[1]
@article{wani2023,
	title = {Deep learning based image steganography: {A} review},
	volume = {13},
	issn = {1942-4787, 1942-4795},
	shorttitle = {Deep learning based image steganography},
	url = {https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1481},
	doi = {10.1002/widm.1481},
	abstract = {Abstract
            A review of the deep learning based image steganography techniques is presented in this paper. For completeness, the recent traditional steganography techniques are also discussed briefly. The three key parameters (security, embedding capacity, and invisibility) for measuring the quality of an image steganographic technique are described. Various steganography techniques, with emphasis on the above three key parameters, are reviewed. The steganography techniques are classified here into three main categories: Traditional, Hybrid, and fully Deep Learning. The hybrid techniques are further divided into three sub‐categories: Cover Generation, Distortion Learning, and Adversarial Embedding. The fully Deep Learning techniques, based on the nature of the input, are further divided into three sub‐categories: GAN Embedding, Embedding Less, and Category Label. The main ideas of the important deep learning based steganography techniques are described. The strong and weak features of these techniques are outlined. The results reported by researchers on benchmark data sets CelebA, Bossbase, PASCAL‐VOC12, CIFAR‐100, ImageNet, and USC‐SIPI are used to evaluate the performance of various steganography techniques. Analysis of the results shows that there is scope for new suitable deep learning architectures that can improve the capacity and invisibility of image steganography.
            
              This article is categorized under:
              
                
                  Technologies {\textgreater} Computational Intelligence
                
                
                  Technologies {\textgreater} Machine Learning
                
                
                  Technologies {\textgreater} Artificial Intelligence},
	language = {en},
	number = {3},
	urldate = {2025-07-08},
	journal = {WIREs Data Mining and Knowledge Discovery},
	author = {Wani, Mohd Arif and Sultan, Bisma},
	month = may,
	year = {2023},
	pages = {e1481},
}

[2]
@article{plachta2022,
	title = {Detection of {Image} {Steganography} {Using} {Deep} {Learning} and {Ensemble} {Classifiers}},
	volume = {11},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	issn = {2079-9292},
	url = {https://www.mdpi.com/2079-9292/11/10/1565},
	doi = {10.3390/electronics11101565},
	abstract = {In this article, the problem of detecting JPEG images, which have been steganographically manipulated, is discussed. The performance of employing various shallow and deep learning algorithms in image steganography detection is analyzed. The data, images from the BOSS database, were used with information hidden using three popular steganographic algorithms: JPEG universal wavelet relative distortion (J-Uniward), nsF5, and uniform embedding revisited distortion (UERD) at two density levels. Various feature spaces were verified, with the discrete cosine transform residuals (DCTR) and the Gabor filter residuals (GFR) yielding best results. Almost perfect detection was achieved for the nsF5 algorithm at 0.4 bpnzac density (99.9\% accuracy), while the detection of J-Uniward at 0.1 bpnzac density turned out to be hardly possible (max. 56.3\% accuracy). The ensemble classifiers turned out to be an encouraging alternative to deep learning-based detection methods.},
	language = {en},
	number = {10},
	urldate = {2025-07-08},
	journal = {Electronics},
	author = {Płachta, Mikołaj and Krzemień, Marek and Szczypiorski, Krzysztof and Janicki, Artur},
	month = may,
	year = {2022},
	pages = {1565},
}

[3]
@article{chinniyan2022,
	title = {Image {Steganography} {Using} {Deep} {Neural} {Networks}},
	volume = {34},
	issn = {1079-8587},
	url = {https://www.techscience.com/iasc/v34n3/47943},
	doi = {10.32604/iasc.2022.027274},
	language = {en},
	number = {3},
	urldate = {2025-07-08},
	journal = {Intelligent Automation \& Soft Computing},
	author = {Chinniyan, Kavitha and Vani Samiyappan, Thamil and Gopu, Aishvarya and Ramasamy, Narmatha},
	year = {2022},
	pages = {1877--1891},
}

[4]
@article{chinniyan_image_2022,
	title = {Image {Steganography} {Using} {Deep} {Neural} {Networks}},
	volume = {34},
	issn = {1079-8587},
	url = {https://www.techscience.com/iasc/v34n3/47943},
	doi = {10.32604/iasc.2022.027274},
	language = {en},
	number = {3},
	urldate = {2025-07-08},
	journal = {Intelligent Automation \& Soft Computing},
	author = {Chinniyan, Kavitha and Vani Samiyappan, Thamil and Gopu, Aishvarya and Ramasamy, Narmatha},
	year = {2022},
	pages = {1877--1891},
}

