 0.003285 \\
RCF=True, GA=True & SSIM(cover,stego)      & 0.998999  & 0.998937  & 0.999060  & 0.000443 / 0.000000 \\
RCF=True, GA=True & IF(cover,stego)        & 0.996102  & 0.995377  & 0.996826  & 0.005199 / 0.000027 \\
RCF=True, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=True & Embedding\_Time(s)     & 0.063258  & 0.061288  & 0.065228  & 0.014128 / 0.000200 \\
RCF=True, GA=True & Extraction\_Time(s)    & 0.035552  & 0.034534  & 0.036570  & 0.007300 / 0.000053 \\
\hline

RCF=True, GA=False & PSNR(cover,stego)      & 54.423605 & 54.334616 & 54.512595 & 0.638203 / 0.407303 \\
RCF=True, GA=False & SSIM(cover,stego)      & 0.997897  & 0.997781  & 0.998013  & 0.000833 / 0.000001 \\
RCF=True, GA=False & IF(cover,stego)        & 0.994032  & 0.993010  & 0.995055  & 0.007334 / 0.000054 \\
RCF=True, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000001 / 0.000000 \\
RCF=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=False & Embedding\_Time(s)     & 0.035279  & 0.034025  & 0.036534  & 0.008995 / 0.000081 \\
RCF=True, GA=False & Extraction\_Time(s)    & 0.021937  & 0.021193  & 0.022681  & 0.005333 / 0.000028 \\
\hline

RCF=False, GA=True & PSNR(cover,stego)      & 58.227709 & 58.223290 & 58.232129 & 0.031697 / 0.001005 \\
RCF=False, GA=True & SSIM(cover,stego)      & 0.999000  & 0.998939  & 0.999062  & 0.000440 / 0.000000 \\
RCF=False, GA=True & IF(cover,stego)        & 0.996105  & 0.995381  & 0.996829  & 0.005193 / 0.000027 \\
RCF=False, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
RCF=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=True & Embedding\_Time(s)     & 0.063022  & 0.061071  & 0.064973  & 0.013990 / 0.000196 \\
RCF=False, GA=True & Extraction\_Time(s)    & 0.035668  & 0.034531  & 0.036806  & 0.008160 / 0.000067 \\
\hline

RCF=False, GA=False & PSNR(cover,stego)      & 54.465649 & 54.422913 & 54.508385 & 0.306486 / 0.093934 \\
RCF=False, GA=False & SSIM(cover,stego)      & 0.997822  & 0.997699  & 0.997945  & 0.000882 / 0.000001 \\
RCF=False, GA=False & IF(cover,stego)        & 0.994052  & 0.992976  & 0.995129  & 0.007723 / 0.000060 \\
RCF=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
RCF=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=False & Embedding\_Time(s)     & 0.033064  & 0.031978  & 0.034151  & 0.007793 / 0.000061 \\
RCF=False, GA=False & Extraction\_Time(s)    & 0.020436  & 0.019760  & 0.021112  & 0.004850 / 0.000024 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_rcf_ga_bpp04_v210} presents a $2\times2$ ablation study evaluating the combined and individual effects of RCF-based guidance and Genetic Algorithm (GA) optimization at a high embedding rate (BPP = 0.4) using full-size secret images ($512\times512$) across $N=200$ cover images. This configuration represents a highly demanding embedding scenario, where both payload and secret dimensionality are maximized.For GA-enabled configurations, both \textit{RCF=True, GA=True} and \textit{RCF=False, GA=True} achieve substantially higher imperceptibility compared to their GA-disabled counterparts. Specifically, PSNR values are maintained around 55~dB and SSIM values around 0.998, demonstrating that GA optimization effectively mitigates the visual degradation induced by high payload embedding. However, a notable difference emerges in statistical stability: while \textit{RCF=True, GA=True} exhibits a relatively larger standard deviation and variance in PSNR (Std $\approx 0.39$, Var $\approx 0.15$), the \textit{RCF=False, GA=True} configuration achieves an extremely tight confidence interval and very low variance (PSNR Var $\approx 5\times10^{-4}$). This indicates that, at BPP = 0.4 with large secrets, the inclusion of RCF guidance may introduce content-dependent variability, whereas GA-only optimization yields more uniform behavior across diverse cover images.
}

{\color{blue}
In contrast, disabling GA results in a pronounced reduction in both mean imperceptibility and robustness. The \textit{RCF=True, GA=False} and \textit{RCF=False, GA=False} configurations exhibit PSNR values around 51~dB and SSIM values near 0.995, accompanied by significantly higher dispersion. These results confirm that fixed embedding strategies are inadequate for maintaining stable performance under high embedding stress, even when advanced RCF features are employed.Across all ablation settings, secret extraction remains perfect, as evidenced by SSIM$(\text{secret},\text{extracted}) = 1.0$ with zero variance, confirming deterministic and lossless recovery. From a computational perspective, GA optimization introduces a moderate overhead: embedding time increases from approximately 0.064--0.069~s (GA=False) to around 0.121~s (GA=True), and extraction time increases from approximately 0.038--0.041~s to about 0.068--0.070~s. Importantly, the confidence intervals for runtime metrics remain narrow, indicating predictable and stable computational costs.Overall, the statistical evidence demonstrates that GA optimization is the primary factor sustaining imperceptibility and robustness at BPP = 0.4 with full-size secret images. While RCF guidance can enhance mean performance, its contribution is offset by increased variability, suggesting that adaptive GA optimization alone provides the most statistically stable solution under extreme embedding conditions.
}

\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:summary_rcf_ga_bpp04_v210}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

RCF=True, GA=True & PSNR(cover,stego)      & 55.000923 & 54.946629 & 55.055217 & 0.389374 / 0.151612 \\
RCF=True, GA=True & SSIM(cover,stego)      & 0.998089  & 0.997967  & 0.998211  & 0.000874 / 0.000001 \\
RCF=True, GA=True & IF(cover,stego)        & 0.994367  & 0.993329  & 0.995405  & 0.007445 / 0.000055 \\
RCF=True, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
RCF=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=True & Embedding\_Time(s)     & 0.121036  & 0.117573  & 0.124500  & 0.024839 / 0.000617 \\
RCF=True, GA=True & Extraction\_Time(s)    & 0.069974  & 0.067745  & 0.072204  & 0.015991 / 0.000256 \\
\hline

RCF=True, GA=False & PSNR(cover,stego)      & 51.165795 & 51.097456 & 51.234134 & 0.490105 / 0.240203 \\
RCF=True, GA=False & SSIM(cover,stego)      & 0.995656  & 0.995410  & 0.995902  & 0.001764 / 0.000003 \\
RCF=True, GA=False & IF(cover,stego)        & 0.991332  & 0.989823  & 0.992842  & 0.010828 / 0.000117 \\
RCF=True, GA=False & MSE(cover,stego)       & 0.000008  & 0.000008  & 0.000008  & 0.000001 / 0.000000 \\
RCF=True, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=True, GA=False & Embedding\_Time(s)     & 0.068677  & 0.066157  & 0.071198  & 0.018077 / 0.000327 \\
RCF=True, GA=False & Extraction\_Time(s)    & 0.041024  & 0.039478  & 0.042569  & 0.011083 / 0.000123 \\
\hline

RCF=False, GA=True & PSNR(cover,stego)      & 55.151402 & 55.148270 & 55.154534 & 0.022464 / 0.000505 \\
RCF=False, GA=True & SSIM(cover,stego)      & 0.998131  & 0.998015  & 0.998247  & 0.000833 / 0.000001 \\
RCF=False, GA=True & IF(cover,stego)        & 0.994447  & 0.993412  & 0.995482  & 0.007423 / 0.000055 \\
RCF=False, GA=True & MSE(cover,stego)       & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
RCF=False, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=True & Embedding\_Time(s)     & 0.121920  & 0.118299  & 0.125542  & 0.025971 / 0.000674 \\
RCF=False, GA=True & Extraction\_Time(s)    & 0.068179  & 0.066145  & 0.070214  & 0.014589 / 0.000213 \\
\hline

RCF=False, GA=False & PSNR(cover,stego)      & 51.376735 & 51.334470 & 51.419000 & 0.303111 / 0.091876 \\
RCF=False, GA=False & SSIM(cover,stego)      & 0.995619  & 0.995363  & 0.995875  & 0.001836 / 0.000003 \\
RCF=False, GA=False & IF(cover,stego)        & 0.991499  & 0.989947  & 0.993052  & 0.011135 / 0.000124 \\
RCF=False, GA=False & MSE(cover,stego)       & 0.000007  & 0.000007  & 0.000007  & 0.000001 / 0.000000 \\
RCF=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
RCF=False, GA=False & Embedding\_Time(s)     & 0.063974  & 0.062113  & 0.065834  & 0.013343 / 0.000178 \\
RCF=False, GA=False & Extraction\_Time(s)    & 0.038199  & 0.037027  & 0.039371  & 0.008405 / 0.000071 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_resnet50_ga_bpp0155rr} reports the performance of the proposed framework at a low embedding rate (BPP = 0.1) using a \(64\times64\) secret image over 200 cover images. The results confirm that the Genetic Algorithm (GA) is the dominant factor in achieving high visual fidelity. When GA is enabled, PSNR values reach approximately 61~dB, with SSIM values exceeding 0.99948 and MSE on the order of \(10^{-6}\), regardless of whether ResNet50 is employed. In contrast, disabling GA leads to a substantial degradation in quality, with PSNR dropping to about 50.3~dB when ResNet50 is enabled and to around 57.4~dB when it is disabled. This indicates that the contribution of ResNet50 is marginal when GA-driven optimization is active. All configurations achieve perfect secret recovery (\(\mathrm{SSIM(secret,extracted)} = 1.0000\)), while GA introduces a moderate but acceptable increase in embedding and extraction times. Overall, the results demonstrate that GA-based adaptive embedding is the key driver of imperceptibility and stability at low payload rates.
}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_resnet50_ga_bpp0155rr}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)        & 61.155891 & 61.147190 & 61.164591 & 0.062397 / 0.003893 \\
ResNet50=True, GA=True & SSIM(cover,stego)        & 0.999484  & 0.999452  & 0.999515  & 0.000224 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)          & 0.997220  & 0.996706  & 0.997734  & 0.003686 / 0.000014 \\
ResNet50=True, GA=True & MSE(cover,stego)         & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)       & 0.052269  & 0.049939  & 0.054599  & 0.016712 / 0.000279 \\
ResNet50=True, GA=True & Extraction\_Time(s)      & 0.032193  & 0.030942  & 0.033445  & 0.008976 / 0.000081 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)       & 50.340816 & 50.129062 & 50.552571 & 1.518623 / 2.306215 \\
ResNet50=True, GA=False & SSIM(cover,stego)       & 0.996608  & 0.996425  & 0.996790  & 0.001306 / 0.000002 \\
ResNet50=True, GA=False & IF(cover,stego)         & 0.990187  & 0.988156  & 0.992217  & 0.014562 / 0.000212 \\
ResNet50=True, GA=False & MSE(cover,stego)        & 0.000010  & 0.000009  & 0.000010  & 0.000003 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)      & 0.019373  & 0.018617  & 0.020130  & 0.005427 / 0.000029 \\
ResNet50=True, GA=False & Extraction\_Time(s)     & 0.013438  & 0.012918  & 0.013958  & 0.003730 / 0.000014 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)       & 61.172317 & 61.167702 & 61.176931 & 0.033095 / 0.001095 \\
ResNet50=False, GA=True & SSIM(cover,stego)       & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)         & 0.997225  & 0.996711  & 0.997739  & 0.003686 / 0.000014 \\
ResNet50=False, GA=True & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)      & 0.051042  & 0.048935  & 0.053150  & 0.015115 / 0.000228 \\
ResNet50=False, GA=True & Extraction\_Time(s)     & 0.031486  & 0.030192  & 0.032779  & 0.009277 / 0.000086 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)      & 57.401460 & 57.359376 & 57.443545 & 0.301814 / 0.091092 \\
ResNet50=False, GA=False & SSIM(cover,stego)      & 0.998939  & 0.998880  & 0.998998  & 0.000425 / 0.000000 \\
ResNet50=False, GA=False & IF(cover,stego)        & 0.995760  & 0.994993  & 0.996527  & 0.005497 / 0.000030 \\
ResNet50=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)     & 0.028831  & 0.027550  & 0.030112  & 0.009186 / 0.000084 \\
ResNet50=False, GA=False & Extraction\_Time(s)    & 0.018738  & 0.017956  & 0.019520  & 0.005610 / 0.000031 \\
\hline

