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\chapter{Introduction}
\label{yyyyy}
As data transmitted over the Internet continues to grow, it has become increasingly important to prioritize its security. The rise of cloud-based applications,  the Internet of Things (IoT), digital commerce networks, social media interactions, etc. has resulted in a large volume of data being created and shared~\cite{wani2023,duan2020}. To protect the confidentiality, integrity, and accessibility of data, cryptography is commonly used. Cryptography involves encoding information in a manner that makes it incomprehensible to individuals~\cite{plachta2022,Himthani2022}. Data hiding (i.e., steganography) differs from cryptography, as it aims to conceal information in such a way that the potential attackers are unaware of the presence of the contents, making it appear natural and inconspicuous rather than making the contents unreadable~\cite{chinniyan2022}. Steganography techniques must guarantee the confidentiality of data by safeguarding it against access, detection, and retrieval from the intruders~\cite{chinniyan_image_2022,zou2019}.

Image steganography is a steganography method used to hide secret information or a secret image within another image in such a way that the existence of the information is concealed. 
In image steganography, there can be three different images : the `secret image' that is concealed and hidden within another image, the `cover image' which is the image that conceals the secret image, and the `stego image' that represents the image after merging the secret image within the cover image. When a secret image is concealed within a cover image using image steganography, it becomes visually hidden and challenging for machines or individuals to extract without specific techniques~\cite{chakraborty2020,SETIADI2023}. A crucial aspect of image steganography is to enhance the capacity to accommodate greater amounts of secret data within the cover image. But increasing the capacity compromises imperceptibility, where imperceptibility refers to the quality of being undetectable or not noticeable. So, the right balance between capacity and imperceptibility is important to ensure the quality of the stego image~\cite{KUMAR2023,sabah_abdulazeez2023}.

Image steganography can be classified into two types: embedding-based steganography and  coverless steganography. In embedding-based steganography, the embedding process is done by modifying the  cover image's pixels. The secret information is directly inserted into the cover image or medium using methods such as LSB (Least Significant Bit) replacement, frequency domain manipulation, or other data concealment techniques~\cite{sedighi2016,Kumar2024}. On the other hand, coverless steganography doesn't depend on  the modification of the  cover image's pixels directly to hide the secret information; rather, it extracts features from the cover image and merges the secret information with those features~\cite{Lin2024,Patwari2025}. Our research aims at finding the appropriate features in the cover image that can accommodate the secret image in an efficient way.


In this research, a composite approach is proposed that combines the advantages of both coverless and embedding-based image steganography to achieve higher security, adaptability, and imperceptibility. Traditional embedding-based methods modify the pixel values of the cover image to conceal secret data, while coverless steganography hides information without altering the image itself—by encoding the secret within extracted image features such as edges or textures. Our work bridges these two paradigms by first adopting a coverless concept to extract robust and discriminative features from the cover image (through edge and texture analysis using deep models like HED, RCF, VGG16, and ResNet50), and then integrating these features into a content-aware embedding process guided by attention maps. This integration enables adaptive data hiding that leverages the perceptual masking properties of natural images while maintaining high payload capacity and low detectability. Thus, the proposed system focuses on a novel contribution in coverless feature-driven analysis combined with a keyed adaptive embedding framework, effectively merging the strengths of both approaches to deliver a secure, intelligent, and perceptually optimized steganographic model.

Traditional image steganography, as shown in Figure~\ref{figure:Architecture22}, functions with an embedding process at the sender end and an extraction process on the receiver side. The system begins with two initial inputs, which consist of the cover image and the secret image. The position-selection module examines the cover image to find appropriate spots for embedding data, which tend to be located in rich areas because such regions allow for minimal perceptible changes. The embedding process adds secret information to the choosen positions in the stego image after the secret image undergoes optional key-based encryption and permutation. At the receiving end, the extraction process utilizes a shared key to select the locations from which to extract the hidden data, allowing for recovery of the secret image~\cite{Lin2024,Patwari2025}.
\begin{figure}[H]
    \includegraphics[width=1.0\linewidth]{Figs/Basic_Ste.PNG}
    \caption{Architecture of Basic Image Steganography}
    \label{figure:Architecture22}
\end{figure}