[5]
@article{zou2019,
	title = {Research on image steganography analysis based on deep learning},
	volume = {60},
	issn = {10473203},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1047320319300914},
	doi = {10.1016/j.jvcir.2019.02.034},
	language = {en},
	urldate = {2025-07-08},
	journal = {Journal of Visual Communication and Image Representation},
	author = {Zou, Ying and Zhang, Ge and Liu, Leian},
	month = apr,
	year = {2019},
	pages = {266--275},
}



[6]
@article{Himthani2022,
	title = {Comparative performance assessment of deep learning based image steganography techniques},
	volume = {12},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-022-17362-1},
	doi = {10.1038/s41598-022-17362-1},
	abstract = {Abstract
            Increasing data infringement while transmission and storage have become an apprehension for the data owners. Even the digital images transmitted over the network or stored at servers are prone to unauthorized access. However, several image steganography techniques were proposed in the literature for hiding a secret image by embedding it into cover media. But the low embedding capacity and poor reconstruction quality of images are significant limitations of these techniques. To overcome these limitations, deep learning-based image steganography techniques are proposed in the literature. Convolutional neural network (CNN) based U-Net encoder has gained significant research attention in the literature. However, its performance efficacy as compared to other CNN based encoders like V-Net and U-Net++ is not implemented for image steganography. In this paper, V-Net and U-Net++ encoders are implemented for image steganography. A comparative performance assessment of U-Net, V-Net, and U-Net++ architectures are carried out. These architectures are employed to hide the secret image into the cover image. Further, a unique, robust, and standard decoder for all architectures is designed to extract the secret image from the cover image. Based on the experimental results, it is identified that U-Net architecture outperforms the other two architectures as it reports high embedding capacity and provides better quality stego and reconstructed secret images.},
	language = {en},
	number = {1},
	urldate = {2025-07-08},
	journal = {Scientific Reports},
	author = {Himthani, Varsha and Dhaka, Vijaypal Singh and Kaur, Manjit and Rani, Geeta and Oza, Meet and Lee, Heung-No},
	month = oct,
	year = {2022},
	pages = {16895},
}

[7]
@article{duan2020,
	title = {A {New} {High} {Capacity} {Image} {Steganography} {Method} {Combined} {With} {Image} {Elliptic} {Curve} {Cryptography} and {Deep} {Neural} {Network}},
	volume = {8},
	copyright = {https://creativecommons.org/licenses/by/4.0/legalcode},
	issn = {2169-3536},
	url = {https://ieeexplore.ieee.org/document/8981989/},
	doi = {10.1109/ACCESS.2020.2971528},
	urldate = {2025-07-08},
	journal = {IEEE Access},
	author = {Duan, Xintao and Guo, Daidou and Liu, Nao and Li, Baoxia and Gou, Mengxiao and Qin, Chuan},
	year = {2020},
	pages = {25777--25788},
}


[8]
@article{chakraborty2020,
	title = {A novel local binary pattern based blind feature image steganography},
	volume = {79},
	issn = {1380-7501, 1573-7721},
	url = {http://link.springer.com/10.1007/s11042-020-08828-3},
	doi = {10.1007/s11042-020-08828-3},
	language = {en},
	number = {27-28},
	urldate = {2025-07-08},
	journal = {Multimedia Tools and Applications},
	author = {Chakraborty, Soumendu and Jalal, Anand Singh},
	month = jul,
	year = {2020},
	pages = {19561--19574},
}





[9]
@article{SETIADI2023,
title = {Digital image steganography survey and investigation (goal, assessment, method, development, and dataset)},
journal = {Signal Processing},
volume = {206},
pages = {108908},
year = {2023},
issn = {0165-1684},
doi = {https://doi.org/10.1016/j.sigpro.2022.108908},
url = {https://www.sciencedirect.com/science/article/pii/S0165168422004479},
author = {De Rosal Ignatius Moses Setiadi and Supriadi Rustad and Pulung Nurtantio Andono and Guruh Fajar Shidik},
keywords = {Image Steganography Review, Image Steganalysis Review, Explicit Steganography Review, Critical Steganography Review, Steganography Investigation, Data Hiding Survey},
abstract = {Digital steganography has a long history, starting to be developed in the 90s until now. The main aspects of early steganography are security, imperceptibility, and payload. Security is an essential aspect of steganography, where its development competes with the steganalysis method. The development of steganographic methods is not only based on statistics but also involves adaptive methods, machine learning, artificial intelligence, generative adversarial networks (GAN), convolution neural networks (CNN), coverless techniques, etc. Several new methods add robustness as an important aspect. This makes the development of steganographic methods more varied and generally focuses the goal of the method on one or two aspects. Nowadays, steganography articles, especially surveys, rarely explicitly classify steganography based on its goals. So this survey contributes to classifying steganographic research based on goals and its assessments. This paper also reviews the use of assessment tools in depth because it is closely related to the goal of steganography. Challenges or issues, methods, developments, and popular datasets are also reviewed. Steganalysis literature was also added to improve the quality of the study and analysis. Finally, this survey provides discussion, investigation, critical analysis, and explicit summary so novice researchers can more easily understand image steganography development.}
}