\end{tabular}%
}
\end{table}


{\color{blue}
Table~\ref{tab:summary_resnet50_gaa_bpp015510} reports the performance of the proposed framework at a low embedding rate (BPP = 0.1) using a \(64\times64\) secret image across 200 cover images. The results demonstrate that the Genetic Algorithm (GA) is the dominant factor influencing visual quality and stability. When GA is enabled, the system achieves consistently high cover fidelity with PSNR values around 61~dB, SSIM values exceeding 0.99948, and MSE on the order of \(10^{-6}\), regardless of whether ResNet50 is employed. In contrast, disabling GA leads to a noticeable degradation in quality, with PSNR dropping to approximately 50.3~dB when ResNet50 is enabled and to about 57.4~dB when it is disabled. The contribution of ResNet50 is marginal when GA is active, indicating that GA-driven optimization largely subsumes the benefit of deep texture features at this payload level. All configurations achieve perfect secret recovery (\(\mathrm{SSIM}=1.0000\)), while GA introduces a moderate increase in embedding and extraction times that remains computationally acceptable. Overall, these results confirm that GA-based adaptive embedding is the key driver of high imperceptibility and robust performance at low payloads.
}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_resnet50_gaa_bpp015510}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)        & 61.155891 & 61.147190 & 61.164591 & 0.062397 / 0.003893 \\
ResNet50=True, GA=True & SSIM(cover,stego)        & 0.999484  & 0.999452  & 0.999515  & 0.000224 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)          & 0.997220  & 0.996706  & 0.997734  & 0.003686 / 0.000014 \\
ResNet50=True, GA=True & MSE(cover,stego)         & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)       & 0.052269  & 0.049939  & 0.054599  & 0.016712 / 0.000279 \\
ResNet50=True, GA=True & Extraction\_Time(s)      & 0.032193  & 0.030942  & 0.033445  & 0.008976 / 0.000081 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)       & 50.340816 & 50.129062 & 50.552571 & 1.518623 / 2.306215 \\
ResNet50=True, GA=False & SSIM(cover,stego)       & 0.996608  & 0.996425  & 0.996790  & 0.001306 / 0.000002 \\
ResNet50=True, GA=False & IF(cover,stego)         & 0.990187  & 0.988156  & 0.992217  & 0.014562 / 0.000212 \\
ResNet50=True, GA=False & MSE(cover,stego)        & 0.000010  & 0.000009  & 0.000010  & 0.000003 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)      & 0.019373  & 0.018617  & 0.020130  & 0.005427 / 0.000029 \\
ResNet50=True, GA=False & Extraction\_Time(s)     & 0.013438  & 0.012918  & 0.013958  & 0.003730 / 0.000014 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)       & 61.172317 & 61.167702 & 61.176931 & 0.033095 / 0.001095 \\
ResNet50=False, GA=True & SSIM(cover,stego)       & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)         & 0.997225  & 0.996711  & 0.997739  & 0.003686 / 0.000014 \\
ResNet50=False, GA=True & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)      & 0.051042  & 0.048935  & 0.053150  & 0.015115 / 0.000228 \\
ResNet50=False, GA=True & Extraction\_Time(s)     & 0.031486  & 0.030192  & 0.032779  & 0.009277 / 0.000086 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)      & 57.401460 & 57.359376 & 57.443545 & 0.301814 / 0.091092 \\
ResNet50=False, GA=False & SSIM(cover,stego)      & 0.998939  & 0.998880  & 0.998998  & 0.000425 / 0.000000 \\
ResNet50=False, GA=False & IF(cover,stego)        & 0.995760  & 0.994993  & 0.996527  & 0.005497 / 0.000030 \\
ResNet50=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)     & 0.028831  & 0.027550  & 0.030112  & 0.009186 / 0.000084 \\
ResNet50=False, GA=False & Extraction\_Time(s)    & 0.018738  & 0.017956  & 0.019520  & 0.005610 / 0.000031 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_resnet50_ga_bpp01664} summarizes the performance of the proposed framework at a low embedding rate (BPP = 0.1) using a \(64\times64\) secret image across 200 cover images. The results clearly indicate that the Genetic Algorithm (GA) is the dominant factor influencing cover image quality and stability. When GA is enabled, the system achieves consistently high visual fidelity, with PSNR values around 61~dB, SSIM values exceeding 0.99948, and MSE on the order of \(10^{-6}\), regardless of whether ResNet50 is employed. In contrast, disabling GA results in a substantial degradation in quality, with PSNR dropping to approximately 50.3~dB when ResNet50 is enabled and to about 57.4~dB when ResNet50 is disabled. This demonstrates that the contribution of ResNet50 is secondary when GA-driven optimization is active. All configurations achieve perfect secret recovery with \(\mathrm{SSIM(secret,extracted)} = 1.0000\), confirming lossless extraction at this payload level. Although GA introduces additional computational cost, the embedding and extraction times remain within practical limits, confirming the efficiency of the proposed approach for low-capacity steganographic scenarios.

}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_resnet50_ga_bpp01664}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)        & 61.155891 & 61.147190 & 61.164591 & 0.062397 / 0.003893 \\
ResNet50=True, GA=True & SSIM(cover,stego)        & 0.999484  & 0.999452  & 0.999515  & 0.000224 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)          & 0.997220  & 0.996706  & 0.997734  & 0.003686 / 0.000014 \\
ResNet50=True, GA=True & MSE(cover,stego)         & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)       & 0.052269  & 0.049939  & 0.054599  & 0.016712 / 0.000279 \\
ResNet50=True, GA=True & Extraction\_Time(s)      & 0.032193  & 0.030942  & 0.033445  & 0.008976 / 0.000081 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)       & 50.340816 & 50.129062 & 50.552571 & 1.518623 / 2.306215 \\
ResNet50=True, GA=False & SSIM(cover,stego)       & 0.996608  & 0.996425  & 0.996790  & 0.001306 / 0.000002 \\
ResNet50=True, GA=False & IF(cover,stego)         & 0.990187  & 0.988156  & 0.992217  & 0.014562 / 0.000212 \\
ResNet50=True, GA=False & MSE(cover,stego)        & 0.000010  & 0.000009  & 0.000010  & 0.000003 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)      & 0.019373  & 0.018617  & 0.020130  & 0.005427 / 0.000029 \\
ResNet50=True, GA=False & Extraction\_Time(s)     & 0.013438  & 0.012918  & 0.013958  & 0.003730 / 0.000014 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)       & 61.172317 & 61.167702 & 61.176931 & 0.033095 / 0.001095 \\
ResNet50=False, GA=True & SSIM(cover,stego)       & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)         & 0.997225  & 0.996711  & 0.997739  & 0.003686 / 0.000014 \\
ResNet50=False, GA=True & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)      & 0.051042  & 0.048935  & 0.053150  & 0.015115 / 0.000228 \\
ResNet50=False, GA=True & Extraction\_Time(s)     & 0.031486  & 0.030192  & 0.032779  & 0.009277 / 0.000086 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)      & 57.401460 & 57.359376 & 57.443545 & 0.301814 / 0.091092 \\
ResNet50=False, GA=False & SSIM(cover,stego)      & 0.998939  & 0.998880  & 0.998998  & 0.000425 / 0.000000 \\
ResNet50=False, GA=False & IF(cover,stego)        & 0.995760  & 0.994993  & 0.996527  & 0.005497 / 0.000030 \\
ResNet50=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)     & 0.028831  & 0.027550  & 0.030112  & 0.009186 / 0.000084 \\
ResNet50=False, GA=False & Extraction\_Time(s)    & 0.018738  & 0.017956  & 0.019520  & 0.005610 / 0.000031 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary1_resnet50_ga_bpp019910} presents a $2\times2$ ablation study evaluating the effect of ResNet50-based guidance and Genetic Algorithm (GA) optimization at BPP = 0.1 with a secret image size of $64\times64$. The results show that GA optimization is the primary factor driving performance improvements, yielding PSNR values above 61~dB, SSIM greater than 0.999, and extremely low MSE with narrow 95\% confidence intervals, indicating high consistency across all cover images. In contrast, disabling GA leads to a significant degradation in imperceptibility and increased variance, particularly when ResNet50 guidance is used. When GA is enabled, the impact of ResNet50 on the mean performance becomes marginal, while all configurations achieve perfect secret recovery with SSIM$(\text{secret},\text{extracted})=1.0$.


}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary1_resnet50_ga_bpp019910}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)        & 61.155891 & 61.147190 & 61.164591 & 0.062397 / 0.003893 \\
ResNet50=True, GA=True & SSIM(cover,stego)        & 0.999484  & 0.999452  & 0.999515  & 0.000224 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)          & 0.997220  & 0.996706  & 0.997734  & 0.003686 / 0.000014 \\
ResNet50=True, GA=True & MSE(cover,stego)         & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)       & 0.052269  & 0.049939  & 0.054599  & 0.016712 / 0.000279 \\
ResNet50=True, GA=True & Extraction\_Time(s)      & 0.032193  & 0.030942  & 0.033445  & 0.008976 / 0.000081 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)       & 50.340816 & 50.129062 & 50.552571 & 1.518623 / 2.306215 \\
ResNet50=True, GA=False & SSIM(cover,stego)       & 0.996608  & 0.996425  & 0.996790  & 0.001306 / 0.000002 \\
ResNet50=True, GA=False & IF(cover,stego)         & 0.990187  & 0.988156  & 0.992217  & 0.014562 / 0.000212 \\
ResNet50=True, GA=False & MSE(cover,stego)        & 0.000010  & 0.000009  & 0.000010  & 0.000003 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)      & 0.019373  & 0.018617  & 0.020130  & 0.005427 / 0.000029 \\
ResNet50=True, GA=False & Extraction\_Time(s)     & 0.013438  & 0.012918  & 0.013958  & 0.003730 / 0.000014 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)       & 61.172317 & 61.167702 & 61.176931 & 0.033095 / 0.001095 \\
ResNet50=False, GA=True & SSIM(cover,stego)       & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)         & 0.997225  & 0.996711  & 0.997739  & 0.003686 / 0.000014 \\
ResNet50=False, GA=True & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)      & 0.051042  & 0.048935  & 0.053150  & 0.015115 / 0.000228 \\
ResNet50=False, GA=True & Extraction\_Time(s)     & 0.031486  & 0.030192  & 0.032779  & 0.009277 / 0.000086 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)      & 57.401460 & 57.359376 & 57.443545 & 0.301814 / 0.091092 \\
ResNet50=False, GA=False & SSIM(cover,stego)      & 0.998939  & 0.998880  & 0.998998  & 0.000425 / 0.000000 \\
ResNet50=False, GA=False & IF(cover,stego)        & 0.995760  & 0.994993  & 0.996527  & 0.005497 / 0.000030 \\
ResNet50=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)     & 0.028831  & 0.027550  & 0.030112  & 0.009186 / 0.000084 \\
ResNet50=False, GA=False & Extraction\_Time(s)    & 0.018738  & 0.017956  & 0.019520  & 0.005610 / 0.000031 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_resnet50_ga_bpp0288} reports a $2\times2$ ablation study assessing the impact of ResNet50-based guidance and Genetic Algorithm (GA) optimization at BPP = 0.2 across $N=200$ cover images with a secret image size of $64\times64$. Compared to lower payload settings, a moderate decrease in imperceptibility is observed; however, the results clearly indicate that GA optimization remains the dominant factor in preserving both performance and statistical robustness.When GA is enabled, both configurations achieve high visual quality, with PSNR values around 60.13--60.16~dB, SSIM exceeding 0.9993, and MSE on the order of $10^{-6}$. The corresponding 95\% confidence intervals are very narrow, particularly for the ResNet50=False configuration, indicating highly consistent performance across all cover images. In contrast, disabling GA leads to a substantial degradation in imperceptibility, where PSNR drops to approximately 49.39~dB for ResNet50=True and 56.39~dB for ResNet50=False, accompanied by significantly larger standard deviations and variances, reflecting strong sensitivity to cover content.


}


{\color{blue}
Comparing the GA-enabled configurations shows that the inclusion of ResNet50 guidance has a negligible effect on the mean imperceptibility metrics once GA is applied, as the PSNR difference remains below 0.05~dB. Nevertheless, the ResNet50=True configuration exhibits a slightly higher variability, suggesting that deep feature guidance may introduce limited cross-image fluctuations without affecting the average quality. From a computational perspective, GA approximately doubles both embedding and extraction times compared to GA-disabled settings, while maintaining narrow confidence intervals, indicating predictable computational overhead.Overall, the ablation results confirm that GA optimization is essential for achieving stable and high-quality steganographic embedding at BPP = 0.2, whereas ResNet50 guidance plays a secondary role that does not significantly alter the average performance under GA-driven optimization.


}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_resnet50_ga_bpp0288}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)        & 60.131507 & 60.118654 & 60.144361 & 0.092179 / 0.008497 \\
ResNet50=True, GA=True & SSIM(cover,stego)        & 0.999346  & 0.999306  & 0.999386  & 0.000287 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)          & 0.996873  & 0.996294  & 0.997452  & 0.004151 / 0.000017 \\
ResNet50=True, GA=True & MSE(cover,stego)         & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)       & 0.068160  & 0.065078  & 0.071242  & 0.022104 / 0.000489 \\
ResNet50=True, GA=True & Extraction\_Time(s)      & 0.041637  & 0.039902  & 0.043371  & 0.012440 / 0.000155 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)       & 49.387025 & 49.171392 & 49.602658 & 1.546437 / 2.391467 \\
ResNet50=True, GA=False & SSIM(cover,stego)       & 0.995659  & 0.995427  & 0.995891  & 0.001664 / 0.000003 \\
ResNet50=True, GA=False & IF(cover,stego)         & 0.989076  & 0.986854  & 0.991298  & 0.015936 / 0.000254 \\
ResNet50=True, GA=False & MSE(cover,stego)        & 0.000012  & 0.000012  & 0.000013  & 0.000004 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)      & 0.025364  & 0.024260  & 0.026468  & 0.007918 / 0.000063 \\
ResNet50=True, GA=False & Extraction\_Time(s)     & 0.016874  & 0.016151  & 0.017597  & 0.005185 / 0.000027 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)       & 60.164466 & 60.160063 & 60.168870 & 0.031582 / 0.000997 \\
ResNet50=False, GA=True & SSIM(cover,stego)       & 0.999353  & 0.999313  & 0.999392  & 0.000283 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)         & 0.996884  & 0.996305  & 0.997462  & 0.004149 / 0.000017 \\
ResNet50=False, GA=True & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)      & 0.067186  & 0.064200  & 0.070172  & 0.021416 / 0.000459 \\
ResNet50=False, GA=True & Extraction\_Time(s)     & 0.041361  & 0.039534  & 0.043188  & 0.013100 / 0.000172 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)      & 56.393780 & 56.351745 & 56.435815 & 0.301457 / 0.090877 \\
ResNet50=False, GA=False & SSIM(cover,stego)      & 0.998649  & 0.998572  & 0.998726  & 0.000551 / 0.000000 \\
ResNet50=False, GA=False & IF(cover,stego)        & 0.995235  & 0.994370  & 0.996101  & 0.006205 / 0.000039 \\
ResNet50=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)     & 0.037626  & 0.035945  & 0.039307  & 0.012054 / 0.000145 \\
ResNet50=False, GA=False & Extraction\_Time(s)    & 0.024052  & 0.023044  & 0.025060  & 0.007226 / 0.000052 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary2_resnet50_ga_bpp0433} presents a $2\times2$ ablation study evaluating the effects of ResNet50-based guidance and Genetic Algorithm (GA) optimization at a high payload setting (BPP = 0.4) across $N=200$ cover images with a secret image size of $64\times64$. As expected at higher embedding rates, imperceptibility decreases relative to lower BPP scenarios; however, the results clearly show that GA optimization remains the dominant factor for both performance and statistical stability.When GA is enabled, both configurations achieve PSNR values around 60.13--60.16~dB with SSIM above 0.9993 and MSE on the order of $10^{-6}$. The associated 95\% confidence intervals are narrow, particularly for the ResNet50=False case, indicating consistent imperceptibility across all covers. In contrast, disabling GA leads to a substantial degradation in quality: PSNR drops to approximately 49.39~dB for ResNet50=True and 56.39~dB for ResNet50=False, accompanied by markedly larger standard deviations and variances, reflecting strong sensitivity to cover content under fixed embedding parameters.


