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\chapter{Related Work}
\label{yyyyy}

The field of information security increasingly recognizes image steganography as a vital research area, as it enables the concealment of secret data within digital images such that unauthorized parties cannot detect its presence. Since its inception, various techniques have been developed, including Least Significant Bit (LSB) substitution and frequency-domain approaches such as the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), as well as modern deep learning–based and generative-model approaches designed to enhance embedding quality, security, and robustness. The effectiveness of these techniques is typically evaluated using visual imperceptibility metrics, payload capacity, and resistance to steganalysis attacks. This review examines key influential works in the field by discussing their methodologies, strengths, and limitations in addressing contemporary steganographic requirements.

\section{Classical Spatial Domain Techniques}
\label{yyyyy}

Chakraborty and Jalal~\cite{chakraborty2020} presented a feature-based blind image steganography technique that employs Local Binary Patterns (LBP) to preserve local structural characteristics of the cover image during data embedding. The proposed method outperforms traditional LSB and Pixel Value Differencing (PVD) approaches by maintaining local pixel relationships, thereby increasing resistance to statistical steganalysis. Specifically, LBP codes are generated from non-overlapping $3 \times 3$ blocks of the cover image, followed by bitwise XOR operations with the payload bits to produce transformed payload data. These transformed bits are embedded into the LSBs of surrounding pixels using synchronized operations, which help preserve the central pixel and its neighborhood structure. The inclusion of a bit-shuffling mechanism further enhances concealment, while the decoding process restores the original payload using LBP consistency. Experimental results demonstrate an embedding capacity exceeding 800,000 bits and a maximum PSNR of 58.6~dB, outperforming edge-based and pixel-difference techniques in terms of both visual quality and steganalysis resistance. Validation using Regular Singular (RS) analysis, Pixel Difference Histogram (PDH) comparison, and higher-order Gabor feature analysis with an ensemble classifier showed at least a 2\% lower classification accuracy compared to competing methods, indicating improved stego-image stealth. While this work advances texture-aware steganography through handcrafted LBP features and deterministic operations, its adaptability to complex or previously unseen image patterns remains limited. In contrast, deep learning–based approaches, including CNNs and attention mechanisms, offer greater flexibility and semantic understanding, enabling more generalized and data-driven embedding strategies.

\medskip
Abdulazeez et al.~\cite{sabah_abdulazeez2023} proposed an enhanced image steganography approach that integrates the conventional LSB technique with XOR-based encryption and secret-key protection. Two embedding strategies were investigated: a 1-LSB approach, which modifies only the least significant bit of each byte, and a 4-LSB approach, which replaces the four least significant bits of a half-byte. The methods were evaluated using visual inspection, histogram analysis, and quantitative metrics such as PSNR and MSE on both color and grayscale images. Experimental results showed that the 1-LSB method achieved excellent imperceptibility, with PSNR values exceeding 42~dB and minimal histogram distortion. Although the 4-LSB method enabled higher payload capacity, it introduced noticeable visual artifacts and resulted in a lower PSNR of approximately 29~dB, rendering it unsuitable for secure applications. This study demonstrates that basic LSB-based methods are effective for scenarios requiring low payload capacity and high imperceptibility; however, they remain vulnerable to statistical steganalysis, underscoring the need for adaptive and intelligent embedding strategies.The protection of Electronic Medical Images (EMIs) is critically important due to their role in real-time healthcare applications. Traditional steganographic techniques based on LSB and Most Significant Bit (MSB) embedding have been widely used to conceal data within medical images; however, these approaches suffer from limited payload capacity and potential visibility issues. To address these challenges, researchers have increasingly focused on embedding information within the Region of No Interest (RONI), thereby preserving diagnostically significant regions while safeguarding sensitive data.