[10]
@article{KUMAR2023,
title = {Encoder-Decoder Architecture for Image Steganography using Skip Connections.},
journal = {Procedia Computer Science},
volume = {218},
pages = {1122-1131},
year = {2023},
note = {International Conference on Machine Learning and Data Engineering},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2023.01.091},
url = {https://www.sciencedirect.com/science/article/pii/S1877050923000911},
author = {Akshay Kumar and Rajneesh Rani and Samayveer Singh},
keywords = {Convolutional Neural Network (CNN), Steganography, Dilated Inception-Net, Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index Matrix (SSIM)},
abstract = {Data security has become a basic need in today's world as it shifts more and more toward an all-online mode of operation. All sorts of data and information are now passed from one entity to another over a variety of transmission channels as well as stored on virtual online repositories, and a large chunk of that information is highly private. Over this stream of technological advancements, various data security techniques like encryption, data hiding, etc. have been developed to provide security while transmitting or storing data. This paper proposes an approach to hiding one image inside another image by using CNN in for feature extraction and processing. The proposed model works on the principles of the auto-encoder architecture and is made up of two modules one of which is responsible for the task of processing the secret image and extracting n features and the incorporating those features in the cover image ion such a manner that the cover image retains its original visual qualities and also the secret image gets hidden in it, that module is the Hiding network/Encoder. The other part of this network is the one responsible to then extract the hidden secret image and them create back to its original, this paper is the Reveal network/Decoder. The proposed model is implemented on ImageNet and Pascal-VOC datasets with images of different sizes. In order to judge the models ability to hide the image and to extract is back, PSNR and SSIM are used as performance evaluation Metrics.}
}

[12]
@article{sabah_abdulazeez2023,
	title = {Hiding {Information} in {Digital} {Images} {Using} {LSB} {Steganography} {Technique}},
	volume = {17},
	copyright = {https://creativecommons.org/licenses/by/4.0},
	issn = {1865-7923},
	url = {https://online-journals.org/index.php/i-jim/article/view/38737},
	doi = {10.3991/ijim.v17i07.38737},
	abstract = {The highest way to protect data from intruder and unauthorized persons has become a major issue. This matter led to the development of many techniques for data security, such as Steganography, Cryptography, and Watermarking to disguise data. This paper proposes an image steganography method using the Least Significant Bits (LSB) technique and XOR operator and a secret key, through which the secret key is transformed into a one-dimensional bit stream array, then these bits are XORed with the bits of the secret image. Multiple experiments have been performed to embed color and grayscale images inside cover media. In this work, the LSB technique is ideal in two ways: firstly, only the least significant one-bit (1bit) of each byte will store the embedded data, this method is named (1-LSB). Secondly, the four least significant bits of the right half-byte (4 bits) of each byte will store the embedded data, this method is named (4-LSB). Subjective and objective analyzes were performed for each LSB process. The subjective analysis is responsible for both HVS and histogram, whereas the objective analysis involved both PSNR and MSE metrics.},
	number = {07},
	urldate = {2025-07-08},
	journal = {International Journal of Interactive Mobile Technologies (iJIM)},
	author = {{Sabah Abdulazeez Jebur} and {Abbas Khalifa Nawar} and {Lubna Emad Kadhim} and {Mothefer Majeed Jahefer}},
	month = apr,
	year = {2023},
	pages = {167--178},
}

[12]
@article{sedighi2016,
	title = {Toss that {BOSSbase}, {Alice}!},
	volume = {28},
	issn = {2470-1173},
	url = {https://library.imaging.org/ei/articles/28/8/art00012},
	doi = {10.2352/ISSN.2470-1173.2016.8.MWSF-076},
	number = {8},
	urldate = {2025-07-08},
	journal = {Electronic Imaging},
	author = {Sedighi, Vahid and Fridrich, Jessica and Cogranne, Rémi},
	month = feb,
	year = {2016},
	pages = {1--9},
}