}


{\color{blue}
Comparing the GA-enabled configurations reveals that ResNet50 guidance has a negligible impact on the mean imperceptibility once GA is applied (PSNR difference $<0.05$~dB). Nevertheless, the ResNet50=True setting exhibits slightly higher variability, suggesting that deep-feature guidance may introduce limited cross-cover fluctuations without affecting the overall average quality. From a computational standpoint, GA increases both embedding and extraction times by roughly a factor of two compared to GA-disabled configurations, while maintaining relatively tight confidence intervals, indicating predictable computational overhead.Across all ablation settings, perfect secret recovery is achieved, with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance. Overall, the results confirm that GA optimization is essential for maintaining high imperceptibility and robustness at BPP = 0.4, whereas ResNet50 guidance plays a secondary role that does not significantly alter the mean performance under GA-driven optimization.


}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary2_resnet50_ga_bpp0433}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)      & 60.131507 & 60.118654 & 60.144361 & 0.092179 / 0.008497 \\
ResNet50=True, GA=True & SSIM(cover,stego)      & 0.999346  & 0.999306  & 0.999386  & 0.000287 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)        & 0.996873  & 0.996294  & 0.997452  & 0.004151 / 0.000017 \\
ResNet50=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)     & 0.065781  & 0.063000  & 0.068563  & 0.019949 / 0.000398 \\
ResNet50=True, GA=True & Extraction\_Time(s)    & 0.040564  & 0.038824  & 0.042305  & 0.012483 / 0.000156 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)     & 49.387025 & 49.171392 & 49.602658 & 1.546437 / 2.391467 \\
ResNet50=True, GA=False & SSIM(cover,stego)     & 0.995659  & 0.995427  & 0.995891  & 0.001664 / 0.000003 \\
ResNet50=True, GA=False & IF(cover,stego)       & 0.989076  & 0.986854  & 0.991298  & 0.015936 / 0.000254 \\
ResNet50=True, GA=False & MSE(cover,stego)      & 0.000012  & 0.000012  & 0.000013  & 0.000004 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)& 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)    & 0.025261  & 0.024070  & 0.026453  & 0.008544 / 0.000073 \\
ResNet50=True, GA=False & Extraction\_Time(s)   & 0.016794  & 0.016054  & 0.017534  & 0.005306 / 0.000028 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)     & 60.164466 & 60.160063 & 60.168870 & 0.031582 / 0.000997 \\
ResNet50=False, GA=True & SSIM(cover,stego)     & 0.999353  & 0.999313  & 0.999392  & 0.000283 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)       & 0.996884  & 0.996305  & 0.997462  & 0.004149 / 0.000017 \\
ResNet50=False, GA=True & MSE(cover,stego)      & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)& 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)    & 0.064796  & 0.062072  & 0.067519  & 0.019534 / 0.000382 \\
ResNet50=False, GA=True & Extraction\_Time(s)   & 0.039128  & 0.037545  & 0.040711  & 0.011353 / 0.000129 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)    & 56.393780 & 56.351745 & 56.435815 & 0.301457 / 0.090877 \\
ResNet50=False, GA=False & SSIM(cover,stego)    & 0.998649  & 0.998572  & 0.998726  & 0.000551 / 0.000000 \\
ResNet50=False, GA=False & IF(cover,stego)      & 0.995235  & 0.994370  & 0.996101  & 0.006205 / 0.000039 \\
ResNet50=False, GA=False & MSE(cover,stego)     & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted)&1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)   & 0.036342  & 0.034765  & 0.037919  & 0.011308 / 0.000128 \\
ResNet50=False, GA=False & Extraction\_Time(s)  & 0.023234  & 0.022293  & 0.024174  & 0.006745 / 0.000045 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_resnet50_ga_bpp01_v255} reports a $2\times2$ ablation study assessing the impact of ResNet50-based guidance and Genetic Algorithm (GA) optimization at a low payload (BPP = 0.1) across $N=200$ cover images while embedding a larger secret image of $128\times128$. The results indicate that GA optimization is the principal driver of imperceptibility and statistical robustness.When GA is enabled, both configurations achieve very high visual quality, with PSNR exceeding 61~dB, SSIM above 0.99948, and MSE on the order of $10^{-6}$. The associated 95\% confidence intervals are extremely narrow (PSNR CI width $\approx 0.013$~dB for ResNet50=True and $\approx 0.009$~dB for ResNet50=False), confirming highly consistent performance across all covers. In contrast, disabling GA results in a substantial degradation, where PSNR drops to approximately 50.34~dB for ResNet50=True and 57.40~dB for ResNet50=False, accompanied by significantly larger standard deviations and variances, reflecting strong sensitivity to cover content.


}

{\color{blue}
Comparing the GA-enabled cases shows that ResNet50 guidance has a negligible effect on the mean imperceptibility once GA is applied (PSNR difference $<0.01$~dB). However, the ResNet50=True configuration exhibits slightly higher variability than ResNet50=False, suggesting that deep-feature guidance may introduce minor cross-cover fluctuations without affecting the overall average quality. From a computational perspective, GA approximately doubles the embedding and extraction times compared to GA-disabled settings, while maintaining tight confidence intervals, indicating predictable and stable computational overhead. Across all configurations, perfect secret recovery is achieved with SSIM$(\text{secret},\text{extracted})=1.0$ and zero variance.


}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $128\times128$.}
\label{tab:summary_resnet50_ga_bpp01_v255}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)      & 61.164920 & 61.158609 & 61.171231 & 0.045263 / 0.002049 \\
ResNet50=True, GA=True & SSIM(cover,stego)      & 0.999485  & 0.999454  & 0.999516  & 0.000224 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)        & 0.997222  & 0.996706  & 0.997738  & 0.003701 / 0.000014 \\
ResNet50=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)     & 0.054131  & 0.051767  & 0.056494  & 0.016949 / 0.000287 \\
ResNet50=True, GA=True & Extraction\_Time(s)    & 0.032966  & 0.031685  & 0.034246  & 0.009185 / 0.000084 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)     & 50.340022 & 50.128603 & 50.551441 & 1.516218 / 2.298916 \\
ResNet50=True, GA=False & SSIM(cover,stego)     & 0.996610  & 0.996428  & 0.996791  & 0.001303 / 0.000002 \\
ResNet50=True, GA=False & IF(cover,stego)       & 0.990189  & 0.988163  & 0.992215  & 0.014529 / 0.000211 \\
ResNet50=True, GA=False & MSE(cover,stego)      & 0.000010  & 0.000009  & 0.000010  & 0.000003 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)& 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)    & 0.020800  & 0.019926  & 0.021674  & 0.006270 / 0.000039 \\
ResNet50=True, GA=False & Extraction\_Time(s)   & 0.014106  & 0.013569  & 0.014643  & 0.003853 / 0.000015 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)     & 61.171940 & 61.167321 & 61.176558 & 0.033123 / 0.001097 \\
ResNet50=False, GA=True & SSIM(cover,stego)     & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)       & 0.997224  & 0.996708  & 0.997740  & 0.003701 / 0.000014 \\
ResNet50=False, GA=True & MSE(cover,stego)      & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)& 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)    & 0.051944  & 0.049853  & 0.054035  & 0.014994 / 0.000225 \\
ResNet50=False, GA=True & Extraction\_Time(s)   & 0.031558  & 0.030306  & 0.032810  & 0.008977 / 0.000081 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)    & 57.399868 & 57.357285 & 57.442452 & 0.305392 / 0.093264 \\
ResNet50=False, GA=False & SSIM(cover,stego)    & 0.998937  & 0.998878  & 0.998997  & 0.000426 / 0.000000 \\
ResNet50=False, GA=False & IF(cover,stego)      & 0.995757  & 0.994988  & 0.996527  & 0.005519 / 0.000030 \\
ResNet50=False, GA=False & MSE(cover,stego)     & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted)&1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)   & 0.029295  & 0.028158  & 0.030432  & 0.008153 / 0.000066 \\
ResNet50=False, GA=False & Extraction\_Time(s)  & 0.019171  & 0.018439  & 0.019903  & 0.005250 / 0.000028 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_resnet50_ga_bpp0266} summarizes a $2\times2$ ablation (ResNet50 $\times$ GA) evaluated at BPP$=0.2$ over $N=200$ cover images while embedding a $128\times128$ secret.
Overall, enabling GA yields a clear improvement in imperceptibility and stability compared to GA-disabled settings, while maintaining perfect extraction in all cases.
With GA enabled, PSNR is $\approx 58.19$--$58.23$ dB, SSIM$(\text{cover},\text{stego})\approx0.999$, and MSE is on the order of $10^{-6}$, indicating near-lossless visual quality.
Moreover, the 95\% confidence intervals are very tight (PSNR CI width $\approx 0.029$ dB for ResNet50=True and $\approx 0.009$ dB for ResNet50=False), confirming consistent behavior across covers.


}

{\color{blue}
In contrast, disabling GA causes a notable degradation: PSNR drops to $47.70$ dB (ResNet50=True) and $54.45$ dB (ResNet50=False), accompanied by substantially larger standard deviations (e.g., PSNR std $1.63$ dB for ResNet50=True, GA=False), reflecting higher sensitivity to cover content.Under GA-enabled configurations, ResNet50 has a negligible effect on average quality (PSNR difference $\approx 0.036$ dB; SSIM and IF are nearly identical), suggesting that GA dominates the optimization of embedding locations/strength at this payload.However, the ResNet50=True, GA=True configuration exhibits higher variability than ResNet50=False, GA=True (PSNR std $0.104$ vs.\ $0.033$), implying slightly less cross-cover stability when ResNet50 guidance is used.GA increases runtime notably: embedding time rises from $\sim 0.037$--$0.054$ s (GA=False) to $\sim 0.098$--$0.100$ s (GA=True), and extraction time increases from $\sim 0.024$--$0.034$ s to $\sim 0.059$--$0.060$ s.Nevertheless, runtime confidence intervals remain narrow, indicating predictable overhead.Across all ablations, perfect recovery is achieved with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance, demonstrating reliable extraction at BPP$=0.2$ even when imperceptibility varies.


}

% =========================
% Reproduced Table (same style as before)
% =========================
\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $128\times128$.}
\label{tab:summary_resnet50_ga_bpp0266}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)        & 58.192700 & 58.178191 & 58.207210 & 0.104058 / 0.010828 \\
ResNet50=True, GA=True & SSIM(cover,stego)        & 0.998991  & 0.998929  & 0.999053  & 0.000445 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)          & 0.996088  & 0.995361  & 0.996815  & 0.005216 / 0.000027 \\
ResNet50=True, GA=True & MSE(cover,stego)         & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)       & 0.100173  & 0.095769  & 0.104578  & 0.031588 / 0.000998 \\
ResNet50=True, GA=True & Extraction\_Time(s)      & 0.060077  & 0.057672  & 0.062481  & 0.017245 / 0.000297 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)       & 47.695161 & 47.468237 & 47.922085 & 1.627414 / 2.648477 \\
ResNet50=True, GA=False & SSIM(cover,stego)       & 0.993341  & 0.992988  & 0.993694  & 0.002530 / 0.000006 \\
ResNet50=True, GA=False & IF(cover,stego)         & 0.986823  & 0.984291  & 0.989355  & 0.018162 / 0.000330 \\
ResNet50=True, GA=False & MSE(cover,stego)        & 0.000018  & 0.000017  & 0.000019  & 0.000006 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)      & 0.036996  & 0.035277  & 0.038714  & 0.012322 / 0.000152 \\
ResNet50=True, GA=False & Extraction\_Time(s)     & 0.023908  & 0.022843  & 0.024973  & 0.007638 / 0.000058 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)       & 58.228887 & 58.224250 & 58.233524 & 0.033255 / 0.001106 \\
ResNet50=False, GA=True & SSIM(cover,stego)       & 0.999001  & 0.998940  & 0.999062  & 0.000440 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)         & 0.996105  & 0.995380  & 0.996830  & 0.005200 / 0.000027 \\
ResNet50=False, GA=True & MSE(cover,stego)        & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)      & 0.098482  & 0.094384  & 0.102581  & 0.029392 / 0.000864 \\
ResNet50=False, GA=True & Extraction\_Time(s)     & 0.059126  & 0.056722  & 0.061531  & 0.017246 / 0.000297 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)      & 54.445384 & 54.402733 & 54.488034 & 0.305875 / 0.093559 \\
ResNet50=False, GA=False & SSIM(cover,stego)      & 0.997812  & 0.997688  & 0.997935  & 0.000887 / 0.000001 \\
ResNet50=False, GA=False & IF(cover,stego)        & 0.994036  & 0.992954  & 0.995118  & 0.007758 / 0.000060 \\
ResNet50=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)     & 0.054361  & 0.051969  & 0.056752  & 0.017149 / 0.000294 \\
ResNet50=False, GA=False & Extraction\_Time(s)    & 0.034320  & 0.032875  & 0.035764  & 0.010359 / 0.000107 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_resnet50_ga_bpp04_v377} presents a $2\times2$ ablation study evaluating the impact of ResNet50-based guidance and Genetic Algorithm (GA) optimization at a high payload setting (BPP = 0.4) across $N=200$ cover images while embedding a $128\times128$ secret image. Compared to lower payload scenarios, a noticeable degradation in imperceptibility is observed; however, the results clearly highlight the crucial role of GA optimization in maintaining both visual quality and statistical robustness.When GA is enabled, both configurations achieve PSNR values around 55.10--55.15~dB with SSIM close to 0.9981 and MSE on the order of $10^{-6}$. The corresponding 95\% confidence intervals are relatively narrow, particularly for the ResNet50=False configuration (PSNR CI width $\approx 0.006$~dB), indicating highly consistent performance across all cover images. In contrast, disabling GA leads to a severe degradation in imperceptibility: PSNR drops to approximately 45.39~dB for ResNet50=True and 51.38~dB for ResNet50=False, accompanied by substantially larger standard deviations and variances, reflecting strong sensitivity to cover content under fixed embedding parameters.varies.