Sharma et al.~\cite{Sharma2024} conducted a comparative analysis of Edge-Based Steganography (EBS) and Block-Based Steganography (BBS) applied to the RONI of normalized cover images. Their experiments, conducted on a dataset of 5,856 chest X-ray images, demonstrated that EBS outperformed BBS in terms of real-time performance and computational efficiency, while maintaining high imperceptibility and achieving a 100\% embedding success rate. The results highlight the effectiveness of RONI-based embedding techniques for protecting large volumes of medical data in practical healthcare environments.

\medskip
Wang~\cite{Wang2025} introduced AttnEdge, an advanced edge detection framework that combines Pixel Difference Convolution (PDC) with self-attention mechanisms to capture global dependencies and enhance feature representation. The model incorporates post-processing steps, including Gaussian smoothing and adaptive thresholding, to improve edge continuity and suppress noise. Experimental evaluations on the BSDS500 dataset demonstrated that AttnEdge outperforms classical Canny and PiDiNet detectors, achieving higher PSNR and SSIM values, which indicate improved structural accuracy and robustness.

\medskip
Rustad et al.~\cite{rustad2022optimization} addressed the trade-off between embedding capacity and imperceptibility by integrating cross-diagonal PVD embedding with a Modulus Function (MF) approach guided by edge-area patterns. The proposed method aims to improve embedding efficiency while preserving visual quality and security. Evaluations conducted on 14 public datasets reported an average embedding capacity of 3.18~BPP, with PSNR values exceeding 40~dB and SSIM scores above 0.98. The incorporation of edge-aware block pattern embedding contributed significantly to maintaining visual fidelity. Moreover, the method demonstrated robustness against Regular Singular (RS) steganalysis attacks, highlighting its effectiveness in resisting statistical detection.

\section{Transform Domain-Based Methods}
\label{yyyyy}

Recent research in image steganography aims to improve the trade-off between embedding capacity, visual imperceptibility, and resistance to steganalysis. While Least Significant Bit (LSB) substitution offers low computational complexity and relatively high payload capacity, it remains vulnerable to both statistical and visual attacks. The introduction of transform-domain techniques, such as the Discrete Wavelet Transform (DWT) and the Discrete Cosine Transform (DCT), along with hybrid approaches that combine encryption, error correction, and deep learning (DL), has significantly enhanced robustness.

Jasim and Kurnaz~\cite{Jasim2024} developed an advanced steganographic framework by integrating Ant Colony Optimization (ACO) with the LSB method. In the proposed ACO–LSB approach, pheromone-guided exploration is used to select optimal embedding locations, thereby improving security while preserving image quality. The method embeds data using decoy bits for integrity verification and applies checksum validation to detect tampering. Experimental results on the BOSSbase dataset demonstrated a 30\% improvement in embedding capacity, while achieving imperceptibility with PSNR of 40.5~dB and SSIM of 0.98. Furthermore, the method reduced detection rates by 15\% compared to Particle Swarm Optimization (PSO)-based approaches, indicating strong resistance to steganalysis.

\section{Steganography Using Edge and Texture Features}
\label{yyyyy}

Ray et al.~\cite{ray2021image} proposed an image steganography system that combines convolutional neural networks (CNNs) with deep supervision–based edge detection to improve embedding performance and visual quality. Traditional spatial-domain methods such as LSB, PVD, and EMD provide simple embedding mechanisms but often introduce noticeable distortions and exhibit limited resistance to steganalysis. Although transform-domain techniques based on DCT, DFT, DWT, Laguerre, and Catalan transforms improve security, they typically incur higher computational cost and reduced embedding capacity. Edge-based steganography addresses these limitations by embedding data in high-gradient regions, where visual distortions are less perceptible. However, conventional edge detectors such as Sobel, Prewitt, LoG, and Canny often fail under noisy conditions or illumination variations.