[13]
@misc{Kumar2024,
      title={StegaVision: Enhancing Steganography with Attention Mechanism}, 
      author={Abhinav Kumar and Pratham Singla and Aayan Yadav},
      year={2024},
      eprint={2411.05838},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2411.05838}, 
}

[14]

@article{Lin2024,
	title = {A novel method for linguistic steganography by {English} translation using attention mechanism and probability distribution theory},
	volume = {19},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295207},
	doi = {10.1371/journal.pone.0295207},
	abstract = {To enhance our ability to model long-range semantical dependencies, we introduce a novel approach for linguistic steganography through English translation. This method leverages attention mechanisms and probability distribution theory, known as NMT-stega (Neural Machine Translation-steganography). Specifically, to optimize translation accuracy and make full use of valuable source text information, we employ an attention-based NMT model as our translation technique. To address potential issues related to the degradation of text quality due to secret information embedding, we have devised a dynamic word pick policy based on probability variance. This policy adaptively constructs an alternative set and dynamically adjusts embedding capacity at each time step, guided by variance thresholds. Additionally, we have incorporated prior knowledge into the model by introducing a hyper-parameter that balances the contributions of the source and target text when predicting the embedded words. Extensive ablation experiments and comparative analyses, conducted on a large-scale Chinese-English corpus, validate the effectiveness of the proposed method across several critical aspects, including embedding rate, text quality, anti-steganography, and semantical distance. Notably, our numerical results demonstrate that the NMT-stega method outperforms alternative approaches in anti-steganography tasks, achieving the highest scores in two steganalysis models, NFZ-WDA (with score of 53) and LS-CNN (with score of 56.4). This underscores the superiority of NMT-stega in the anti-steganography attack task. Furthermore, even when generating longer sentences, with average lengths reaching 47 words, our method maintains strong semantical relationships, as evidenced by a semantic distance of 87.916. Moreover, we evaluate the proposed method using two metrics, Bilingual Evaluation Understudy and Perplexity, and achieve impressive scores of 42.103 and 23.592, respectively, highlighting its exceptional performance in the machine translation task.},
	language = {en},
	number = {1},
	urldate = {2025-06-28},
	journal = {PLOS ONE},
	author = {Lin, YiQing and Wang, ZhongHua},
	month = jan,
	year = {2024},
	keywords = {Probability distribution, Language, Semantics, Markov models, Syntax, Attention, Algorithms, Neurolinguistics},
	pages = {e0295207},
}



[15]
@article{Patwari2025,
title = {Blockchain-Integrated Image Steganography using xDoG Edge Detection for Authentication},
journal = {Cyber Security and Applications},
pages = {100091},
year = {2025},
issn = {2772-9184},
doi = {https://doi.org/10.1016/j.csa.2025.100091},
url = {https://www.sciencedirect.com/science/article/pii/S2772918425000086},
author = {Biswajit Patwari and Utpal Nandi and Srishti Dey and Sayani Dhar and Sudipta Kr Ghosal and Tapas Si},
keywords = {Steganography, etended Difference of Gaussians, Blockchain, Edge Detection, Embedding, Extraction, Payload},
abstract = {The fusion of image steganography with blockchain technology have brought about novel prospects for safe and decentralized authentication systems in recent years. In this paper, we propose a blockchain-based authentication system that makes use of eXtended Difference of Gaussians (xDoG) edge detection in steganography domain. Here, the xDoG edge detection captures both major and subtle edges for a more detailed and nuanced edge map. Other parameters viz. edge thickness, contrast and brightness etc. may be fine-tuned, making it more prominent. The principle behind embedding the secret image inside the cover image is to conceal more secret bits in the edge pixels and fewer bits in the non-edge pixels. The Stego-image obtained through the process is conveyed to the intended recipient using a public channel. The sender computes a hash value from the Stego-image and records the same in the blockchain network which makes illegal access and data tampering more stimulating. Blockchain's intrinsic immutability and decentralized structure are exploited to verify the authenticity and integrity of the Stego-image, allows the receiver to extract out the hidden content and, thereby firming resistance against attacks. Simulation result ensures that the proposed method attain an average payload of up to 3.43 bpp. The average PSNR in this trial consistently stays above 34 dB, and the SSIM never falls below 0.93, guaranteeing that the Stego-images' image quality is both very acceptable and better than that of the state-of-the-art techniques. To confirm the findings, the StegExpose and SR-Net are also used to assess the security of the Stego-image.}
}