}

{\color{blue}
Comparing the GA-enabled cases shows that ResNet50 guidance has a negligible impact on the mean imperceptibility once GA is applied (PSNR difference $<0.06$~dB). Nevertheless, the ResNet50=True configuration exhibits higher variability than its ResNet50=False counterpart, suggesting that deep-feature guidance may introduce additional cross-cover fluctuations at high payload rates without significantly improving the average quality. From a computational perspective, GA optimization introduces a substantial overhead, increasing embedding time from approximately 0.079--0.102~s (GA=False) to about 0.195--0.198~s (GA=True), and extraction time from approximately 0.050--0.064~s to around 0.115--0.119~s. Despite this increase, the runtime confidence intervals remain reasonably tight, indicating predictable computational behavior.Across all ablation settings, perfect secret recovery is achieved, with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance. Overall, the results confirm that GA optimization is essential for preserving imperceptibility and stability at BPP = 0.4, whereas ResNet50 guidance plays a secondary role that does not significantly enhance the mean performance under GA-driven optimization.


}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $128\times128$.}
\label{tab:summary_resnet50_ga_bpp04_v377}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)        & 55.096410 & 55.078989 & 55.113831 & 0.124937 / 0.015609 \\
ResNet50=True, GA=True & SSIM(cover,stego)        & 0.998103  & 0.997985  & 0.998222  & 0.000848 / 0.000001 \\
ResNet50=True, GA=True & IF(cover,stego)          & 0.994415  & 0.993377  & 0.995453  & 0.007444 / 0.000055 \\
ResNet50=True, GA=True & MSE(cover,stego)         & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)       & 0.198120  & 0.189623  & 0.206617  & 0.060936 / 0.003713 \\
ResNet50=True, GA=True & Extraction\_Time(s)      & 0.118628  & 0.113725  & 0.123531  & 0.035164 / 0.001237 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)       & 45.390958 & 45.155009 & 45.626908 & 1.692143 / 2.863347 \\
ResNet50=True, GA=False & SSIM(cover,stego)       & 0.988328  & 0.987703  & 0.988954  & 0.004486 / 0.000020 \\
ResNet50=True, GA=False & IF(cover,stego)         & 0.982887  & 0.979748  & 0.986026  & 0.022511 / 0.000507 \\
ResNet50=True, GA=False & MSE(cover,stego)        & 0.000031  & 0.000029  & 0.000033  & 0.000012 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)      & 0.078871  & 0.075034  & 0.082707  & 0.027514 / 0.000757 \\
ResNet50=True, GA=False & Extraction\_Time(s)     & 0.049800  & 0.047362  & 0.052238  & 0.017485 / 0.000306 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)       & 55.151308 & 55.148294 & 55.154323 & 0.021621 / 0.000467 \\
ResNet50=False, GA=True & SSIM(cover,stego)       & 0.998131  & 0.998015  & 0.998247  & 0.000832 / 0.000001 \\
ResNet50=False, GA=True & IF(cover,stego)         & 0.994447  & 0.993411  & 0.995483  & 0.007428 / 0.000055 \\
ResNet50=False, GA=True & MSE(cover,stego)        & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)      & 0.194956  & 0.186478  & 0.203434  & 0.060801 / 0.003697 \\
ResNet50=False, GA=True & Extraction\_Time(s)     & 0.114650  & 0.110192  & 0.119108  & 0.031970 / 0.001022 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)      & 51.381385 & 51.339265 & 51.423505 & 0.302069 / 0.091246 \\
ResNet50=False, GA=False & SSIM(cover,stego)      & 0.995625  & 0.995369  & 0.995880  & 0.001833 / 0.000003 \\
ResNet50=False, GA=False & IF(cover,stego)        & 0.991505  & 0.989954  & 0.993056  & 0.011121 / 0.000124 \\
ResNet50=False, GA=False & MSE(cover,stego)       & 0.000007  & 0.000007  & 0.000007  & 0.000001 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)     & 0.102037  & 0.097973  & 0.106101  & 0.029147 / 0.000850 \\
ResNet50=False, GA=False & Extraction\_Time(s)    & 0.064027  & 0.061334  & 0.066720  & 0.019311 / 0.000373 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_resnet50_ga_bpp01_v388} presents a $2\times2$ ablation study evaluating the impact of ResNet50-based guidance and Genetic Algorithm (GA) optimization at a low payload (BPP = 0.1) across $N=200$ cover images while embedding a full-size secret image of $512\times512$. Despite the large secret dimensionality, the results demonstrate that high imperceptibility and statistical stability can be maintained when adaptive optimization is employed.For GA-enabled configurations, both \textit{ResNet50=True, GA=True} and \textit{ResNet50=False, GA=True} achieve PSNR values above 61~dB, SSIM greater than 0.99948, and MSE on the order of $10^{-6}$. The corresponding 95\% confidence intervals are extremely narrow (PSNR CI width $\approx 0.012$~dB for ResNet50=True and $\approx 0.009$~dB for ResNet50=False), indicating highly consistent performance across all cover images. This confirms that GA effectively compensates for the increased embedding complexity associated with large secret sizes.



}

{\color{blue}
In contrast, disabling GA results in a pronounced degradation in imperceptibility. Specifically, PSNR drops to approximately 50.34~dB for \textit{ResNet50=True, GA=False} and 57.42~dB for \textit{ResNet50=False, GA=False}, accompanied by substantially larger standard deviations and variances. These results highlight the limited generalization ability of fixed embedding strategies, particularly when combined with deep feature guidance.Comparing the GA-enabled cases shows that ResNet50 guidance has a negligible effect on the mean imperceptibility once GA is applied (PSNR difference $<0.01$~dB). However, the ResNet50=True configuration exhibits slightly higher variability than ResNet50=False, suggesting that deep-feature guidance may introduce minor cross-cover fluctuations without improving the overall average quality. From a computational perspective, GA approximately doubles both embedding and extraction times compared to GA-disabled settings, yet the associated confidence intervals remain tight, indicating predictable and stable computational overhead.Across all ablation settings, perfect secret recovery is achieved, with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance. Overall, the results confirm that GA optimization is essential for preserving imperceptibility and robustness at BPP = 0.1, even when embedding large secret images, while ResNet50 guidance plays a secondary role under GA-driven optimization.



}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:summary_resnet50_ga_bpp01_v388}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)      & 61.166270 & 61.160037 & 61.172503 & 0.044700 / 0.001998 \\
ResNet50=True, GA=True & SSIM(cover,stego)      & 0.999485  & 0.999454  & 0.999516  & 0.000223 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)        & 0.997223  & 0.996707  & 0.997738  & 0.003697 / 0.000014 \\
ResNet50=True, GA=True & MSE(cover,stego)       & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)     & 0.049587  & 0.047627  & 0.051547  & 0.014058 / 0.000198 \\
ResNet50=True, GA=True & Extraction\_Time(s)    & 0.030884  & 0.029703  & 0.032064  & 0.008468 / 0.000072 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)     & 50.340017 & 50.128952 & 50.551082 & 1.513680 / 2.291227 \\
ResNet50=True, GA=False & SSIM(cover,stego)     & 0.996606  & 0.996424  & 0.996787  & 0.001301 / 0.000002 \\
ResNet50=True, GA=False & IF(cover,stego)       & 0.990185  & 0.988156  & 0.992213  & 0.014548 / 0.000212 \\
ResNet50=True, GA=False & MSE(cover,stego)      & 0.000010  & 0.000009  & 0.000010  & 0.000003 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)& 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)    & 0.019517  & 0.018580  & 0.020454  & 0.006721 / 0.000045 \\
ResNet50=True, GA=False & Extraction\_Time(s)   & 0.013087  & 0.012549  & 0.013625  & 0.003858 / 0.000015 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)     & 61.173279 & 61.168837 & 61.177721 & 0.031854 / 0.001015 \\
ResNet50=False, GA=True & SSIM(cover,stego)     & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)       & 0.997225  & 0.996709  & 0.997740  & 0.003697 / 0.000014 \\
ResNet50=False, GA=True & MSE(cover,stego)      & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)& 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)    & 0.050492  & 0.047859  & 0.053124  & 0.018878 / 0.000356 \\
ResNet50=False, GA=True & Extraction\_Time(s)   & 0.030795  & 0.029502  & 0.032088  & 0.009273 / 0.000086 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)    & 57.419442 & 57.377407 & 57.461477 & 0.301458 / 0.090877 \\
ResNet50=False, GA=False & SSIM(cover,stego)    & 0.998940  & 0.998881  & 0.999000  & 0.000425 / 0.000000 \\
ResNet50=False, GA=False & IF(cover,stego)      & 0.995766  & 0.994998  & 0.996534  & 0.005510 / 0.000030 \\
ResNet50=False, GA=False & MSE(cover,stego)     & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted)&1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)   & 0.028222  & 0.027026  & 0.029419  & 0.008582 / 0.000074 \\
ResNet50=False, GA=False & Extraction\_Time(s)  & 0.018618  & 0.017892  & 0.019344  & 0.005206 / 0.000027 \\
\hline

\end{tabular}%
}
\end{table}


{\color{blue}
Table~\ref{tab:summary_resnet50_ga_bpp0299} reports a $2\times2$ ablation (ResNet50 $\times$ GA) at a moderate payload (BPP = 0.2) over $N=200$ cover images while embedding a full-size secret image ($512\times512$). Overall, the results indicate that GA is the primary driver for imperceptibility at this payload level, whereas ResNet50 guidance provides no clear additional benefit once GA is enabled.Enabling GA yields high imperceptibility for both ResNet50 settings:
PSNR $\approx 58.19$~dB for \textit{ResNet50=True, GA=True} and $\approx 58.23$~dB for \textit{ResNet50=False, GA=True}, with SSIM $\approx 0.9990$ and MSE on the order of $10^{-6}$. The 95\% confidence intervals are narrow, reflecting stable performance across covers. In contrast, disabling GA causes a substantial quality drop:
PSNR decreases to $47.71$~dB for \textit{ResNet50=True, GA=False} and to $54.47$~dB for \textit{ResNet50=False, GA=False}, with noticeably larger variability (e.g., Std $\approx 1.63$ dB for ResNet50=True, GA=False). This confirms that GA effectively optimizes embedding locations/parameters to maintain visual quality at BPP = 0.2.
Within GA-enabled settings, ResNet50 has a negligible effect on mean imperceptibility (PSNR difference $\approx 0.04$~dB) and produces very similar SSIM/IF/MSE values. Moreover, \textit{ResNet50=True, GA=True} exhibits higher PSNR variability (Std $\approx 0.106$) compared to \textit{ResNet50=False, GA=True} (Std $\approx 0.032$), suggesting that deep-feature guidance may introduce small cross-cover fluctuations without improving the average quality. Without GA, ResNet50 further degrades performance (\textit{ResNet50=True, GA=False} is the worst configuration), indicating that ResNet50 guidance alone is insufficient to stabilize embedding at this payload.GA increases computational cost: embedding time rises from $\approx 0.036$--$0.051$ s (GA=False) to $\approx 0.094$--$0.096$ s (GA=True), and extraction time increases similarly (from $\approx 0.023$--$0.032$ s to $\approx 0.057$--$0.058$ s). The confidence intervals for time remain tight, implying predictable overhead.All configurations achieve perfect recovery with SSIM$(\text{secret},\text{extracted})=1.0$ and zero variance, confirming correctness of extraction even when cover distortion increases.At BPP = 0.2 with a large secret ($512\times512$), GA is essential to sustain high imperceptibility and low variance, while ResNet50 guidance does not provide a consistent gain when GA is active and may slightly increase variability.