Ghosal et al.~\cite{ghosal2021image} introduced an image steganography method based on Kirsch edge detection to achieve higher payload capacity and improved image quality. Traditional approaches distribute secret data uniformly across pixels, leading to significant visual degradation. Although Sobel-, Prewitt-, and Canny-based embedding minimizes perceptual distortion at edge locations, these detectors generate limited edge pixels and remain sensitive to noise. The Kirsch operator addresses these issues by detecting edges in eight compass directions (N, NE, E, SE, S, SW, W, NW), producing a denser edge map due to its wider convolution masks and strong directional weights. The proposed framework consists of three main stages: edge generation, data embedding, and data extraction. The cover image is processed using the Kirsch operator to generate an accurate edge map, and the image is divided into pixel triplets that receive variable numbers of secret bits, with edge pixels allocated more capacity. Experimental results demonstrated an embedding capacity of up to 3~bpp while maintaining PSNR above 34~dB and SSIM values between 0.90 and 0.99. Compared to methods based on Canny and LoG detectors, the Kirsch-based approach achieved 30--40\% higher payload capacity with only a 2--3~dB reduction in PSNR. StegExpose evaluation further showed that 95\% of the generated stego images remained undetected, indicating strong robustness.

Niu and Zhang~\cite{9988162} proposed a texture-perception–based image steganography approach aimed at improving visual quality without sacrificing robustness or embedding capacity. Addressing limitations of deep models such as \textit{HiDDeN} and \textit{StegaStamp}, which often introduce visible artifacts in low-frequency regions, the authors integrated explicit texture analysis with an enhanced StegaStamp encoder. Texture features were extracted using the gray-level co-occurrence matrix (GLCM), including energy, entropy, contrast, correlation, and inverse difference moment, to compute region-wise texture complexity scores. Secret messages were preferentially embedded in high-complexity regions where modifications are less perceptible. The encoder architecture was augmented with additional convolutional layers and a squeeze-and-excitation (SE) attention module to emphasize informative feature channels. Experiments on the MIRFLICKR and VOC2012 datasets reported improved perceptual quality, achieving an average PSNR of 33.79~dB and SSIM of 0.92, compared to StegaStamp's 30.48~dB and 0.87, respectively, while preserving performance even with a 50\% payload increase.

The protection of Electronic Medical Images (EMIs) is critical due to their importance in real-time healthcare applications. Traditional LSB- and Most Significant Bit (MSB)-based techniques are commonly used but suffer from limited payload capacity and potential visibility issues. To address this, researchers have explored embedding information within the Region of No Interest (RONI) to preserve diagnostically important regions. Sharma et al.~\cite{Sharma2024} conducted a comparative study of Edge-Based Steganography (EBS) and Block-Based Steganography (BBS) applied to the RONI of normalized medical images. Experiments conducted on 5,856 chest X-ray images showed that EBS outperformed BBS in terms of computational efficiency and real-time performance, while maintaining high imperceptibility and achieving a 100\% embedding success rate. The results highlight the effectiveness of RONI-based techniques for secure medical data protection.

\section{Deep Learning-Based Steganography}
\label{yyyyy}

Das et al.~\cite{das2021multi} proposed a deep learning–based steganographic framework capable of embedding multiple secret images within a single cover image of identical resolution. Extending Baluja’s 2017 model, the architecture incorporates multiple preparation and reveal networks, each responsible for processing a distinct secret image. The encoder concatenates the outputs of the preparation networks with the cover image and passes them through a hiding network, while individual reveal networks reconstruct the corresponding secrets. By distributing hidden content across the full pixel depth rather than relying on LSB manipulation, the framework successfully embedded and recovered three full-color images from the Tiny ImageNet dataset with low visual distortion, demonstrating the feasibility of multi-secret, same-size steganography via end-to-end training.