The challenges and goals in image steganography are --- (1) Imperceptibility: Embedding data into an image using steganography involves blending it to ensure any alterations are undetectable or barely noticeable to observers. The main objective is for the stego image to keep its look appearing unaltered to maintain its integrity while effectively hiding the encoded information. (2) Capacity: Steganography methods strive to maximize the volume of information that can be concealed within a given cover image without compromising its quality. (3) Security: Safeguarding the hidden data within the cover image is essential to stop access or detection. Steganography techniques should resist analysis and extraction attempts by algorithms or adversaries. (4) Robustness: In the field of steganography, it is crucial that the secret data stay preserved and retrievable even if the steganographic image goes through processes like compression, filtering, or resizing. Steganography methods are created to maintain the strength of the information against changes, ensuring that the encoded data remains protected and available despite any adjustments made to the cover image~\cite{Sharma2024,Verma2024}. Existing approaches face difficulties in making a proper balance in achieving those goals, and our research addresses this issue.
 
 
Image steganography is used in different areas such as --- (1) Military operations: In this field, image steganography allows military personnel to conceal sensitive data such as mission plans, troop movements, or intelligence reports within seemingly innocuous images. In this case the military operators can share critical details without drawing attention to the existence of hidden messages~\cite{Panigrahi2025,Ray2021}.(2) E-Health: Medical settings for safeguarding patient data in images like X-rays, MRIs, etc. In this field, image steganography helps in protecting patient privacy, such as his name, medical report number, or other confidential details, by concealing sensitive data within medical images~\cite{Setiadi2024,Liu2024}.(3) Telecommunications for secure communication: In this field, image steganography allows telecommunications professionals to hide sensitive data within images, enabling covert communication channels. By embedding information within seemingly unnoticeable images, parties can exchange data discreetly without drawing attention to the existence of hidden messages~\cite{Wang2025,Abdulhammed2024}.(4) Social Media: Some social media platforms utilize steganography methods for communication and sharing of data. Social platforms can utilize steganography to protect user privacy by allowing individuals to hide sensitive information within images shared on their profiles. This hidden data includes personal details or confidential information that only intended recipients can access~\cite{Jasim2024,Tahbaz2024}. So our goal is to develop an efficient image steganography technique that can support a wide range of applications in those areas.

Secret sharing has several practical applications across different fields. In the banking sector, it can be applied to secure sensitive accounts that require at least k out of n shares to reconstruct a super password or target key (TK). This ensures that important transactions or high-value accounts can only be accessed through multiple authorized parties, enhancing accountability and error correction in financial operations. In voting systems, secret sharing helps maintain trust by requiring k-of-n officials to verify the results, thereby preventing manipulation and ensuring the credibility of election outcomes. In the medical field, the same principle can protect highly sensitive test results, allowing access only when multiple specialists collaborate to reconstruct the target key. This approach not only secures confidential medical data but also supports systems using deep neural networks, such as sparse auto-encoders for brain imaging and micro-bleed detection, by safeguarding the integrity of patient information and analysis results~\cite{gutub2019counting}.To meet these requirements, this research introduces a Counting-Based Secret Sharing (CBSS) mechanism that divides a secret key into multiple shares, enabling access only when a predefined threshold of authorized users collaborates. By integrating the proposed image steganography framework with CBSS, the system not only ensures covert communication and data concealment but also provides secure, threshold-based group access, enhancing both confidentiality and reliability across various high-security applications.

Previous researches~\cite{liu2025color,habiban2024hybrid,singh2025enhanced,patwari2025blockchain}suggest that hiding data in sharp areas (i.e., edges) or richer areas (i.e., texture) of an image makes it less noticeable to the human eye compared to uniform areas~\cite{Xiao2025,Song2024}.This motivates mapping the secret information in the appropriate edge regions or texture regions of the cover image to create a quality stego image. In this work we will build ALIS (Attention-map and keyed LSB-based Image Steganography)—a content-aware bit allocation system driven by predicted attention maps to improve the rate–distortion–security trade-off (higher BPP with lower detectability). The goal of the attention map is to highlight the most important regions in the image, guiding the model to focus more on relevant features. This improves accuracy and efficiency by emphasizing informative areas and reducing attention to less significant ones. In our work, the attention map is developed by considering the features of cover image to identify and include information about the pixels of the cover image to direct the efficient embedding process.So, we will develop optimized deep learning models to build the attention map based on edge and texture detectors and that wil be used for the embedding of a secret image into a cover image. In edge detectors, we will use the HED (Holistically-Nested) and the RCF (Richer Convolutional Features) for developing the deep learning models: The HED is a multi-scale side output and deep supervision to produce precise, thin edges, while the RCF aggregates features across layers  to produce stronger continuity and higher recall on textured regions. HED excels at localization of weak edges; RCF improves recall and stability in textured areas. The output is a normalized edge probability map (0–1) used in attention fusion with the distance map.