[16]
@ARTICLE{Sharma2024,
  author={Sharma, Divya and Prabha, Chander and Gupta, Deepali and Juneja, Sapna and Abd-Elnaby, Mohammed and Alraddady, Fahad and Nauman, Ali},
  journal={IEEE Access}, 
  title={Securing X-Ray Images in No Interest Region (NIR) of the Normalized Cover Image by Edge Steganography}, 
  year={2024},
  volume={12},
  number={},
  pages={168672-168689},
  keywords={X-ray imaging;Electromagnetic interference;Steganography;Security;Real-time systems;Medical diagnostic imaging;Hospitals;Internet of Medical Things;Biomedical imaging;Security;steganography;Internet of Medical Things (IoMT);real-time applications (RTA);region of no interest (RONI);electronic medical image (EMI)},
  doi={10.1109/ACCESS.2024.3467167}}

[17]
@INPROCEEDINGS{Verma2024,
  author={Verma, Ashish Kumar and Shruti and Sarkar, Tiyas},
  booktitle={2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE)}, 
  title={Utilizing Imaging Steganographic Improvement using LSB \& Image Decoder}, 
  year={2024},
  volume={},
  number={},
  pages={144-150},
  keywords={Measurement;Steganography;Visualization;Image recognition;Image coding;Forensics;Imaging;Steganography;Cryptography;Image decoder;Steganography;Improve image;LSB(Least Significant Bit);Loss function},
  doi={10.1109/IC3SE62002.2024.10593623}}

[18]
@article{Panigrahi2025,
title = {An effective steganographic technique for hiding the image data using the LSB technique},
journal = {Cyber Security and Applications},
volume = {3},
pages = {100069},
year = {2025},
issn = {2772-9184},
doi = {https://doi.org/10.1016/j.csa.2024.100069},
url = {https://www.sciencedirect.com/science/article/pii/S2772918424000353},
author = {Rasmita Panigrahi and Neelamadhab Padhy},
keywords = {Steganographic techniques, Encode, Decode, Least significant bits, Stego image watermarking, QR Code, Embedding},
abstract = {Steganography is the art and science of writing secret messages so that neither the sender nor the intended recipient knows there is a hidden message. Data hiding is the art of hiding data for various reasons, such as keeping private data, secure, confidential data, etc. With increasing data exchange over a computer network, information security has become a significant issue. There are many methods used for data hiding, and steganography is a well-known technique. Steganography is the art of invisible contact and science. Steganography is the process through which the presence of a message can be kept secret. The objective of this paper is to hide data using the LSB (Least Significant Bit) technique into images that can be detected only by the specified user. We have developed a user-friendly GUI such that it can be used with the utmost ease. This paper is motivated to hide the message stated by the user in the dialog box given within the picture. The secret text is converted to the ciphertext to make it more stable. The sender selects the cover image, and it is used to generate the secured Stegno image, which is identical to the cover image. With the support of a private or public communication network, on the other hand, the stegno image can be saved and sent to the designated user, i.e., the recipient downloads the stegno image and can retrieve the secret text concealed in the stegno image using that same application. As for the watermarking, we have visible and invisible we have used the same LSB technique. In visible watermarking, text or image is embedded in the cover image, which can be noticed easily. As for invisible watermarking, some specific text is inserted into an image, and while retrieving it, it generates a QR code, which can be scanned to get the watermarked text. We used the three different types of cover images i.e. Gray, and RGB also estimated the performance metrics. SNR, MSE, and PSNR the three performance metrics are used, and found that PSNR achieved good results i.e.,71.4733. The RGB image with the hidden text is achieved up to 77.6697}
}

[19]
@article{Ray2021,
	title = {Image steganography using deep learning based edge detection},
	volume = {80},
	issn = {1573-7721},
	url = {https://doi.org/10.1007/s11042-021-11177-4},
	doi = {10.1007/s11042-021-11177-4},
	abstract = {This paper introduces a deep learning-based Steganography method for hiding secret information within the cover image. For this, we use a convolutional neural network (CNN) with Deep Supervision based edge detector, which can retain more edge pixels over conventional edge detection algorithms. Initially, the cover image is pre-processed by masking the last 5-bits of each pixel. The said edge detector model is then applied to obtain a gray-scale edge map. To get the prominent edge information, the gray-scale edge map is converted into a binary version using both global and adaptive binarization schemes. The purpose of using different binarization techniques is to prove the less sensitive nature of the edge detection method to the thresholding approaches. Our rule for embedding secret bits within the cover image is as follows: more bits into the edge pixels while fewer bits into the non-edge pixels. Experimental outcomes on various standard images confirm that compared to state-of-the-art methods, the proposed method achieves a higher payload.},
	language = {en},
	number = {24},
	urldate = {2025-06-28},
	journal = {Multimedia Tools and Applications},
	author = {Ray, Biswarup and Mukhopadhyay, Souradeep and Hossain, Sabbir and Ghosal, Sudipta Kr and Sarkar, Ram},
	month = oct,
	year = {2021},
	keywords = {
                Biometrics
            , 
                Computer Vision
            , 
                Cryptology
            , 
                Image Processing
            , 
                Object Recognition
            , 
                Optical cloaking
            , Steganography, Edge detection, LSB, CNN, Deep learning},
	pages = {33475--33503},
}