Płachta et al.~\cite{plachta2022} investigated the effectiveness of shallow ensemble classifiers and deep learning models in detecting steganography in JPEG images. The study evaluated three advanced embedding schemes—nsF5, J-UNIWARD, and UERD—under embedding densities of 0.1 and 0.4~bpnzac. Detection models were trained using residual features from Deep Convolutional Texture Residuals (DCTR), Gabor Filter Residuals (GFR), and the Phase-Aware Projection Model (PHARM). Results showed that ensemble classifiers using linear regression on DCTR features matched or outperformed deep neural networks while requiring significantly less computational cost. Detection accuracy reached up to 99.9\% for nsF5 at 0.4~bpnzac, whereas detecting low embedding rates such as J-UNIWARD at 0.1~bpnzac remained challenging. The findings suggest that well-tuned ensemble methods can rival DL-based detectors, with DCTR and GFR emerging as effective feature representations.

Kumar et al.~\cite{KUMAR2023} proposed a deep learning–based image steganography framework built on an encoder–decoder architecture using a U-Net backbone enhanced with dilated inception modules. The system embeds a secret image into a cover image through joint end-to-end training of hiding and reveal networks. Multi-scale feature extraction via dilated convolutions enables high embedding capacity while preserving imperceptibility, and the decoder reconstructs the secret image with minimal distortion. Evaluations on the ImageNet and Pascal VOC datasets reported PSNR values exceeding 37~dB and SSIM values up to 0.99 for both stego and recovered images. Although the model achieves strong visual quality and robustness, it still faces challenges related to training complexity and occasional color fidelity degradation during reconstruction.


%The current research in image steganography emphasizes developing methods that combine superior imperceptibility with robustness and high embedding capacity. The widespread adoption of LSB substitution as a method continues because of its basic nature, but it produces noticeable distortions and remains susceptible to statistical steganalysis attacks. Researchers have integrated DL methods, including autoencoders, Generative Adversarial Networks (GANs), and neural decoders, to improve visual quality and minimize distortion in stego images. Verma et al. \cite{Verma2024} presented a hybrid system that merged LSB embedding with DL-based image decoding and SSIM perceptual loss functions to produce better visual quality and less pixel degradation. The research demonstrated how combining image enhancement techniques with decoder-based recovery methods produces high-quality imperceptible steganography, especially when visual integrity matters in specific applications.

%Research on DL methods, including GANs for improving image steganography, security, and adaptability, has grown substantially throughout the last few years. The embedding algorithms S-UNIWARD, HILL, and MiPOD operate with fixed cost functions that depend on local image structures. Yet, these limitations restrict their adaptability and resistance to steganalysis attacks. Wang et al. \cite{wang2024gan} created a GAN-based framework that trains adaptive embedding cost maps through a dual-stream U-Net generator that integrates Convolutional Spatial Attention (CSA) with image enhancement modules. The Automatic Steganographic Distortion Learning framework with GAN (ASDL-GAN) and UT-GAN models received enhancements through adversarial training and architectural improvements for cost learning. The approach encountered difficulties during training stability and detection precision. The research of Steganographic Pixel-wise Actions and Rewards with Reinforcement Learning (SPAR-RL) and CF-UT-GAN explored RL and feedback-based optimization to enhance embedding decisions. The approach delivers improved cost map accuracy through its detailed attention to edge and texture regions. The experimental results demonstrated that their method enhances resistance to advanced steganalysis models, which resulted in a significant advancement for adaptive image steganography with attention guidance.

Current research in image steganography emphasizes the development of methods that achieve high imperceptibility while maintaining robustness and high embedding capacity. Although Least Significant Bit (LSB) substitution remains widely adopted due to its simplicity, it often introduces noticeable distortions and remains vulnerable to statistical steganalysis attacks. To address these limitations, researchers have increasingly integrated deep learning (DL) techniques, including autoencoders, Generative Adversarial Networks (GANs), and neural decoders, to enhance visual quality and minimize distortion in stego images. Verma et al.~\cite{Verma2024} presented a hybrid framework that combines LSB embedding with DL-based image decoding and Structural Similarity Index Measure (SSIM)–based perceptual loss functions to improve visual quality and reduce pixel degradation. Their study demonstrates that integrating image enhancement techniques with decoder-based recovery can produce high-quality and imperceptible steganography, particularly in applications where visual integrity is critical.