On the other hand, we use ResNet50 and VGG16 (pretrained models) as texture detectors. These methods extract the textured area from the mid/high layers (conv blocks) to produce texture feature maps. We extract these texture areas because these areas tolerate modifications better perceptually~\cite{Ji2025,Sanjalawe2025}. The goal is to produce robust edge or texture evidence to guide attention. After generating these attention map by merging the edges or texture maps with the distance maps, we will move to predict the attention maps using CNN and WGAN-GP model. In this stage, we will embed the secret within the cover image guided by these attention maps. In this embedding process, we will use keyed adaptive LSB (Least Significant Bits) that will be optimized using a GA (Genetic Algorithm) integrated with the attention maps' guidance. The motivation behind using these kinds of attention maps is the need for a high payload without visible artifacts and low detectability, and the guided LSB enhances capacity in non-smooth regions (while compared with uniform LSB).The goal of GA is to intelligently select optimal thresholds and bit distributions within the attention map to achieve the best trade-off between quality, capacity, and security.The use of content-aware attention (edges/textures) is to steer embedding, and then the perceptual masking (textures/edges) is used to tolerate perturbations better. In this work, all of the deep learning models that were used to create the attention maps are based on pretrained models to make our image steganography more robust and support a wide range of applications. %because we need strong generic texture cues and stable labels~\cite{Duan20202}.


In the second part of our research, we will develop a counting-based secret sharing (CBSS) algorithm to be used for simultaneous group access to any cloud-based resource (e.g., any confidential file, contracts, bonds, official records, etc.)~\cite{stinson1992explication,naskar2011novel}. CBSS is a method where the secret is divided among multiple participants, and the reconstruction of the original secret relies on counting the number of shares contributed by the corresponding participants. It provides an effective method for secure access to a common resource by multiple participants at the same time and prohibits each individual from accessing it alone. CBSS divides a piece of information (i.e., secret key) among a group of people in such a way that the information can only be put back together when a specific number of participants provide their portions. Each intended participant receives their portion of the secret information by the same sender, and the full details can only be unveiled when enough shares are brought together~\cite{binu2017secure,al2018reliable,al2019security}. This critical threshold is established during the sharing process which plays an important role in reconstructing the original confidential data.

In the example scenario shown in Figure~\ref{figure:Counting22}, we have a secret key (or a password). This secret key can be divided into different shares using CBSS, and each share can be assigned to a specific user. Again, there is a contract file encrypted by the sender with the same secret key and stored it in the cloud storage. If multiple recipient users (those have a share of the secret key) want access to this contract file, CBSS can allow it if a certain number of users (i.e. the threshold) meet to access the contract file together, other wise the request is rejected.  
%\begin{figure}[h]
   % \includegraphics[width=1.0\linewidth]{Figs/New_CBSS.PNG}
  %  \caption{Counting based secret sharing Scenario}
   % \label{figure:Counting22}
%\end{figure}

\begin{figure}[h]
    \includegraphics[width=1.0\linewidth]{Figs/New_CBSS (1).PNG}
    \caption{Counting based secret sharing Scenario}
    \label{figure:Counting22}
\end{figure}
Finally, in our research, we will integrate the counting-based secret sharing concept with our image steganography systems, and the developed image steganography, ALIS will be used to  transmit the secret shares to corresponding users through the communication channels. In this ALIS-specific integration, the secret share will be considered as a secret image and then will be embedded in the cover image.