[20]
@article{Setiadi2024,
	title = {Graded fuzzy edge detection for imperceptibility optimization of image steganography},
	volume = {72},
	issn = {1368-2199, 1743-131X},
	url = {https://www.tandfonline.com/doi/full/10.1080/13682199.2023.2219880},
	doi = {10.1080/13682199.2023.2219880},
	language = {en},
	number = {6},
	urldate = {2025-06-28},
	journal = {The Imaging Science Journal},
	author = {Setiadi, De Rosal Ignatius Moses and Rustad, Supriadi and Andono, Pulung Nurtantio and Shidik, Guruh Fajar},
	month = aug,
	year = {2024},
	pages = {693--705},
}

[21]
@article{Liu2024,
	title = {Color {Image} {Steganography} {Method} {Based} on {RGB} {Model} and {Edge} {Detection}},
	issn = {1573-7721},
	url = {https://doi.org/10.1007/s11042-024-20008-1},
	doi = {10.1007/s11042-024-20008-1},
	abstract = {Due to advancements in camera manufacturing technology, photography has evolved from the black-and-white photos of the past to today's high-definition color images. This shift has also led to the gradual transition of information hiding techniques from grayscale images to color images. In recent years, scholars have continuously proposed information hiding techniques for color images. Based on the RGB model and edge detection, this study combines pixel value differencing and edge detection for color image steganography. This study improves on previous color image pixel value differencing—which initially splits the image into red, green, and blue layers—and combines with edge detection to embed more confidential information while maintaining a good camouflage quality due to the complex and less detectable textures on the edges. First, after dividing the carrier image into blocks of different sizes, the Laplacian of the Gaussian is used to determine whether it is an edge block; if it is an edge block, more confidential information is embedded. The procedure first uses the red and green layers as one group for steganography before using the green layer (with confidential information) and the blue layer as one group for steganography. The two sets of green layers are averaged and the red and blue layers with confidential information are adjusted synchronously. Finally, the adjusted red, green, and blue layers are superimposed to complete the steganographic procedure of this study. Compared with previously proposed methods, using the Lean image as an example, concealing capacity can be improved by approximately 23\%. Extensive testing with 450 images of the color image database revealed an increase in concealing capacity of 15\%, with no reduction in image quality of PSNR value is 33.51 dB. Furthermore, this study uses RS detection and pixel difference histogram for security analysis to verify the security of the proposed method. The experimental results demonstrated that the method proposed resists the detection of RS and pixel difference histograms. This study presents an irreversible information hiding method, where the experimental process involves simultaneous computation of all three RGB layers. Consequently, it is not possible to extract a single layer for information hiding. Therefore, this method can only be applied to information hiding in color images and cannot be used with grayscale images.},
	language = {en},
	urldate = {2025-06-28},
	journal = {Multimedia Tools and Applications},
	author = {Liu, Hsing-Han and Su, Yu-Ting},
	month = aug,
	year = {2024},
	keywords = {Colour vision, Cytological Techniques, Image Processing, Imaging Techniques, Optical cloaking, 3-D Image Reconstruction, RGB model, Edge detection, Laplacian of the Gaussian, Pixel value differencing, Steganography},
}