Research on DL-based methods, including GANs, for improving the security and adaptability of image steganography has grown substantially in recent years. Traditional embedding algorithms such as S-UNIWARD, HILL, and MiPOD rely on fixed cost functions derived from local image structures, which limits their adaptability and resistance to advanced steganalysis attacks. To overcome these constraints, Wang et al.~\cite{wang2024gan} proposed a GAN-based framework that learns adaptive embedding cost maps using a dual-stream U-Net generator that integrates Convolutional Spatial Attention (CSA) with image enhancement modules. Their Automatic Steganographic Distortion Learning (ASDL-GAN) framework and UT-GAN model employ adversarial training and architectural enhancements to improve cost-map learning, although challenges related to training stability and detection precision remain. In addition, approaches such as Steganographic Pixel-wise Actions and Rewards with Reinforcement Learning (SPAR-RL) and CF-UT-GAN have explored reinforcement learning and feedback-based optimization to refine embedding decisions. These methods improve cost-map accuracy by focusing on edge and texture regions, and experimental results demonstrate enhanced resistance to advanced steganalysis models, representing a significant advancement in adaptive, attention-guided image steganography.

\section{Attention-Guided Methods}
\label{yyyyy}

Traditional image steganography techniques, including LSB substitution and transform-domain approaches, often exhibit limited robustness and visual quality. The emergence of deep learning has led to the widespread adoption of encoder–decoder architectures, which are well suited for learning complex hiding and extraction transformations. Recent research has focused on incorporating attention mechanisms to improve feature selection during the embedding process. Kumar et al.~\cite{Kumar2024} proposed StegaVision, which integrates channel-wise and spatial attention modules into an encoder–decoder framework and evaluates five different attention configurations. Their results show that the parallel application of channel and spatial attention yields superior PSNR and SSIM values, thereby achieving improved trade-offs between hiding capacity and visual quality. This work highlights the growing effectiveness of attention-guided deep steganography systems for secure data hiding with high visual fidelity.

Wang~\cite{Wang2025} introduced AttnEdge, an advanced edge detection framework that combines Pixel Difference Convolution (PDC) with self-attention mechanisms to capture global dependencies and enhance feature representation. The model incorporates post-processing steps, including Gaussian smoothing and adaptive thresholding, to improve edge continuity and suppress noise. Experimental evaluations on the BSDS500 dataset demonstrate that AttnEdge outperforms classical Canny and PiDiNet detectors in terms of PSNR and SSIM, indicating improved structural accuracy and robustness.