\section{Motivation}
\label{yyyyy}
In cryptography, when sending a message between two parties, the ciphertext can be seen by anyone and potentially attract  attention. The security derived from using steganography relies on the assumption that no one would suspect data within a cover medium. However, to ensure further benefits, steganography can also be used along with cryptography by hiding the encrypted data. Image steganography addresses the issue of discreetly transmitting confidential information through digital images. Its focus lies in concealing the presence of data, thereby making it challenging for individuals or automated systems to uncover or extract the concealed content~\cite{rustad2022optimization}.In this work, our motivations are:

\begin{itemize}
\item \textbf{Improved imperceptibility:} 
Directing embedding towards edges and textured regions places modifications where human vision and natural image statistics are less sensitive. This reduces visible artifacts compared to random or uniform LSB embedding and typically improves quantitative fidelity metrics (e.g., Peak Signal-to-Noise Ratio ( PSNR), Structural Similarity Index Measure (SSIM).


\item \textbf{Higher effective payload with preserved quality:} 
Adaptive embedding allocates more BPP (Bits-Per-Pixel) to ``safe'' regions (high texture / edge) and fewer bits to smooth regions. The result is higher capacity without proportional loss in visual quality.

\item \textbf {Better resistance to steganalysis:}
Selecting embedding locations using attention maps breaks regular patterns that many steganalysis detectors target. Adaptive, content-aware selection reduces detectable statistical fingerprints and lowers detection metrics compared to naive LSB.

\item \textbf {Smart parameter tuning with genetic algorithms (GA):} 
A GA can automatically search for near-optimal parameters such as attention thresholds, per-region bit allocation, and selection strategies when multiple objectives exist (maximize payload, maximize PSNR/SSIM, minimize detectability). This avoids manual tuning and identifies trade-offs across competing goals.

 \end{itemize}

The research motivation focuses on creating an optimized steganographic system that uses texture and edge analysis and attention map prediction to enhance embedding operations. The proposed system uses deep learning (DL) models to predict attention areas, which it then applies to an adaptive embedding method to achieve maximum payload capacity and preserve both image quality and steganalysis resistance. The system achieves statistical indistinguishability between hidden information and natural image noise through the implementation of whitening and keyed operations, which boost its security level. The research aims to unite traditional steganography methods with contemporary deep learning attention mechanisms to create a dependable method for secure image transmission in operational environments.
This research is also motivated by the integration of (CBSS) with image steganography to achieve a higher level of loss resilience. CBSS divides confidential information into multiple shares, where only a specific number of shares are required to reconstruct the secret. By distributing a secret across multiple shares using a CBSS scheme, the system becomes robust to the loss or corruption of some carrier images. Even if a subset of the stego images is lost, damaged, or intercepted, the original secret can still be reconstructed as long as the intended entity collects at least the threshold number ($t$) of valid shares. This threshold property prevents single-point failure—no single stego image contains enough information to recover the secret—while allowing graceful degradation. The more shares available above ($t$), the higher the reconstruction reliability (especially when combined with lightweight error correction). In practical deployments, choosing ($n$) and ($t$) balances redundancy, storage/transmission overhead, and the desired level of fault tolerance.

\section{Research Gap}
\label{yyyyy}

The reason behind this research is to fill the gaps in research identified in the field of image steganography and contribute to the existing body of literature. The main research gaps targeted by this study include: (1) Lack of content awareness in embedding: Many traditional and even adaptive LSB methods embed data uniformly or based on simple pixel statistics, without considering perceptual relevance or visual saliency. This often leads to visible distortions or detectable artifacts. This research introduces attention-guided embedding using edge and texture awareness to focus on perceptually less sensitive regions. (2) Absence of intelligent parameter optimization: Parameter selection (e.g., embedding depth, threshold levels) in existing works is often manual or heuristic, leading to suboptimal performance. This research integrates a Genetic Algorithm (GA) to automatically optimize parameters for multi-objective performance (imperceptibility, payload, and security). (3) Insufficient resistance to advanced steganalysis: Earlier approaches were mainly tested against simple statistical detectors and are easily exposed by modern machine-learning-based steganalysis. The proposed method employs attention-guided variability to disrupt statistical regularities and is evaluated against advanced detection models. This work conducts a rigorous, multi-dimensional evaluation using benchmark datasets and multiple quality, capacity, and detectability metrics.

\section{Problem Statement}
\label{yyyyy}

In image steganography, this is important to hide data effectively, making it difficult for both humans and automated systems to detect or extract the concealed content. On the other hand, there are challenges faced by image steganography, such as ensuring imperceptibility, capacity, security, and robustness. Adding more data to the image can lead to noticeable changes, affecting its imperceptibility. On the other hand, hiding too little data may render the stenographic system ineffective for practical use. Research needs to balance the concealment of data with maintaining imperceptibility. In this case, achieving a harmonious blend of concealment, data capacity, and imperceptibility will be a crucial aspect. 