[22]
@inproceedings{Wang2025,
	title = {{AttnEdge}: an enhanced edge detection method based on self-attention mechanism},
	volume = {13486},
	shorttitle = {{AttnEdge}},
	url ={https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13486/134861V/AttnEdge--an-enhanced-edge-detection-method-based-on-self/10.1117/12.3055875.full},
	doi = {10.1117/12.3055875},
	abstract = {We introduce a novel approach to edge detection named AttnEdge, which combines pixel differential convolution with advanced post-processing techniques to enhance the detection and representation of image edges. This approach utilizes Pixel Difference Convolution (PDC) to directly learn edge features at the pixel level, enhancing the ability to handle complex boundaries and multi-scale features within images. Significantly, AttnEdge integrates a self-attention mechanism to capture global dependencies and refine feature representations, greatly improving the accuracy of edge detection. Attention mechanism plays a key role in edge detection model, which can significantly improve edge recognition accuracy and model robustness by analyzing global dependencies among pixels. It helps the model effectively distinguish between noise and real edges in complex backgrounds and optimizes detection results. The post-processing stage, featuring Gaussian blur and adaptive thresholding, further refines edge detection by reducing noise and dynamically adjusting to local brightness variations, ensuring robust and reliable edge detection across various scenarios. Furthermore, we have augmented the traditional convolution operations with innovations such as channel reduction in the self-attention mechanism, which reduces computational complexity while maintaining high performance. Experiments on the BSDS500 dataset demonstrate the superiority of AttnEdge over existing methods like PiDiNet, particularly in terms of structural similarity and noise resilience, making it a promising solution for advanced edge detection tasks.},
	urldate = {2025-06-28},
	booktitle = {Fourth {International} {Conference} on {Computer} {Vision}, {Application}, and {Algorithm} ({CVAA} 2024)},
	publisher = {SPIE},
	author = {Wang, Ming},
	month = jan,
	year = {2025},
	pages = {438--446},
}

[23]
@article{Abdulhammed2024,
	title = {A robust image steganography based on a novel technique by using improved {DNA} and modified chaotic approach},
	volume = {80},
	issn = {1573-0484},
	url = {https://doi.org/10.1007/s11227-023-05459-x},
	doi = {10.1007/s11227-023-05459-x},
	abstract = {After the tremendous development and fast growth in communication systems and electronic devices, information security and preservation have become one of the most important procedures to be considered when transferring data between two parties. Encryption and data hiding are two of the most important techniques to protect the information sent over networks and the Internet. In this paper, a new technique of encryption and concealment is used through the following steps: First, the secret information is encrypted by using developed DNA to get (S1), second, by scrambling the encrypted data, the (S1) is diffused by using a zigzag method to get (S2), third, re-obfuscation the (S2) by using developed zigzag method through using Tinkerbell technique to get (S3), fourth, perform XoR operation between the (S3) and the Tinkerbell technique to get (S4), and fifth, the (S4) is hidden in the random locations of the cover image based on the LSB technique and the Tinkerbell technology, where the Tinkerbell has incredibly excellent characteristics and used to generate random sequence numbers twice with different initial and parameters value, once for the encryption process for scrambling secret information and once for the hiding process for determining hidden locations. Several metrics were used to evaluate the system's efficiency: histogram, PSNR, MSE, mean absolute error, bit error rate, encryption quality, Unified Average Changing Intensity, hamming distance, entropy, and correlation coefficient. The experimental results display that the suggested method has a good performance and reliability, high security, and greater embedding capacity and ensures higher image quality; also, the proposed method presented preferable outcomes when compared to other approaches.},
	language = {en},
	number = {1},
	urldate = {2025-06-28},
	journal = {The Journal of Supercomputing},
	author = {Abdulhammed, Omar Younis},
	month = jan,
	year = {2024},
	keywords = {
                Chromosome conformation capture-based methods
            , 
                Cytological Techniques
            , 
                DNA computing and cryptography
            , 
                Genetic Techniques
            , 
                Optical cloaking
            , 
                Organizing materials with DNA
            , Steganography, Tinkerbell map, Zigzag, Secret message, DNA},
	pages = {226--248},
}

[24]
@article{Jasim2024,
	title = {An {Improved} {Image} {Steganography} {Security} and {Capacity} {Using} {Ant} {Colony} {Algorithm} {Optimization}},
	volume = {80},
	issn = {1546-2218, 1546-2226},
	url = {https://www.techscience.com/cmc/v80n3/57914},
	doi = {10.32604/cmc.2024.055195},
	abstract = {This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization (ACO) algorithm. Image steganography, a technique of embedding hidden information in digital photographs, should i... {\textbar} Find, read and cite all the research you need on Tech Science Press},
	language = {en},
	number = {3},
	urldate = {2025-06-28},
	journal = {Computers, Materials \& Continua},
	author = {Jasim, Zinah and Kurnaz, Sefer},
	year = {2024},
	pages = {4643--4662},
}