\section{Counting based Secret Sharing }
\label{yyyyy}

Shamir  \cite{shamir1979share} and  Blakley \cite{blakley1979safeguarding} independently initiated the threshold-based secret sharing scheme. Shamir's scheme is based on polynomial interpolation whereas Blakley's scheme is based on geometry. Later, various threshold-based secret sharing  schemes  have been proposed to enhance security, reduce computation cost, increase reliability, scalability, capacity, and robustness of  secret sharing schemes. %Some of those schemes improved the performance of Shamir \hl{(Shamir, 1979)  and Blakley (Blakley, 1979), use references} methods and the remaining focused on other aspects like the number of shared secrets, share weight, changeability of shares and users’ rights, etc. %We will briefly describe some of these related works in this  chapter, which are classified into several categories.
%\subsection{Shamir's Threshold Secret Sharing Scheme}
 Shamir's threshold secret sharing method uses Lagrange interpolation polynomial to get (k, n) threshold  scheme. This method makes a random (k-1) degree polynomial as $q(x) = a_0 + a_1x + \ldots + a_{k-1}x^{k-1}$ assuming ($a_0$) is the secret key $S_k$.
 %\in \mathbb{F}_p$ and $a_i \in \mathbb{F}_p$, where (p) is a prime number. The dealer
The polynomial helps to generate n shares using $S_i = q(i) \ldots S_n = q(n)$ and then sends them to participants through a secure channel. The secret key  $S_k$ can be reconstructed using the polynomial and by selecting a subset  $S_t$ from $\{S_i.... S_n\}$.
%Then we can construct the polynomial and recover the secret by selecting any subset t of those$S_n$ values, but cannot calculate the secret with (k-1) participants. 
%But in Shamir's threshold scheme, %has  some features that make it secure. 
%the number of shares does not override the size of the secret  key. Also, the scheme is to be expandable, i.e. when (k) is  fixed, the shares can be dynamically deleted or added without impact on the other shares. The system can have  some changes within the shares without alteration of the secret key, and without the need to generate new shares  and present them to the participants. 
Shamir's scheme is thought to be a hierarchical scheme, by  which it can provide each participant with a various number of shares based on their importance. \cite{shamir1979share}. Blakley's method \cite{blakley1979safeguarding} depends on geometry to achieve $(k, n)$ threshold-based scheme. The secret is represented as a point $(P)$ in the vector space %\hl{ $\mathbb{Fqk}$ and $(n)$, -double check}
and n different shares are created using the point P in a (k-1) dimensional hyperplane. The (k-1)  dimensional  hyperplane is of the form:
$\{ (\mathcal{X}_1, \ldots, \mathcal{X}_t) \in \mathbb{F}_q^k \, | \, \alpha_1\mathcal{X}_1 + \ldots + \alpha_k\mathcal{X}_k = \beta \}$,
where  $\alpha_1,\ldots,\alpha_k$ and $\beta$ help to represent arbitrary points of the field $\mathbb{F}_q^k$. The  secret is thus obtained by intersecting k hyperplanes at P but fewer than k hyperplanes will also intersect in some subspace containing P. So,  Blakley's scheme allows fewer than k participants to be able to recover the subspace but will not ensure the recovery of the secret correctly. That's why,  Blakley’s mehtod is not considered preferred scheme because the unauthorized group of participants may know part of the subspace where the secret is and so may guess the secret \cite{blakley1979safeguarding}. The scheme is then improved by Simmons \cite{simmons1991introduction} to make it acceptable by using affine space instead of vector space.
%\subsection{Hierarchical Secret Sharing Schemes}
A multi-level secret sharing scheme has also been presented by Simmons \cite{simmons1991introduction} 
%then by presented Brickell \cite{brickell1989some}.  
In the multi-level access structure, %there are perfect schemes according to Brickell. In these 
the members of an organization are grouped in hierarchical structure, and a secret is shared among the groups and %However, there is a need  to share the secret among all the members of the organization 
this sharing strategy includes the consideration of groups at different levels having different privileges%, and thus there is a need to consider the power in sharing the information 
~\cite{tassa2007hierarchical}. 
%The information is shared based on a fixed sequence of threshold requirements. Such thresholds need a member with  a higher power to enhance the organization’s secret, as in Tamir Tassa's Hierarchical Secret  Sharing \cite{tassa2007hierarchical}.
Tamir Tassa's hierarchical secret sharing approach \cite{tassa2007hierarchical} %, the perfect secret sharing  has been introduced to
also improves the threshold-based secret sharing strategy where the secret  is divided among a set of users based on their position in an organization. This protocol uses polynomial derivatives to produce fewer shares for low level participants %than Shamir's scheme %which represents the secret as the free coefficient of some polynomials. This scheme
%and reconstructs the secret using Birkhoff interpolation.% as discussed to determine the identity of  participants from the underlying finite field.
%\subsection{Secret Sharing Schemes Based on Weighted}
%Again, some threshold-based secret sharing schemes have been presented to be able to 
and give more shares to the important and powerful persons in organizations. More weights can be considered for the shares provided to the executives whereas the weights are less on the shares provided to the low-profile entities.%; where the president, for example, has three shares,  the vice president has two shares, and then each of the other executives has only one share, The system threshold is 3. 
%Any three shares can be combined from the given participants' shares to be valid 
The reconstruction of the secret will only be possible if and only if the sum of the weights of the participants is greater than or equal to the fixed threshold. %In Shamir's scheme  \cite{shamir1979share}, the idea was to use a $(k,n)$ threshold where each participant is granted the weight of shares based on his power in the company or organization.
 