Finding the right balance between concealing an amount of data and maintaining imperceptibility poses a challenge. When more data is added to the image, noticeable changes might occur, risking its imperceptibility. Conversely, if little data is hidden the stenographic system may not be effective for real world applications~\cite{patwari2025blockchain}. The objective is to hide an amount of information without altering the original image or media content. In order to proficiently embed data within images, steganography can be viewed as a problem of optimization. Different methods and algorithms have been created to tackle this issue focusing on maximizing data capacity while reducing any noticeable distortions caused by the concealed information~\cite{singh2025enhanced}.

Many current techniques for hiding data in images focus mainly on how the data is embedded without taking into account factors like where in the image it should be placed or the content of the image itself. This oversight can restrict how much data can be securely hidden in an image. To ensure concealment of data it is important to assess the characteristics of the image and locate spots for mapping that minimize any noticeable changes. Most existing methods embed data uniformly across an image or use simple adaptive rules based on pixel intensity, without adequately considering human visual perception or local image complexity. As a result, these methods often introduce detectable distortions, produce suboptimal embedding efficiency, and remain vulnerable to modern statistical and deep learning–based steganalysis tools. Furthermore, many previous approaches lack systematic parameter optimization, relying instead on fixed or empirically chosen thresholds, which reduces adaptability across different image types and conditions.
Therefore, there is a critical need for an attention-guided, adaptive, and optimizable steganographic framework that dynamically allocates embedding capacity based on edge and texture attention, uses intelligent optimization (e.g., genetic algorithms) for parameter tuning, and undergoes rigorous evaluation to ensure high imperceptibility, robustness, and security against detection.

Again, the secret key is shared between entities, and any intended resource can  be accessed using the secret key by the receiver individually. But, there are some cases where a group of people (with a threshold number) need to access the resource at the same time, otherwise no one should have access to the resource. Researches  also need explore an efficient algorithm to achieve this group oriented resource access mechanism.

\section{ Research Objectives }
\label{yyyyy}

The main objectives of this research is to design and evaluate an attention-guided adaptive LSB steganography framework that intelligently embeds secret data into images while maximizing imperceptibility, payload capacity, and resistance to steganalysis. To achieve this overarching goal, the study pursues the following specific objectives:

\begin{itemize}
       
    \item To develop an attention-guided embedding mechanism that identifies and prioritizes edge and textured regions within images, allowing data to be hidden in perceptually less sensitive areas.
    \item To implement an adaptive bit allocation strategy that adjusts the number of embedded bits per pixel according to local image complexity, balancing payload size and visual quality.
    \item To integrate optional optimization through a Genetic Algorithm (GA) to automatically tune critical embedding parameters (e.g., thresholds,and  region weights) for optimal trade-offs among multiple objectives.
    \item To conduct a rigorous experimental evaluation using benchmark datasets to measure image fidelity (PSNR, SSIM), payload capacity (bpp), extraction reliability (BER), and resistance to detection (AUC/Accuracy).
    \item To design an effective (CBSS) method to integrate with image steganography to get higher reselience data loss.
    
 \end{itemize}

\section{Research Questions}
\label{yyyyy}

This study seeks to answer the following research questions:

\begin{itemize}
\item RQ1: How can attention mechanisms based on edge and texture features be leveraged to guide the keyed adaptive LSB steganography for improved imperceptibility and security?

\item RQ2: To what extent does the keyed adaptive bit allocation, guided by local image characteristics, enhance the trade-off between payload capacity and visual quality compared to traditional uniform embedding?

\item RQ3: Can Genetic Algorithm (GA)-based optimization effectively tune key embedding parameters (e.g., thresholds, bit allocation ratios, attention weights) to achieve optimal performance across multiple objectives?

\item RQ4: How resistant is the proposed approach to modern steganalysis techniques, including deep learning-based detectors?

\item RQ5: What are the comparative advantages of the proposed method over baseline and existing adaptive or deep-learning-based steganography models in terms of fidelity, payload, and detectability?