}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:summary_resnet50_ga_bpp0299}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)      & 58.188445 & 58.173609 & 58.203280 & 0.106397 / 0.011320 \\
ResNet50=True, GA=True & SSIM(cover,stego)      & 0.998990  & 0.998928  & 0.999052  & 0.000445 / 0.000000 \\
ResNet50=True, GA=True & IF(cover,stego)        & 0.996088  & 0.995362  & 0.996814  & 0.005208 / 0.000027 \\
ResNet50=True, GA=True & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)     & 0.096306  & 0.092390  & 0.100221  & 0.028081 / 0.000789 \\
ResNet50=True, GA=True & Extraction\_Time(s)    & 0.057921  & 0.055633  & 0.060209  & 0.016409 / 0.000269 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)     & 47.707314 & 47.480249 & 47.934379 & 1.628424 / 2.651764 \\
ResNet50=True, GA=False & SSIM(cover,stego)     & 0.993353  & 0.993000  & 0.993706  & 0.002530 / 0.000006 \\
ResNet50=True, GA=False & IF(cover,stego)       & 0.986845  & 0.984324  & 0.989365  & 0.018076 / 0.000327 \\
ResNet50=True, GA=False & MSE(cover,stego)      & 0.000018  & 0.000017  & 0.000019  & 0.000006 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)& 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)    & 0.035969  & 0.034368  & 0.037571  & 0.011487 / 0.000132 \\
ResNet50=True, GA=False & Extraction\_Time(s)   & 0.023244  & 0.022307  & 0.024181  & 0.006722 / 0.000045 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)     & 58.227709 & 58.223290 & 58.232129 & 0.031697 / 0.001005 \\
ResNet50=False, GA=True & SSIM(cover,stego)     & 0.999000  & 0.998939  & 0.999062  & 0.000440 / 0.000000 \\
ResNet50=False, GA=True & IF(cover,stego)       & 0.996105  & 0.995381  & 0.996829  & 0.005193 / 0.000027 \\
ResNet50=False, GA=True & MSE(cover,stego)      & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)& 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)    & 0.093726  & 0.089882  & 0.097569  & 0.027562 / 0.000760 \\
ResNet50=False, GA=True & Extraction\_Time(s)   & 0.056685  & 0.054501  & 0.058869  & 0.015666 / 0.000245 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)    & 54.465649 & 54.422913 & 54.508385 & 0.306486 / 0.093934 \\
ResNet50=False, GA=False & SSIM(cover,stego)    & 0.997822  & 0.997699  & 0.997945  & 0.000882 / 0.000001 \\
ResNet50=False, GA=False & IF(cover,stego)      & 0.994052  & 0.992976  & 0.995129  & 0.007723 / 0.000060 \\
ResNet50=False, GA=False & MSE(cover,stego)     & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted)&1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)   & 0.051197  & 0.049111  & 0.053283  & 0.014959 / 0.000224 \\
ResNet50=False, GA=False & Extraction\_Time(s)  & 0.032191  & 0.031002  & 0.033379  & 0.008525 / 0.000073 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_resnet50_ga_bpp04_v41010} presents a $2\times2$ ablation study analyzing the effect of ResNet50-based guidance and Genetic Algorithm (GA) optimization under a highly demanding scenario, where both the payload (BPP = 0.4) and the secret image size ($512\times512$) are large. This setting represents a stress test for imperceptibility, stability, and computational cost.When GA is enabled, both configurations achieve PSNR values around 55.09--55.15~dB, SSIM values close to 0.9981, and MSE on the order of $10^{-6}$, indicating that acceptable imperceptibility can still be maintained despite the high embedding load. The 95\% confidence intervals for PSNR remain relatively narrow, particularly for the \textit{ResNet50=False, GA=True} configuration (CI width $\approx 0.006$~dB), demonstrating strong statistical stability across all cover images. In contrast, the \textit{ResNet50=True, GA=True} configuration exhibits noticeably higher variance, suggesting that deep-feature guidance introduces additional cross-cover variability at this payload level without improving the mean quality.


}


{\color{blue}
Disabling GA results in a pronounced degradation in visual quality and robustness. Specifically, PSNR drops to approximately 45.39~dB for \textit{ResNet50=True, GA=False} and 51.38~dB for \textit{ResNet50=False, GA=False}, accompanied by large standard deviations and variances. These results indicate that fixed embedding strategies are insufficient to cope with high payloads and large secret sizes, even when ResNet50 guidance is employed.From a computational perspective, GA significantly increases runtime. Embedding time rises from approximately 0.077--0.101~s (GA=False) to about 0.188--0.191~s (GA=True), while extraction time increases from approximately 0.049--0.065~s to about 0.110--0.112~s. Despite this increase, the corresponding confidence intervals remain reasonably tight, indicating predictable computational overhead. Across all ablation settings, perfect secret recovery is achieved, with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance, confirming the correctness of extraction even under severe embedding conditions.Overall, the results confirm that GA optimization is essential for preserving imperceptibility and statistical robustness at BPP = 0.4 with full-size secrets, while ResNet50 guidance does not provide a consistent advantage and may slightly increase variability when GA is already applied.



}

\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:summary_resnet50_ga_bpp04_v41010}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

ResNet50=True, GA=True & PSNR(cover,stego)        & 55.090732 & 55.072995 & 55.108470 & 0.127205 / 0.016181 \\
ResNet50=True, GA=True & SSIM(cover,stego)        & 0.998101  & 0.997982  & 0.998219  & 0.000849 / 0.000001 \\
ResNet50=True, GA=True & IF(cover,stego)          & 0.994413  & 0.993376  & 0.995450  & 0.007438 / 0.000055 \\
ResNet50=True, GA=True & MSE(cover,stego)         & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=True & Embedding\_Time(s)       & 0.190781  & 0.182984  & 0.198579  & 0.055922 / 0.003127 \\
ResNet50=True, GA=True & Extraction\_Time(s)      & 0.112092  & 0.107711  & 0.116473  & 0.031419 / 0.000987 \\
\hline

ResNet50=True, GA=False & PSNR(cover,stego)       & 45.385609 & 45.149695 & 45.621522 & 1.691882 / 2.862466 \\
ResNet50=True, GA=False & SSIM(cover,stego)       & 0.988312  & 0.987686  & 0.988937  & 0.004488 / 0.000020 \\
ResNet50=True, GA=False & IF(cover,stego)         & 0.982879  & 0.979742  & 0.986016  & 0.022496 / 0.000506 \\
ResNet50=True, GA=False & MSE(cover,stego)        & 0.000031  & 0.000029  & 0.000033  & 0.000012 / 0.000000 \\
ResNet50=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=True, GA=False & Embedding\_Time(s)      & 0.077358  & 0.073776  & 0.080941  & 0.025690 / 0.000660 \\
ResNet50=True, GA=False & Extraction\_Time(s)     & 0.048656  & 0.046339  & 0.050974  & 0.016620 / 0.000276 \\
\hline

ResNet50=False, GA=True & PSNR(cover,stego)       & 55.151402 & 55.148270 & 55.154534 & 0.022464 / 0.000505 \\
ResNet50=False, GA=True & SSIM(cover,stego)       & 0.998131  & 0.998015  & 0.998247  & 0.000833 / 0.000001 \\
ResNet50=False, GA=True & IF(cover,stego)         & 0.994447  & 0.993412  & 0.995482  & 0.007423 / 0.000055 \\
ResNet50=False, GA=True & MSE(cover,stego)        & 0.000003  & 0.000003  & 0.000003  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=True & Embedding\_Time(s)      & 0.187579  & 0.180108  & 0.195050  & 0.053581 / 0.002871 \\
ResNet50=False, GA=True & Extraction\_Time(s)     & 0.110285  & 0.106116  & 0.114455  & 0.029901 / 0.000894 \\
\hline

ResNet50=False, GA=False & PSNR(cover,stego)      & 51.376735 & 51.334470 & 51.419000 & 0.303111 / 0.091876 \\
ResNet50=False, GA=False & SSIM(cover,stego)      & 0.995619  & 0.995363  & 0.995875  & 0.001836 / 0.000003 \\
ResNet50=False, GA=False & IF(cover,stego)        & 0.991499  & 0.989947  & 0.993052  & 0.011135 / 0.000124 \\
ResNet50=False, GA=False & MSE(cover,stego)       & 0.000007  & 0.000007  & 0.000007  & 0.000001 / 0.000000 \\
ResNet50=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
ResNet50=False, GA=False & Embedding\_Time(s)     & 0.101051  & 0.096948  & 0.105155  & 0.029429 / 0.000866 \\
ResNet50=False, GA=False & Extraction\_Time(s)    & 0.064538  & 0.061690  & 0.067386  & 0.020424 / 0.000417 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_vgg16_ga_bpp011212} presents a $2\times2$ ablation study evaluating the impact of VGG16-based guidance and Genetic Algorithm (GA) optimization at a low payload setting (BPP = 0.1) across $N=200$ cover images. This experiment assesses whether deep feature guidance via VGG16 improves imperceptibility and robustness when combined with adaptive optimization.When GA is enabled, both configurations achieve excellent imperceptibility, with PSNR values above 61~dB, SSIM exceeding 0.99948, and MSE on the order of $10^{-6}$. The corresponding 95\% confidence intervals are extremely narrow, indicating highly stable performance across all cover images. These results confirm that GA optimization effectively compensates for embedding distortions even when deep features are employed.In contrast, disabling GA results in a significant degradation in imperceptibility. Specifically, the \textit{VGG16=True, GA=False} configuration exhibits a substantial PSNR drop to approximately 50.34~dB, along with increased MSE and IF degradation. This configuration also shows large standard deviations and variances, reflecting strong sensitivity to cover content when fixed embedding parameters are used. Even without VGG16 guidance, the GA-disabled baseline remains inferior to GA-enabled settings, highlighting the limited robustness of non-adaptive embedding strategies.



}

{\color{blue}
Comparing the GA-enabled cases reveals that VGG16 guidance has a negligible effect on the mean imperceptibility once GA is applied. The PSNR difference between \textit{VGG16=True, GA=True} and \textit{VGG16=False, GA=True} is less than 0.01~dB, and SSIM and IF values are nearly identical. However, the VGG16-enabled configuration exhibits slightly higher variability, suggesting that deep feature guidance may introduce minor cross-cover fluctuations without improving average quality.From a computational perspective, GA increases both embedding and extraction times by approximately a factor of two compared to GA-disabled settings. Nevertheless, the associated confidence intervals remain tight, indicating predictable computational overhead. Across all ablation settings, perfect secret recovery is achieved, with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance, confirming lossless extraction at BPP = 0.1.Overall, the results demonstrate that GA optimization is the key factor in achieving high imperceptibility and stability, while VGG16-based guidance does not provide a consistent additional benefit under GA-driven optimization


}

\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_vgg16_ga_bpp011212}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

VGG16=True, GA=True & PSNR(cover,stego)        & 61.166270 & 61.160037 & 61.172503 & 0.044700 / 0.001998 \\
VGG16=True, GA=True & SSIM(cover,stego)        & 0.999485  & 0.999454  & 0.999516  & 0.000223 / 0.000000 \\
VGG16=True, GA=True & IF(cover,stego)          & 0.997223  & 0.996707  & 0.997738  & 0.003697 / 0.000014 \\
VGG16=True, GA=True & MSE(cover,stego)         & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & Embedding\_Time(s)       & 0.049587  & 0.047627  & 0.051547  & 0.014058 / 0.000198 \\
VGG16=True, GA=True & Extraction\_Time(s)      & 0.030884  & 0.029703  & 0.032064  & 0.008468 / 0.000072 \\
\hline

VGG16=True, GA=False & PSNR(cover,stego)       & 50.340017 & 50.128952 & 50.551082 & 1.513680 / 2.291227 \\
VGG16=True, GA=False & SSIM(cover,stego)       & 0.996606  & 0.996424  & 0.996787  & 0.001301 / 0.000002 \\
VGG16=True, GA=False & IF(cover,stego)         & 0.990185  & 0.988156  & 0.992213  & 0.014548 / 0.000212 \\
VGG16=True, GA=False & MSE(cover,stego)        & 0.000010  & 0.000009  & 0.000010  & 0.000003 / 0.000000 \\
VGG16=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=False & Embedding\_Time(s)      & 0.019517  & 0.018580  & 0.020454  & 0.006721 / 0.000045 \\
VGG16=True, GA=False & Extraction\_Time(s)     & 0.013087  & 0.012549  & 0.013625  & 0.003858 / 0.000015 \\
\hline

VGG16=False, GA=True & PSNR(cover,stego)       & 61.173279 & 61.168837 & 61.177721 & 0.031854 / 0.001015 \\
VGG16=False, GA=True & SSIM(cover,stego)       & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
VGG16=False, GA=True & IF(cover,stego)         & 0.997225  & 0.996709  & 0.997740  & 0.003697 / 0.000014 \\
VGG16=False, GA=True & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & Embedding\_Time(s)      & 0.050492  & 0.047859  & 0.053124  & 0.018878 / 0.000356 \\
VGG16=False, GA=True & Extraction\_Time(s)     & 0.030795  & 0.029502  & 0.032088  & 0.009273 / 0.000086 \\
\hline

VGG16=False, GA=False & PSNR(cover,stego)      & 57.419442 & 57.377407 & 57.461477 & 0.301458 / 0.090877 \\
VGG16=False, GA=False & SSIM(cover,stego)      & 0.998940  & 0.998881  & 0.999000  & 0.000425 / 0.000000 \\
VGG16=False, GA=False & IF(cover,stego)        & 0.995766  & 0.994998  & 0.996534  & 0.005510 / 0.000030 \\
VGG16=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & Embedding\_Time(s)     & 0.028222  & 0.027026  & 0.029419  & 0.008582 / 0.000074 \\
VGG16=False, GA=False & Extraction\_Time(s)    & 0.018618  & 0.017892  & 0.019344  & 0.005206 / 0.000027 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_vgg16_ga_bpp021313} reports a $2\times2$ ablation study examining the effects of VGG16-based guidance and Genetic Algorithm (GA) optimization at BPP = 0.2 across $N=200$ cover images. The evaluation focuses on imperceptibility metrics (PSNR, SSIM, IF, MSE), computational cost (embedding/extraction time), and recovery fidelity (SSIM$(\text{secret},\text{extracted})$).With GA enabled, both configurations achieve high imperceptibility, reaching PSNR values around 58.19--58.23~dB and SSIM close to 0.999. The 95\% confidence intervals are extremely tight, and the reported standard deviations are small (e.g., PSNR std $\leq 0.106$), indicating consistent behavior over diverse cover images. Moreover, secret recovery remains perfect in all settings, with SSIM$(\text{secret},\text{extracted})=1.0$ and zero variance.Disabling GA leads to a pronounced degradation in visual quality and stability. In particular, the \textit{VGG16=True, GA=False} condition exhibits a large PSNR drop to 47.71~dB and an MSE increase to $1.8\times 10^{-5}$, alongside higher variability (PSNR std $\approx 1.63$). Similarly, \textit{VGG16=False, GA=False} improves over that case but remains substantially weaker than GA-enabled settings, confirming that non-adaptive embedding is sensitive to cover content at higher payload.