%Beimel et. al. \cite{beimel2005characterizing} are characterized with all weighted threshold access structures as considered implementable. They showed that weighted threshold access structure is possible if and only if it is a hierarchical threshold access structure or a triple access structure or a structure of two ideal weighted threshold access structures that are determined on smaller sets of users. Through all those cases the weighted threshold access structure may be achieved by a linear ideal secret sharing scheme. Morillo \cite{morillo1999weighted}, dealt with weighted threshold schemes through the property of information rate. The access structures of weighted threshold schemes presented the complete characterization of all the minimal authorized subsets that have involved at most two elements. Lower bounds for the optimal information rate of these access structures are given and can be further referred to as clarified in \cite{morillo1999weighted}.

%\subsection{Secret Sharing Based on parallel counting}

Al-Qurashi et al. \cite{al2018reliable} increased the reliability of the counting-based secret sharing scheme by increasing the size of the S$_K$ to 64 bits and considered it as an adequate size for real-time application. The authors also improved the secret sharing algorithm by verifying the randomness of S$_K$ sequence when it is generated. 
%With This study secret key atS$_K$ aimed assessing generation the reliability intruder of S$_K$ only by try checking to its guess randomness and thereby seeking to enhance its effectiveness. 
The Java Random function generator was used to test the randomness of S$_k$ and to generate an effective pseudorandom sequence.  The fascinating randomness characteristics of S$_k$ have been evaluated by two standard statistical tests from NIST 800-22 suites \cite{gutub2020secure}. %Secure shares generation via M-blocks partitioning for counting-based secret sharing. Journal of Engineering Research, 8(3)."]}: the frequency (monobit) test and the frequency test within a block. 
Finally, the reliability score of S$_k$ is considered the highest when S$_K$ passes the significance threshold $\alpha$ =  0. 05 for both frequency tests. Such highly reliable S$_K$  can provide optimized and suitable secret shares for counting based secret sharing scheme. 