\item RQ6: Can the integration of CBSS with image steganography increase the data loss resilience?
\end{itemize}

\section{Research Contributions}
This dissertation makes several significant contributions to the field of image steganography and secure key sharing. First, it introduces ALIS, a novel attention-guided and keyed adaptive LSB steganography framework that unifies coverless feature extraction with embedding-based methods. The framework leverages deep edge and texture detectors (HED, RCF, VGG16, ResNet50) to generate content-aware attention maps and uses them to drive pixel-level adaptive bit allocation, achieving higher imperceptibility, improved payload capacity, and reduced detectability. Second, the work integrates a deterministic HMAC-DRBG–based keying mechanism, pixel permutation, and optional whitening to enhance security and resist modern steganalysis attacks. Third, a Genetic Algorithm is incorporated to automatically optimize thresholds and bit-allocation strategies, offering an intelligent multi-objective balance between PSNR, SSIM, and BPP constraints. Fourth, the research proposes a compact mini-attention–in-header design that enables robust extraction and synchronization even under noise or compression. Finally, the dissertation introduces an enhanced Counting-Based Secret Sharing (CBSS) scheme using block masking and threshold-controlled reconstruction, and demonstrates its integration with the steganographic framework to achieve distributed, loss-resilient, and secure multi-party access to hidden information. Together, these contributions form a comprehensive, adaptive, and secure steganographic system that advances both the theoretical and practical capabilities of modern data hiding.

This research proposal describes the related works in Chapter 2. Chapter 3 demonstrates the solution methodology ,chapter 4 demonstrate the computation of attention maps process, the evaluation strategy and results analysis are included in Chapter 5. Finally, Chapter 6 concludes this proposal.
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% \subsection{Introduction}
% The security of data within organizations against threats is becoming more important than before and the required confidentiality can be achieved through cryptography ~\cite{al2018reliable} \cite{shambour2022progress}. However, the traditional cryptographic techniques often assume that the access to the encrypted data within the system is controlled by a single entity. The problem arises if the encryption key for the encrypted data is lost \cite{alkhodaidi2022scalable} \cite{al2022securing}. This indicates the case where there is security without reliability, as losing the encryption key leads to data loss and makes access to the secret data impossible \cite{alaseri2018merging}.
% The management problem of such centralized resource access can be resolved by distributing copies of the secret key, $S_k$ to more than one person, which eventually allows sharing the control\cite{al2019security} \cite{gutub2020hiding}. This strategy may reduce the confidentiality and may make data exposed to a greater risk, such as data being modified or destroyed by other(s) either intentionally or unintentionally. But secret sharing is a technique that needs to ensure both reliability and confidentiality of the encrypted data at the same time \cite{al2019security} \cite{al2022increasing}. Also, it needs to minimize the dependence on a single entity and introduce the possibility of group decision-making and shared responsibility, thus enhancing security \cite{alkhodaidi2021refining}. The secret sharing approach specially becomes useful in scenarios where data has to be protected by more than one person. Traditional secret sharing schemes provide robust security but do not perform well in resource-constrained environments though some schemes like polynomial-based secret sharing, boolean operation secret sharing, etc. are well known for threshold flexibility and reducing computational and memory complexity in a limited manner\cite{pei1996chinese}\cite{iftene2007general}.
% CBSS is a very similar approach that partitions the $S_k$  between a set of participants, where a subset of participants must be available at the same time in order to gain access to the data \cite{gutub2023dynamic}. It does not permit participants to reconstruct the $S_k$  individually rather demands a set of participants (known as a threshold, \texttt{k}) out of the total number of participants (\texttt{M}) to reconstruct the $S_k$  for the authorized and collaborative access to the encrypted contents\cite{al2018reliable}. CBSS approaches introduce parallel bit counting operations and perform better in balancing between security and minimizing efforts for shared keys generation and $S_k$  reconstruction\cite{beimel2005characterizing} \cite{al2022securing}. As CBSS schemes can optimize resource usage so can offer light-weight cryptographic solutions for resource-constrained devices, but the effectiveness of CBSS highly depends on the generation of appropriate shares\cite{qin2021verifiable}. 