}

{\color{blue}
Comparing GA-enabled cases shows that VGG16 guidance does not provide a consistent advantage once optimization is applied. In fact, \textit{VGG16=False, GA=True} slightly outperforms \textit{VGG16=True, GA=True} in PSNR (58.23 vs. 58.19~dB) and maintains lower variability (std 0.032 vs. 0.106), suggesting that GA already finds near-optimal embedding locations without relying on deep feature guidance.From a runtime perspective, GA approximately doubles the embedding and extraction time compared to GA-disabled settings (e.g., embedding time $\sim 0.094$--$0.096$~s vs. $\sim 0.036$--$0.051$~s). However, the time confidence intervals remain narrow, indicating predictable computational overhead.Overall, the results highlight GA as the dominant factor for achieving high imperceptibility and robustness at BPP = 0.2, while VGG16 guidance yields marginal or inconsistent gains under GA-driven optimization.


}

\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_vgg16_ga_bpp021313}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

VGG16=True, GA=True & PSNR(cover,stego)        & 58.188445 & 58.173609 & 58.203280 & 0.106397 / 0.011320 \\
VGG16=True, GA=True & SSIM(cover,stego)        & 0.998990  & 0.998928  & 0.999052  & 0.000445 / 0.000000 \\
VGG16=True, GA=True & IF(cover,stego)          & 0.996088  & 0.995362  & 0.996814  & 0.005208 / 0.000027 \\
VGG16=True, GA=True & MSE(cover,stego)         & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & Embedding\_Time(s)       & 0.096306  & 0.092390  & 0.100221  & 0.028081 / 0.000789 \\
VGG16=True, GA=True & Extraction\_Time(s)      & 0.057921  & 0.055633  & 0.060209  & 0.016409 / 0.000269 \\
\hline

VGG16=True, GA=False & PSNR(cover,stego)       & 47.707314 & 47.480249 & 47.934379 & 1.628424 / 2.651764 \\
VGG16=True, GA=False & SSIM(cover,stego)       & 0.993353  & 0.993000  & 0.993706  & 0.002530 / 0.000006 \\
VGG16=True, GA=False & IF(cover,stego)         & 0.986845  & 0.984324  & 0.989365  & 0.018076 / 0.000327 \\
VGG16=True, GA=False & MSE(cover,stego)        & 0.000018  & 0.000017  & 0.000019  & 0.000006 / 0.000000 \\
VGG16=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=False & Embedding\_Time(s)      & 0.035969  & 0.034368  & 0.037571  & 0.011487 / 0.000132 \\
VGG16=True, GA=False & Extraction\_Time(s)     & 0.023244  & 0.022307  & 0.024181  & 0.006722 / 0.000045 \\
\hline

VGG16=False, GA=True & PSNR(cover,stego)       & 58.227709 & 58.223290 & 58.232129 & 0.031697 / 0.001005 \\
VGG16=False, GA=True & SSIM(cover,stego)       & 0.999000  & 0.998939  & 0.999062  & 0.000440 / 0.000000 \\
VGG16=False, GA=True & IF(cover,stego)         & 0.996105  & 0.995381  & 0.996829  & 0.005193 / 0.000027 \\
VGG16=False, GA=True & MSE(cover,stego)        & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & Embedding\_Time(s)      & 0.093726  & 0.089882  & 0.097569  & 0.027562 / 0.000760 \\
VGG16=False, GA=True & Extraction\_Time(s)     & 0.056685  & 0.054501  & 0.058869  & 0.015666 / 0.000245 \\
\hline

VGG16=False, GA=False & PSNR(cover,stego)      & 54.465649 & 54.422913 & 54.508385 & 0.306486 / 0.093934 \\
VGG16=False, GA=False & SSIM(cover,stego)      & 0.997822  & 0.997699  & 0.997945  & 0.000882 / 0.000001 \\
VGG16=False, GA=False & IF(cover,stego)        & 0.994052  & 0.992976  & 0.995129  & 0.007723 / 0.000060 \\
VGG16=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & Embedding\_Time(s)     & 0.051197  & 0.049111  & 0.053283  & 0.014959 / 0.000224 \\
VGG16=False, GA=False & Extraction\_Time(s)    & 0.032191  & 0.031002  & 0.033379  & 0.008525 / 0.000073 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_vgg16_ga_bpp021414} reports a $2\times2$ ablation study examining the effects of VGG16-based guidance and Genetic Algorithm (GA) optimization at BPP = 0.2 across $N=200$ cover images. The evaluation focuses on imperceptibility metrics (PSNR, SSIM, IF, MSE), computational cost (embedding/extraction time), and recovery fidelity (SSIM$(\text{secret},\text{extracted})$).With GA enabled, both configurations achieve high imperceptibility, reaching PSNR values around 58.19--58.23~dB and SSIM close to 0.999. The 95\% confidence intervals are extremely tight, and the reported standard deviations are small (e.g., PSNR std $\leq 0.106$), indicating consistent behavior over diverse cover images. Moreover, secret recovery remains perfect in all settings, with SSIM$(\text{secret},\text{extracted})=1.0$ and zero variance.Disabling GA leads to a pronounced degradation in visual quality and stability. In particular, the \textit{VGG16=True, GA=False} condition exhibits a large PSNR drop to 47.71~dB and an MSE increase to $1.8\times 10^{-5}$, alongside higher variability (PSNR std $\approx 1.63$). Similarly, \textit{VGG16=False, GA=False} improves over that case but remains substantially weaker than GA-enabled settings, confirming that non-adaptive embedding is sensitive to cover content at higher payload.


}

{\color{blue}
Comparing GA-enabled cases shows that VGG16 guidance does not provide a consistent advantage once optimization is applied. In fact, \textit{VGG16=False, GA=True} slightly outperforms \textit{VGG16=True, GA=True} in PSNR (58.23 vs. 58.19~dB) and maintains lower variability (std 0.032 vs. 0.106), suggesting that GA already finds near-optimal embedding locations without relying on deep feature guidance.From a runtime perspective, GA approximately doubles the embedding and extraction time compared to GA-disabled settings (e.g., embedding time $\sim 0.094$--$0.096$~s vs. $\sim 0.036$--$0.051$~s). However, the time confidence intervals remain narrow, indicating predictable computational overhead.Overall, the results highlight GA as the dominant factor for achieving high imperceptibility and robustness at BPP = 0.2, while VGG16 guidance yields marginal or inconsistent gains under GA-driven optimization.


}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $64\times64$.}
\label{tab:summary_vgg16_ga_bpp021414}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

VGG16=True, GA=True & PSNR(cover,stego)        & 58.188445 & 58.173609 & 58.203280 & 0.106397 / 0.011320 \\
VGG16=True, GA=True & SSIM(cover,stego)        & 0.998990  & 0.998928  & 0.999052  & 0.000445 / 0.000000 \\
VGG16=True, GA=True & IF(cover,stego)          & 0.996088  & 0.995362  & 0.996814  & 0.005208 / 0.000027 \\
VGG16=True, GA=True & MSE(cover,stego)         & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & Embedding\_Time(s)       & 0.096306  & 0.092390  & 0.100221  & 0.028081 / 0.000789 \\
VGG16=True, GA=True & Extraction\_Time(s)      & 0.057921  & 0.055633  & 0.060209  & 0.016409 / 0.000269 \\
\hline

VGG16=True, GA=False & PSNR(cover,stego)       & 47.707314 & 47.480249 & 47.934379 & 1.628424 / 2.651764 \\
VGG16=True, GA=False & SSIM(cover,stego)       & 0.993353  & 0.993000  & 0.993706  & 0.002530 / 0.000006 \\
VGG16=True, GA=False & IF(cover,stego)         & 0.986845  & 0.984324  & 0.989365  & 0.018076 / 0.000327 \\
VGG16=True, GA=False & MSE(cover,stego)        & 0.000018  & 0.000017  & 0.000019  & 0.000006 / 0.000000 \\
VGG16=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=False & Embedding\_Time(s)      & 0.035969  & 0.034368  & 0.037571  & 0.011487 / 0.000132 \\
VGG16=True, GA=False & Extraction\_Time(s)     & 0.023244  & 0.022307  & 0.024181  & 0.006722 / 0.000045 \\
\hline

VGG16=False, GA=True & PSNR(cover,stego)       & 58.227709 & 58.223290 & 58.232129 & 0.031697 / 0.001005 \\
VGG16=False, GA=True & SSIM(cover,stego)       & 0.999000  & 0.998939  & 0.999062  & 0.000440 / 0.000000 \\
VGG16=False, GA=True & IF(cover,stego)         & 0.996105  & 0.995381  & 0.996829  & 0.005193 / 0.000027 \\
VGG16=False, GA=True & MSE(cover,stego)        & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & Embedding\_Time(s)      & 0.093726  & 0.089882  & 0.097569  & 0.027562 / 0.000760 \\
VGG16=False, GA=True & Extraction\_Time(s)     & 0.056685  & 0.054501  & 0.058869  & 0.015666 / 0.000245 \\
\hline

VGG16=False, GA=False & PSNR(cover,stego)      & 54.465649 & 54.422913 & 54.508385 & 0.306486 / 0.093934 \\
VGG16=False, GA=False & SSIM(cover,stego)      & 0.997822  & 0.997699  & 0.997945  & 0.000882 / 0.000001 \\
VGG16=False, GA=False & IF(cover,stego)        & 0.994052  & 0.992976  & 0.995129  & 0.007723 / 0.000060 \\
VGG16=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & Embedding\_Time(s)     & 0.051197  & 0.049111  & 0.053283  & 0.014959 / 0.000224 \\
VGG16=False, GA=False & Extraction\_Time(s)    & 0.032191  & 0.031002  & 0.033379  & 0.008525 / 0.000073 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_vgg16_ga_bpp01_v21515} presents a $2\times2$ ablation study analyzing the impact of VGG16-based guidance and Genetic Algorithm (GA) optimization at a low payload (BPP = 0.1) across $N=200$ cover images while embedding a $128\times128$ secret image. The results demonstrate that GA optimization plays the dominant role in achieving both high imperceptibility and statistical stability, even when the secret size increases.When GA is enabled, both \textit{VGG16=True, GA=True} and \textit{VGG16=False, GA=True} achieve PSNR values above 61~dB, SSIM exceeding 0.99948, and MSE on the order of $10^{-6}$. The corresponding 95\% confidence intervals are narrow, indicating consistent performance across all cover images. Notably, the \textit{VGG16=False, GA=True} configuration exhibits lower PSNR variability (Std $\approx 0.033$) compared to \textit{VGG16=True, GA=True} (Std $\approx 0.112$), suggesting slightly higher robustness when VGG16 guidance is not used under GA-driven optimization.


}

{\color{blue}
In contrast, disabling GA leads to a clear degradation in imperceptibility and increased variance. Although \textit{VGG16=True, GA=False} achieves relatively high PSNR values (around 59.27~dB), the large standard deviation (Std $\approx 1.67$) and wider confidence interval indicate unstable performance across different covers. The baseline \textit{VGG16=False, GA=False} further confirms the limited robustness of non-adaptive embedding strategies, with PSNR decreasing to approximately 57.40~dB.From a computational standpoint, GA increases embedding and extraction times compared to GA-disabled configurations; however, the associated confidence intervals remain tight, indicating predictable computational overhead. Across all ablation settings, perfect secret recovery is achieved, with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance.Overall, the results confirm that GA optimization is essential for maintaining high imperceptibility and robustness at BPP = 0.1 with larger secret images, while VGG16-based guidance provides limited additional benefit and may slightly increase cross-cover variability when GA is already applied.

}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $128\times128$.}
\label{tab:summary_vgg16_ga_bpp01_v21515}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

VGG16=True, GA=True & PSNR(cover,stego)        & 61.134115 & 61.118447 & 61.149782 & 0.112362 / 0.012625 \\
VGG16=True, GA=True & SSIM(cover,stego)        & 0.999487  & 0.999455  & 0.999518  & 0.000223 / 0.000000 \\
VGG16=True, GA=True & IF(cover,stego)          & 0.997214  & 0.996698  & 0.997731  & 0.003703 / 0.000014 \\
VGG16=True, GA=True & MSE(cover,stego)         & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & Embedding\_Time(s)       & 0.062817  & 0.060334  & 0.065300  & 0.017808 / 0.000317 \\
VGG16=True, GA=True & Extraction\_Time(s)      & 0.036266  & 0.034521  & 0.038012  & 0.012520 / 0.000157 \\
\hline

VGG16=True, GA=False & PSNR(cover,stego)       & 59.267496 & 59.035085 & 59.499908 & 1.666771 / 2.778124 \\
VGG16=True, GA=False & SSIM(cover,stego)       & 0.999458  & 0.999429  & 0.999488  & 0.000214 / 0.000000 \\
VGG16=True, GA=False & IF(cover,stego)         & 0.996575  & 0.996009  & 0.997140  & 0.004059 / 0.000016 \\
VGG16=True, GA=False & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000001 / 0.000000 \\
VGG16=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=False & Embedding\_Time(s)      & 0.054424  & 0.051918  & 0.056930  & 0.017972 / 0.000323 \\
VGG16=True, GA=False & Extraction\_Time(s)     & 0.031863  & 0.030247  & 0.033479  & 0.011590 / 0.000134 \\
\hline

VGG16=False, GA=True & PSNR(cover,stego)       & 61.171940 & 61.167321 & 61.176558 & 0.033123 / 0.001097 \\
VGG16=False, GA=True & SSIM(cover,stego)       & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
VGG16=False, GA=True & IF(cover,stego)         & 0.997224  & 0.996708  & 0.997740  & 0.003701 / 0.000014 \\
VGG16=False, GA=True & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & Embedding\_Time(s)      & 0.064135  & 0.061404  & 0.066866  & 0.019585 / 0.000384 \\
VGG16=False, GA=True & Extraction\_Time(s)     & 0.036343  & 0.034617  & 0.038069  & 0.012379 / 0.000153 \\
\hline

VGG16=False, GA=False & PSNR(cover,stego)      & 57.399868 & 57.357285 & 57.442452 & 0.305392 / 0.093264 \\
VGG16=False, GA=False & SSIM(cover,stego)      & 0.998937  & 0.998878  & 0.998997  & 0.000426 / 0.000000 \\
VGG16=False, GA=False & IF(cover,stego)        & 0.995757  & 0.994988  & 0.996527  & 0.005519 / 0.000030 \\
VGG16=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & Embedding\_Time(s)     & 0.034588  & 0.032857  & 0.036318  & 0.012409 / 0.000154 \\
VGG16=False, GA=False & Extraction\_Time(s)    & 0.021694  & 0.020662  & 0.022727  & 0.007402 / 0.000055 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_vgg16_ga_bpp021616} presents a $2\times2$ ablation study investigating the effect of VGG16-based guidance and Genetic Algorithm (GA) optimization at a moderate payload (BPP = 0.2) across $N=200$ cover images while embedding a $128\times128$ secret image. This setting provides a balanced scenario between embedding capacity and imperceptibility.When GA is enabled, both configurations achieve high visual quality, with PSNR values around 58.20--58.23~dB, SSIM close to 0.999, and MSE on the order of $10^{-6}$. The corresponding 95\% confidence intervals are narrow, confirming stable and consistent performance across all cover images. Notably, the \textit{VGG16=False, GA=True} configuration exhibits lower PSNR variability (Std $\approx 0.033$) compared to \textit{VGG16=True, GA=True} (Std $\approx 0.113$), indicating that GA-driven optimization without deep feature guidance yields slightly more robust behavior.In contrast, disabling GA leads to a noticeable degradation in imperceptibility and increased variance. The \textit{VGG16=True, GA=False} configuration shows a PSNR drop to approximately 56.42~dB with a relatively large standard deviation (Std $\approx 1.50$), while the baseline \textit{VGG16=False, GA=False} further degrades to about 54.45~dB. These results demonstrate that fixed embedding strategies are less effective at this payload level, even when VGG16 guidance is applied.