Al-Ghamdi et al. \cite{al2019security} attempted to solve some of the issues of the secret sharing scheme that affect the security and efficiency of the scheme. They used some enhanced methods for the share-generation based on their original 1-bit and 2-bits models to address the challenges that may arise in the context of the length and structure of the secret share. 
%The research also went further to solve the problems that were identified with the initially counting-based developed secret-sharing generation methods.  
Their work has been tested using a secret verification tool which ensures that the secret targets are in a secure form with a certain length and structure. %Furthermore, new and further merged generation methods have been suggested as developments of the current basic generation methods including the same
%Their new methods also considered the bits of value in the secret target S$_K$ as well as the merged methods and the new methods as the combination of both the new and the basic methods. 
%The various research possibilities identified the best outcomes with different possibilities depending on the varying lengths of our secret findings. 
Finally, their proposed model assumes that when the length of the secret target S$_K$ is greater than 20-bits it takes longer time to generate the shares.
Gutub et al. \cite{gutub2020secure} laid down the foundation of share generation in counting-based secret sharing with M-block partitioning models. The design of their system also enhanced security by increasing the size of S$_K$ to 64 bits. Their M-block models are categorized as  1-block, 2-blocks, 4-blocks, and 8-blocks models. All theses models generate shares using blocks but integrates the original 1-bit and 2-bits methods applied in each block. %The models' benefits and weaknesses have been analyzed in detail and all the possibilities were tried out %and the findings were then generalized with the assumptions of the open testing of secret keys as if for real-life situations. 
The authors computed the averages of the similarity rate among all the shares generated by different models and they also calculate the average of similarity rates of shares with the S$_K$. The outcomes revealed that the average of the similarity among the shares varies based on the type of block, and the estimated similarity rates for the 1-block, 2-blocks, 4-blocks, and 8-blocks models were on average 78\%, 66\%, 47\%, and 28\% respectively. 
Gutub et al. \cite{gutub2019counting} suggested a new approach for secret sharing that generates the secret shares in parallel. %done counting based of on the bits in the shares in order to generate the secret target key S$_K$ output. 
%The work came up with two different approaches for the secret share distribution: the 1-bit approach and the 2-bits approach; both of which were analyzed with their strengths and weaknesses. 
The authors also analyzed the trade-off between the number of secret shares to be chosen with the target key confirming the level of security needed for the application. %It was quite interesting that 
Their study also emphasized the variations in the selection of target key S$_K$ based on high, medium, or low level security requirement which can be corelated with the number of shares that can be used. %The outputs of the modeling and simulation have been very compelling to a point of encouraging further creativity in the share generation methods. The research work and results are expected to be very interesting as a basis for further research in this direction of secret sharing techniques and applications.

\section{The Gaps}
\label{yyyyy}

The current state of image steganography shows multiple operational limitations which prevent its widespread adoption. The current methods fail to achieve proper equilibrium between capacity and imperceptibility and robustness when dealing with high payload amounts. The current techniques lack protection against statistical and deep learning-based steganalysis attacks which requires new methods for secure data embedding. The current approaches for image steganography primarily work with fixed-size images and basic datasets yet researchers have not adequately studied their performance with different image types and resolutions. The absence of complete evaluation metrics prevents scientists from performing objective assessments because these metrics must measure both visual quality through PSNR and SSIM and attack resistance. The implementation of complex or adaptive steganographic schemes faces two main challenges which are achieving real-time operation and maintaining efficient computation. The existing research gaps create opportunities to create methods which combine high capacity with security features and general applicability and efficient computation to connect theoretical concepts with real-world usage. The gap is that most existing methods struggle with adaptability because they rely on fixed rules for embedding, ignoring image content. Existing methods often treat all regions the same, leading to higher distortion and easier detection. Few approaches use intelligent guidance or optimization to choose the best areas for hiding data, making it hard to balance between invisibility and capacity, especially on new or varied images.


Despite substantial progress in image steganography, existing techniques still struggle to simultaneously achieve high imperceptibility, large payload capacity, robustness, and resilience against modern steganalysis. Most traditional LSB and transform-domain methods rely on fixed or heuristic rules that ignore local image content, causing unnecessary distortion or inefficient embedding in complex regions. Even contemporary adaptive and deep learning–based approaches often fail to incorporate true content awareness, as they typically overlook the combined contribution of edges, textures, and distance cues when deciding optimal embedding locations. Moreover, current systems rarely include systematic parameter optimization, leaving threshold selection, bit-depth allocation, and embedding strategies suboptimal and inconsistent across datasets. These limitations create vulnerabilities against advanced CNN- and GAN-based steganalysis models, which can easily exploit spatial regularities and fixed embedding patterns. Another important gap lies in robustness and reliability: few methods ensure error-tolerant extraction or consider synchronization loss under noise, compression, or image transformations. Additionally, most steganographic frameworks ignore secure multi-party access or redundancy mechanisms, leaving no built-in resilience against data loss, corruption, or shared-access requirements. Therefore, a comprehensive framework that integrates content-driven attention, keyed adaptive embedding, optimization via evolutionary algorithms, robust header encoding, and threshold-based secret-sharing remains missing from the current literature—an essential gap this dissertation addresses.
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