% Previous secret sharing based researches focused on security aspects of the $S_k$ , creation of secret shares ($\mathrm{Sh}_x$), transmission of Sh$_x$ through a secure channel to the participants and reconstruction of the $S_k$  from some of the $\mathrm{Sh}_x$ in a secured manner\cite{gutub2020secure}. Gutub et al.\cite{gutub2019counting} proposed 1-bit method and 2-bits method for CBSS where these methods mostly focus on the zero bits available in the $S_k$. The 1-bit method creates all the possible Sh$_x$ depending on the zeros present in the  $S_k$. The 1-bit method produces a valid share by selecting a 0 (zero) from a specific position of $S_k$then flipping it to 1 (one). Similarly, the 2-bits method select two zeros present in $S_k$ and flip them to 1s to produce a Sh$_x$.  

% Gutub et al.~\cite{gutub2024novel} also improved the security and strength of the 1-bit method and 2-bits method by updating the S$_K$ reconstruction strategy based on a threshold value, \texttt{K}. In their schemes, the total number of Sh$_x$ cannot be greater than the total number of zeros present in S$_K$ and if a S$_K$ contains less number of zeros then the total number of Sh$_x$ generated from that S$_K$ will also decrease proportionately. The possibility of secret shares (Sh$_x$) scarcity highlights the limitation of both 1-bit method and 2-bits method and can not allow them to be a general solution for any randomized S$_K$. The authors also tried to improve the performance of their proposed strategy using block-based modeling and with the integration of 1-bit method and 2-bits method \cite{gutub2020secure}, but these improvements still have limitations in creating a large number of shares due to high dependence on the zeros present in S$_K$.

% So there is a need to overcome this limitation related to Sh$_x$ scarcity and develop a new counting based secret sharing scheme that can also be characterized by low complexity, fast computation, simple operation but be highly secure and reliable \cite{alkhodaidi2022scalable}. In this research, we propose block masking based CBSS to create secret shares Sh$_x$ from the original S$_K$ by dividing the S$_K$ into multiple adjacent blocks and considering each block separately and XOR it with a specific sequence of ones to generate the secret shares\cite{binu2016secret} \cite{patil2022improved}.
% Our proposed approach differs from Gutub et al. \cite{gutub2020secure} as we need not to look at the type of the bit present in the S$_K$. As a result, it overcomes the main challenge present in the schemes of Gutub et al.\cite{gutub2019counting} i.e. the dependency on the presence of 0s in the S$_K$.Our developed CBSS scheme can generate the required number of Sh$_x$ from any random S$_K$ and then distributes the secret shares Sh$_x$ among participants in a manner that allows the S$_K$ to be reconstructed or combined together only when a certain number of Sh$_x$ are brought together. In this version of CBSS an important threshold value, \texttt{k} is also considered which identifies a minimum number of secret Sh$_x$ that will be able to recover the S$_K$ successfully. Our CBSS plays an attractive role in advancing collaborative access to an encrypted data and in improving security by increasing the size of the S$_K$ to any number of random bits. 


% Again, the secret shares Sh$_x$ generated by Gutub et al.\cite{gutub2019counting}'s 1-bit and 2-bits methods have a little difference among themselves \cite{gutub2020secure}\cite{smid2010statistical}. This makes it very easy to predict the secret key S$_K$ from the secret shares Sh$_x$ and also to guess the remaining Sh$_x$ from any two Sh$_x$. This is completely undesirable in the secret sharing scheme and it provides the lowest level of security because it is violating the confidentiality property and \cite{pei1996chinese}\cite{morillo1999weighted} state that there should not be any part of the system that can provide hints about the S$_K$.  Therefore, we have to make sure that the Sh$_x$ are as random as possible and cannot be related to the S$_K$ in any way. Our proposed approach makes the secret Sh$_x$ in a way that it becomes very difficult to predict the S$_K$  from any given key share(s). This is highly desirable in any CBSS scheme to ensure the higest level of security; otherwise, it will violate the confidentiality of the S$_K$ \cite{pei1996chinese}.  Finally, we experimented our developed strategy with existing approaches and demonstrated its benefit regarding the speed and efficiency of the S$_K$ generation and S$_K$ reconstruction by varying the number of participating secret shares Sh$_x$, and also by varying the number of participants involved, the threshold settings, etc. The main contributions of our research can be summarized as follows:

% \begin{itemize}
%     \item Improving the security of Sh$_x$ by suggesting a new CBSS method for generating secret shares Sh$_x$ that provides less similarity among Sh$_x$.
%     \item Increasing the vagueness of secret shares Sh$_x$ before distributing to participants to ensure and reduce the probability of guessing the S$_K$ from Sh$_x$.
   
% \end{itemize}