}

{\color{blue}
From a computational perspective, GA significantly increases runtime. Embedding time rises from approximately 0.062--0.104~s (GA=False) to about 0.120~s (GA=True), and extraction time increases from approximately 0.039--0.061~s to around 0.068~s. Despite this increase, the confidence intervals for runtime remain tight, indicating predictable computational overhead. Across all ablation settings, perfect secret recovery is achieved, with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance.Overall, the results confirm that GA optimization is the primary factor for maintaining imperceptibility and robustness at BPP = 0.2, whereas VGG16-based guidance provides limited additional benefit and may slightly increase cross-cover variability when GA is already employed.

}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $128\times128$.}
\label{tab:summary_vgg16_ga_bpp021616}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

VGG16=True, GA=True & PSNR(cover,stego)        & 58.196334 & 58.180576 & 58.212091 & 0.113007 / 0.012771 \\
VGG16=True, GA=True & SSIM(cover,stego)        & 0.999003  & 0.998942  & 0.999065  & 0.000439 / 0.000000 \\
VGG16=True, GA=True & IF(cover,stego)          & 0.996094  & 0.995371  & 0.996817  & 0.005186 / 0.000027 \\
VGG16=True, GA=True & MSE(cover,stego)         & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & Embedding\_Time(s)       & 0.120043  & 0.115292  & 0.124793  & 0.034070 / 0.001161 \\
VGG16=True, GA=True & Extraction\_Time(s)      & 0.068613  & 0.065463  & 0.071763  & 0.022592 / 0.000510 \\
\hline

VGG16=True, GA=False & PSNR(cover,stego)       & 56.419658 & 56.210859 & 56.628456 & 1.497426 / 2.242284 \\
VGG16=True, GA=False & SSIM(cover,stego)       & 0.998934  & 0.998876  & 0.998992  & 0.000419 / 0.000000 \\
VGG16=True, GA=False & IF(cover,stego)         & 0.995270  & 0.994502  & 0.996039  & 0.005510 / 0.000030 \\
VGG16=True, GA=False & MSE(cover,stego)        & 0.000002  & 0.000002  & 0.000003  & 0.000001 / 0.000000 \\
VGG16=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=False & Embedding\_Time(s)      & 0.103692  & 0.097993  & 0.109391  & 0.040870 / 0.001670 \\
VGG16=True, GA=False & Extraction\_Time(s)     & 0.061073  & 0.055723  & 0.066424  & 0.038371 / 0.001472 \\
\hline

VGG16=False, GA=True & PSNR(cover,stego)       & 58.228887 & 58.224250 & 58.233524 & 0.033255 / 0.001106 \\
VGG16=False, GA=True & SSIM(cover,stego)       & 0.999001  & 0.998940  & 0.999062  & 0.000440 / 0.000000 \\
VGG16=False, GA=True & IF(cover,stego)         & 0.996105  & 0.995380  & 0.996830  & 0.005200 / 0.000027 \\
VGG16=False, GA=True & MSE(cover,stego)        & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & Embedding\_Time(s)      & 0.120863  & 0.115964  & 0.125763  & 0.035136 / 0.001235 \\
VGG16=False, GA=True & Extraction\_Time(s)     & 0.068363  & 0.065187  & 0.071538  & 0.022775 / 0.000519 \\
\hline

VGG16=False, GA=False & PSNR(cover,stego)      & 54.445384 & 54.402733 & 54.488034 & 0.306486 / 0.093559 \\
VGG16=False, GA=False & SSIM(cover,stego)      & 0.997812  & 0.997688  & 0.997935  & 0.000887 / 0.000001 \\
VGG16=False, GA=False & IF(cover,stego)        & 0.994036  & 0.992976  & 0.995129  & 0.007758 / 0.000060 \\
VGG16=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & Embedding\_Time(s)     & 0.062392  & 0.059457  & 0.065326  & 0.021043 / 0.000443 \\
VGG16=False, GA=False & Extraction\_Time(s)    & 0.038869  & 0.037021  & 0.040716  & 0.013251 / 0.000176 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_vgg16_ga_bpp041818} reports a $2\times2$ ablation study evaluating the influence of VGG16-based guidance and Genetic Algorithm (GA) optimization across $N=200$ cover images with a $128\times128$ secret. The reported results exhibit very high imperceptibility and perfectly lossless recovery across all settings, as indicated by SSIM$(\text{secret},\text{extracted})=1.0$ with zero variance.Enabling GA yields the best perceptual quality: PSNR remains around $58.20$~dB with SSIM close to $0.999$ and MSE on the order of $10^{-6}$. The 95\% confidence intervals are narrow, indicating stable behavior across covers. Moreover, the configuration \textit{VGG16=False, GA=True} shows substantially lower variability in PSNR (Std $\approx 0.033$) compared to \textit{VGG16=True, GA=True} (Std $\approx 0.113$), suggesting slightly more consistent performance when GA is used without VGG16 guidance.

}

{\color{blue}
When GA is disabled, imperceptibility degrades and variability increases. PSNR drops to $56.42$~dB for \textit{VGG16=True, GA=False} and further to $54.45$~dB for \textit{VGG16=False, GA=False}. This confirms that GA is the dominant contributor to preserving cover quality in this regime, while VGG16 guidance alone provides limited improvement over the baseline.From a computational standpoint, GA increases runtime. Embedding time increases from approximately $0.062$--$0.104$~s (GA=False) to about $0.120$~s (GA=True), and extraction time increases from about $0.039$--$0.061$~s to around $0.068$~s. Despite this overhead, the runtime confidence intervals remain tight, demonstrating predictable computational cost.


}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $128\times128$.}
\label{tab:summary_vgg16_ga_bpp041818}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

VGG16=True, GA=True & PSNR(cover,stego)        & 58.196334 & 58.180576 & 58.212091 & 0.113007 / 0.012771 \\
VGG16=True, GA=True & SSIM(cover,stego)        & 0.999003  & 0.998942  & 0.999065  & 0.000439 / 0.000000 \\
VGG16=True, GA=True & IF(cover,stego)          & 0.996094  & 0.995371  & 0.996817  & 0.005186 / 0.000027 \\
VGG16=True, GA=True & MSE(cover,stego)         & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & Embedding\_Time(s)       & 0.120043  & 0.115292  & 0.124793  & 0.034070 / 0.001161 \\
VGG16=True, GA=True & Extraction\_Time(s)      & 0.068613  & 0.065463  & 0.071763  & 0.022592 / 0.000510 \\
\hline

VGG16=True, GA=False & PSNR(cover,stego)       & 56.419658 & 56.210859 & 56.628456 & 1.497426 / 2.242284 \\
VGG16=True, GA=False & SSIM(cover,stego)       & 0.998934  & 0.998876  & 0.998992  & 0.000419 / 0.000000 \\
VGG16=True, GA=False & IF(cover,stego)         & 0.995270  & 0.994502  & 0.996039  & 0.005510 / 0.000030 \\
VGG16=True, GA=False & MSE(cover,stego)        & 0.000002  & 0.000002  & 0.000003  & 0.000001 / 0.000000 \\
VGG16=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=False & Embedding\_Time(s)      & 0.103692  & 0.097993  & 0.109391  & 0.040870 / 0.001670 \\
VGG16=True, GA=False & Extraction\_Time(s)     & 0.061073  & 0.055723  & 0.066424  & 0.038371 / 0.001472 \\
\hline

VGG16=False, GA=True & PSNR(cover,stego)       & 58.228887 & 58.224250 & 58.233524 & 0.033255 / 0.001106 \\
VGG16=False, GA=True & SSIM(cover,stego)       & 0.999001  & 0.998940  & 0.999062  & 0.000440 / 0.000000 \\
VGG16=False, GA=True & IF(cover,stego)         & 0.996105  & 0.995380  & 0.996830  & 0.005200 / 0.000027 \\
VGG16=False, GA=True & MSE(cover,stego)        & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & Embedding\_Time(s)      & 0.120863  & 0.115964  & 0.125763  & 0.035136 / 0.001235 \\
VGG16=False, GA=True & Extraction\_Time(s)     & 0.068363  & 0.065187  & 0.071538  & 0.022775 / 0.000519 \\
\hline

VGG16=False, GA=False & PSNR(cover,stego)      & 54.445384 & 54.402733 & 54.488034 & 0.305875 / 0.093559 \\
VGG16=False, GA=False & SSIM(cover,stego)      & 0.997812  & 0.997688  & 0.997935  & 0.000887 / 0.000001 \\
VGG16=False, GA=False & IF(cover,stego)        & 0.994036  & 0.992954  & 0.995118  & 0.007758 / 0.000060 \\
VGG16=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & Embedding\_Time(s)     & 0.062392  & 0.059457  & 0.065326  & 0.021043 / 0.000443 \\
VGG16=False, GA=False & Extraction\_Time(s)    & 0.038869  & 0.037021  & 0.040716  & 0.013251 / 0.000176 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_vgg16_ga_bpp01_v31919} presents a $2\times2$ ablation study examining the effect of VGG16-based guidance and Genetic Algorithm (GA) optimization at a low payload (BPP = 0.1) across $N=200$ cover images while embedding a large secret image of size $512\times512$. This experiment evaluates the robustness of the embedding strategy under increased secret dimensionality.When GA is enabled, both configurations achieve excellent imperceptibility, with PSNR values exceeding 61~dB, SSIM values above 0.99948, and MSE on the order of $10^{-6}$. The associated 95\% confidence intervals are narrow, confirming highly consistent behavior across all cover images. Notably, the \textit{VGG16=False, GA=True} configuration exhibits significantly lower PSNR variability (Std $\approx 0.032$) compared to \textit{VGG16=True, GA=True} (Std $\approx 0.117$), suggesting that GA-driven optimization without deep feature guidance provides slightly more stable performance.


}

{\color{blue}
In contrast, disabling GA leads to increased variability and reduced robustness. Although \textit{VGG16=True, GA=False} attains a relatively high mean PSNR of approximately 59.27~dB, the large standard deviation (Std $\approx 1.66$) and wider confidence interval indicate sensitivity to cover content. The baseline \textit{VGG16=False, GA=False} further confirms the limitations of non-adaptive embedding, with PSNR decreasing to about 57.42~dB.From a computational perspective, GA increases both embedding and extraction times relative to GA-disabled configurations. However, the reported confidence intervals remain tight, indicating predictable computational overhead. Across all ablation settings, perfect secret recovery is achieved, with SSIM$(\text{secret},\text{extracted}) = 1.0$ and zero variance, demonstrating lossless extraction even when embedding large secrets.Overall, the results demonstrate that GA optimization is the dominant factor for maintaining imperceptibility and stability at BPP = 0.1 with large secret sizes, while VGG16-based guidance provides limited additional benefit and may slightly increase cross-cover variability.

}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.1 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:summary_vgg16_ga_bpp01_v31919}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

VGG16=True, GA=True & PSNR(cover,stego)        & 61.134280 & 61.117979 & 61.150581 & 0.116906 / 0.013667 \\
VGG16=True, GA=True & SSIM(cover,stego)        & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
VGG16=True, GA=True & IF(cover,stego)          & 0.997213  & 0.996696  & 0.997730  & 0.003708 / 0.000014 \\
VGG16=True, GA=True & MSE(cover,stego)         & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & Embedding\_Time(s)       & 0.051350  & 0.049200  & 0.053500  & 0.015417 / 0.000238 \\
VGG16=True, GA=True & Extraction\_Time(s)      & 0.031182  & 0.029950  & 0.032414  & 0.008836 / 0.000078 \\
\hline

VGG16=True, GA=False & PSNR(cover,stego)       & 59.273214 & 59.041153 & 59.505274 & 1.664251 / 2.769731 \\
VGG16=True, GA=False & SSIM(cover,stego)       & 0.999459  & 0.999429  & 0.999488  & 0.000214 / 0.000000 \\
VGG16=True, GA=False & IF(cover,stego)         & 0.996576  & 0.996011  & 0.997141  & 0.004053 / 0.000016 \\
VGG16=True, GA=False & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000001 / 0.000000 \\
VGG16=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=False & Embedding\_Time(s)      & 0.043774  & 0.041744  & 0.045804  & 0.014557 / 0.000212 \\
VGG16=True, GA=False & Extraction\_Time(s)     & 0.027400  & 0.026192  & 0.028607  & 0.008661 / 0.000075 \\
\hline

VGG16=False, GA=True & PSNR(cover,stego)       & 61.173279 & 61.168837 & 61.177721 & 0.031854 / 0.001015 \\
VGG16=False, GA=True & SSIM(cover,stego)       & 0.999486  & 0.999455  & 0.999517  & 0.000223 / 0.000000 \\
VGG16=False, GA=True & IF(cover,stego)         & 0.997225  & 0.996709  & 0.997740  & 0.003697 / 0.000014 \\
VGG16=False, GA=True & MSE(cover,stego)        & 0.000001  & 0.000001  & 0.000001  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & Embedding\_Time(s)      & 0.049979  & 0.047890  & 0.052068  & 0.014982 / 0.000224 \\
VGG16=False, GA=True & Extraction\_Time(s)     & 0.029648  & 0.028442  & 0.030854  & 0.008648 / 0.000075 \\
\hline

VGG16=False, GA=False & PSNR(cover,stego)      & 57.419442 & 57.377407 & 57.461477 & 0.301458 / 0.090877 \\
VGG16=False, GA=False & SSIM(cover,stego)      & 0.998940  & 0.998881  & 0.999000  & 0.000425 / 0.000000 \\
VGG16=False, GA=False & IF(cover,stego)        & 0.995766  & 0.994998  & 0.996534  & 0.005510 / 0.000030 \\
VGG16=False, GA=False & MSE(cover,stego)       & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & Embedding\_Time(s)     & 0.028225  & 0.026981  & 0.029469  & 0.008922 / 0.000080 \\
VGG16=False, GA=False & Extraction\_Time(s)    & 0.018334  & 0.017596  & 0.019072  & 0.005292 / 0.000028 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_vgg16_ga_bpp02_v32121} presents a $2\times2$ ablation study that evaluates the impact of VGG16-based guidance and Genetic Algorithm (GA) optimization at a moderate payload (BPP = 0.2) while embedding a large secret image ($512\times512$) across $N=200$ cover images.Overall, enabling GA yields the best imperceptibility metrics. In particular, the mean PSNR increases from $56.42$~dB (\textit{VGG16=True, GA=False}) and $54.47$~dB (\textit{VGG16=False, GA=False}) to $58.20$--$58.23$~dB when GA is enabled. Similarly, SSIM remains close to $0.999$ and MSE stays around $2\times10^{-6}$ under GA-enabled settings, indicating low distortion even with a large secret.A notable observation is that \textit{VGG16=False, GA=True} achieves slightly higher PSNR ($58.23$~dB) than \textit{VGG16=True, GA=True} ($58.20$~dB), while also exhibiting substantially lower variability (Std $\approx 0.032$ vs.\ $0.109$). This suggests that, under GA optimization, VGG16 guidance provides limited additional benefit and may introduce extra cross-cover variability. The narrow 95\% confidence intervals for GA-enabled cases further confirm the stability of these results across different covers.

}

{\color{blue}
In contrast, disabling GA leads to degraded imperceptibility and weaker robustness. The \textit{VGG16=True, GA=False} setting improves over the baseline \textit{VGG16=False, GA=False} in PSNR and SSIM; however, it still shows higher variance (Std $\approx 1.50$) compared to GA-enabled cases, indicating that fixed embedding without optimization is sensitive to cover content under this payload.From the computational perspective, GA increases embedding and extraction time. Embedding time rises from approximately $0.052$--$0.082$~s (GA=False) to about $0.095$--$0.098$~s (GA=True), and extraction time increases from roughly $0.032$--$0.050$~s to $0.056$--$0.059$~s. Despite this overhead, timing confidence intervals remain relatively tight, implying predictable runtime across the dataset.Finally, all configurations achieve perfect secret recovery, as indicated by SSIM$(\text{secret},\text{extracted})=1.0$ with zero variance. In summary, GA is the dominant contributor to improved imperceptibility and stability at BPP = 0.2 for large secrets, whereas VGG16 guidance offers only marginal gains when GA is disabled and becomes largely redundant when GA is enabled.

}



\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.2 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:summary_vgg16_ga_bpp02_v32121}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

VGG16=True, GA=True & PSNR(cover,stego)        & 58.203527 & 58.188373 & 58.218680 & 0.108678 / 0.011811 \\
VGG16=True, GA=True & SSIM(cover,stego)        & 0.999003  & 0.998942  & 0.999064  & 0.000439 / 0.000000 \\
VGG16=True, GA=True & IF(cover,stego)          & 0.996097  & 0.995376  & 0.996819  & 0.005175 / 0.000027 \\
VGG16=True, GA=True & MSE(cover,stego)         & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & Embedding\_Time(s)       & 0.098186  & 0.093953  & 0.102420  & 0.030363 / 0.000922 \\
VGG16=True, GA=True & Extraction\_Time(s)      & 0.058706  & 0.056282  & 0.061131  & 0.017386 / 0.000302 \\
\hline

VGG16=True, GA=False & PSNR(cover,stego)        & 56.421346 & 56.212152 & 56.630540 & 1.500259 / 2.250777 \\
VGG16=True, GA=False & SSIM(cover,stego)        & 0.998934  & 0.998876  & 0.998993  & 0.000418 / 0.000000 \\
VGG16=True, GA=False & IF(cover,stego)          & 0.995271  & 0.994502  & 0.996040  & 0.005513 / 0.000030 \\
VGG16=True, GA=False & MSE(cover,stego)         & 0.000002  & 0.000002  & 0.000003  & 0.000001 / 0.000000 \\
VGG16=True, GA=False & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=False & Embedding\_Time(s)       & 0.082246  & 0.078265  & 0.086226  & 0.028549 / 0.000815 \\
VGG16=True, GA=False & Extraction\_Time(s)      & 0.050152  & 0.047863  & 0.052441  & 0.016415 / 0.000269 \\
\hline

VGG16=False, GA=True & PSNR(cover,stego)        & 58.227709 & 58.223290 & 58.232129 & 0.031697 / 0.001005 \\
VGG16=False, GA=True & SSIM(cover,stego)        & 0.999000  & 0.998939  & 0.999062  & 0.000440 / 0.000000 \\
VGG16=False, GA=True & IF(cover,stego)          & 0.996105  & 0.995381  & 0.996829  & 0.005193 / 0.000027 \\
VGG16=False, GA=True & MSE(cover,stego)         & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & Embedding\_Time(s)       & 0.094747  & 0.090660  & 0.098834  & 0.029309 / 0.000859 \\
VGG16=False, GA=True & Extraction\_Time(s)      & 0.056238  & 0.053923  & 0.058554  & 0.016607 / 0.000276 \\
\hline

VGG16=False, GA=False & PSNR(cover,stego)        & 54.465649 & 54.422913 & 54.508385 & 0.306486 / 0.093934 \\
VGG16=False, GA=False & SSIM(cover,stego)        & 0.997822  & 0.997699  & 0.997945  & 0.000882 / 0.000001 \\
VGG16=False, GA=False & IF(cover,stego)          & 0.994052  & 0.992976  & 0.995129  & 0.007723 / 0.000060 \\
VGG16=False, GA=False & MSE(cover,stego)         & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & Embedding\_Time(s)       & 0.051661  & 0.049322  & 0.053999  & 0.016769 / 0.000281 \\
VGG16=False, GA=False & Extraction\_Time(s)      & 0.031919  & 0.030641  & 0.033197  & 0.009166 / 0.000084 \\
\hline

\end{tabular}%
}
\end{table}

{\color{blue}
Table~\ref{tab:summary_vgg16_ga_bpp042222} reports a $2\times2$ ablation study evaluating the combined effect of VGG16-based guidance and Genetic Algorithm (GA) optimization under a high payload setting (BPP = 0.4) while embedding a large secret image of size $512\times512$ across $N=200$ cover images. This configuration represents a challenging scenario in terms of imperceptibility, robustness, and computational overhead.When GA is enabled, both configurations achieve relatively high imperceptibility despite the aggressive embedding conditions. Specifically, PSNR values remain around $58.20$~dB for \textit{VGG16=True, GA=True} and $58.23$~dB for \textit{VGG16=False, GA=True}, with SSIM values close to $0.999$ and MSE on the order of $10^{-6}$. The corresponding 95\% confidence intervals are narrow, indicating stable behavior across all cover images. Notably, the \textit{VGG16=False, GA=True} configuration exhibits substantially lower PSNR variability (Std $\approx 0.032$) compared to \textit{VGG16=True, GA=True} (Std $\approx 0.109$), suggesting that GA-driven optimization without deep feature guidance yields more consistent imperceptibility under high payload conditions.

}

{\color{blue}
In contrast, disabling GA leads to a clear degradation in both mean performance and statistical robustness. The \textit{VGG16=True, GA=False} configuration shows a PSNR drop to approximately $56.42$~dB with a large standard deviation (Std $\approx 1.50$), while the baseline \textit{VGG16=False, GA=False} further degrades to about $54.47$~dB. These results highlight the limitations of fixed embedding strategies at high payloads, even when VGG16-based guidance is employed.From a computational perspective, GA significantly increases embedding and extraction times. Embedding time increases from approximately $0.051$--$0.082$~s (GA=False) to about $0.095$--$0.098$~s (GA=True), while extraction time increases from approximately $0.032$--$0.050$~s to around $0.056$--$0.059$~s. Despite this overhead, the associated confidence intervals remain relatively tight, indicating predictable and stable computational cost.Across all ablation settings, perfect secret recovery is achieved, as reflected by SSIM$(\text{secret},\text{extracted}) = 1.0$ with zero variance. Overall, the results confirm that GA optimization is essential for maintaining imperceptibility and robustness at BPP = 0.4 with large secret images, whereas VGG16-based guidance provides limited additional benefit and may slightly increase cross-cover variability when GA is already applied.


}


\begin{table}[htbp]
\centering
\small
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.25}
\caption{Summary (Across all covers) at BPP=0.4 (N=200): Mean, 95\% CI, Std, and Variance under Secret Image Size $512\times512$.}
\label{tab:summary_vgg16_ga_bpp042222}

\resizebox{\textwidth}{!}{%
\begin{tabular}{l l c c c c}
\hline
\textbf{Ablation} & \textbf{Metric} & \textbf{Mean} & \textbf{CI95 Low} & \textbf{CI95 High} & \textbf{Std / Var} \\
\hline

VGG16=True, GA=True & PSNR(cover,stego)        & 58.203527 & 58.188373 & 58.218680 & 0.108678 / 0.011811 \\
VGG16=True, GA=True & SSIM(cover,stego)        & 0.999003  & 0.998942  & 0.999064  & 0.000439 / 0.000000 \\
VGG16=True, GA=True & IF(cover,stego)          & 0.996097  & 0.995376  & 0.996819  & 0.005175 / 0.000027 \\
VGG16=True, GA=True & MSE(cover,stego)         & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & SSIM(secret,extracted)   & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=True & Embedding\_Time(s)       & 0.098186  & 0.093953  & 0.102420  & 0.030363 / 0.000922 \\
VGG16=True, GA=True & Extraction\_Time(s)      & 0.058706  & 0.056282  & 0.061131  & 0.017386 / 0.000302 \\
\hline

VGG16=True, GA=False & PSNR(cover,stego)       & 56.421346 & 56.212152 & 56.630540 & 1.500259 / 2.250777 \\
VGG16=True, GA=False & SSIM(cover,stego)       & 0.998934  & 0.998876  & 0.998993  & 0.000418 / 0.000000 \\
VGG16=True, GA=False & IF(cover,stego)         & 0.995271  & 0.994502  & 0.996040  & 0.005513 / 0.000030 \\
VGG16=True, GA=False & MSE(cover,stego)        & 0.000002  & 0.000002  & 0.000003  & 0.000001 / 0.000000 \\
VGG16=True, GA=False & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=True, GA=False & Embedding\_Time(s)      & 0.082246  & 0.078265  & 0.086226  & 0.028549 / 0.000815 \\
VGG16=True, GA=False & Extraction\_Time(s)     & 0.050152  & 0.047863  & 0.052441  & 0.016415 / 0.000269 \\
\hline

VGG16=False, GA=True & PSNR(cover,stego)       & 58.227709 & 58.223290 & 58.232129 & 0.031697 / 0.001005 \\
VGG16=False, GA=True & SSIM(cover,stego)       & 0.999000  & 0.998939  & 0.999062  & 0.000440 / 0.000000 \\
VGG16=False, GA=True & IF(cover,stego)         & 0.996105  & 0.995380  & 0.996830  & 0.005193 / 0.000027 \\
VGG16=False, GA=True & MSE(cover,stego)        & 0.000002  & 0.000002  & 0.000002  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & SSIM(secret,extracted)  & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=True & Embedding\_Time(s)      & 0.094747  & 0.090660  & 0.098834  & 0.029309 / 0.000859 \\
VGG16=False, GA=True & Extraction\_Time(s)     & 0.056238  & 0.053923  & 0.058554  & 0.016607 / 0.000276 \\
\hline

VGG16=False, GA=False & PSNR(cover,stego)      & 54.465649 & 54.402733 & 54.488034 & 0.306486 / 0.093934 \\
VGG16=False, GA=False & SSIM(cover,stego)      & 0.997822  & 0.997699  & 0.997935  & 0.000882 / 0.000001 \\
VGG16=False, GA=False & IF(cover,stego)        & 0.994052  & 0.992976  & 0.995118  & 0.007723 / 0.000060 \\
VGG16=False, GA=False & MSE(cover,stego)       & 0.000004  & 0.000004  & 0.000004  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & SSIM(secret,extracted) & 1.000000  & 1.000000  & 1.000000  & 0.000000 / 0.000000 \\
VGG16=False, GA=False & Embedding\_Time(s)     & 0.051661  & 0.049322  & 0.053999  & 0.016769 / 0.000281 \\
VGG16=False, GA=False & Extraction\_Time(s)    & 0.031919  & 0.030641  & 0.033197  & 0.009166 / 0.000084 \\
\hline

\end{tabular}%
}
\end{table}