[25]
@article{Tahbaz2024,
	title = {Evolutionary-based image encryption using {Magic} {Square} {Chaotic} algorithm and {RNA} codons truth table},
	volume = {83},
	issn = {1573-7721},
	url = {https://doi.org/10.1007/s11042-023-15677-3},
	doi = {10.1007/s11042-023-15677-3},
	abstract = {Information security plays a key role in different areas such as Internet communications, medical imaging multimedia systems, and military communications. Image encryption is one of the techniques to ensure information security in communications. Therefore, this paper proposes a new hybrid model of a Magical Square Chaotic (MSC) algorithm and RNA codons for image encryption. The proposed model is composed of four steps. In the first phase, a secret key using the SHA-256 algorithm was considered for the initial value of the LS2 Map chaotic function. In the second phase, MSC algorithm was used for moving the image pixels. In the third phase, a combination of the RNA codons and chaotic function was employed for propagation of the image pixels. Finally, an optimization process was performed based on entropy criteria using genetic algorithm operators. The outputs of the simulation results confirm the superiority of the proposed model over other models. In addition, the analysis of the performance proves the high strength and security of the proposed model compared to the various types of attacks.},
	language = {en},
	number = {1},
	urldate = {2025-06-28},
	journal = {Multimedia Tools and Applications},
	author = {Tahbaz, Mahdi and Shirgahi, Hossein and Yamaghani, Mohammad Reza},
	month = jan,
	year = {2024},
	keywords = {
                Alternative Splicing
            , 
                Coding and Information Theory
            , 
                Cryptology
            , 
                DNA computing and cryptography
            , 
                Interspersed repetitive sequences
            , 
                Nonlinear Dynamics and Chaos Theory
            , Image encryption, LS2 map, RNA codons, Magic square algorithm, Hash function, Genetic algorithm},
	pages = {503--526},
}

[26]
@article{Xiao2025,
title = {A transformer-based adversarial network framework for steganography},
journal = {Expert Systems with Applications},
volume = {269},
pages = {126391},
year = {2025},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2025.126391},
url = {https://www.sciencedirect.com/science/article/pii/S0957417425000132},
author = {Chaoen Xiao and Sirui Peng and Lei Zhang and Jianxin Wang and Ding Ding and Jianyi Zhang},
keywords = {Steganography, Deep learning, Transformer, GAN},
abstract = {In the digital information era, image steganography effectively protects the security and confidentiality of information by hiding secret information in ordinary images. Image steganography methods based on deep learning have enhanced the robustness of secret information embedding, but there is still room for improvement in terms of imperceptibility and anti-interference capability. Therefore, we propose a Transformer-based adversarial network watermark steganography framework. First, during the information embedding phase, our method uses attention mechanisms to extract features from the cover image and the steganographic information. Secondly, in the extraction network, it maintains spatial relationships between tensors to enhance the detail extraction capability of the steganography process. Finally, a WGAN discriminator is introduced to cyclically adjust the steganographic image generator, improving the imperceptibility of the information embedding process. Experiments demonstrate that the proposed method reduces the learned perceptual image patch similarity (LPIPS) metric to 0.048 and achieves an information extraction accuracy of 98.99% on the ImageNet dataset. Even after various spatial disturbances such as cropping, rotation, and distortion, it can still maintain an extraction accuracy of over 60%. The Transformer-based adversarial network steganography framework not only significantly enhances the imperceptibility and robustness of steganographic images. but also introduces novel methodologies to the field of image steganography, providing valuable references and insights for future studies. Code is available at https://github.com/Supersirui/TransStego.}
}

[27]
@article{Song2024,
title = {A survey on Deep-Learning-based image steganography},
journal = {Expert Systems with Applications},
volume = {254},
pages = {124390},
year = {2024},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2024.124390},
url = {https://www.sciencedirect.com/science/article/pii/S0957417424012569},
author = {Bingbing Song and Ping Wei and Sixing Wu and Yu Lin and Wei Zhou},
keywords = {Image steganography, Information hiding, Deep-Learning-based steganography, Encryption, Security},
abstract = {With the development of Internet and multimedia development, digital image steganography is becoming more extensive in transmitting data with high capacity and security. Although there have been some relevant surveys, there is a lack of relevant surveys on Deep-Learning-based image steganography recently, especially for the developments and perspectives on generative steganography and reversible steganography. So this paper fills this gap and investigates recent developments in Deep-Learning-based image steganography from the viewpoints of strategy and network structures, which is summarized and categorized into ten strategies and five network models. In particular, this paper summarizes the relative merits and challenges of the leading techniques and discusses the research prospect of these fields for promoting the development of steganography.}
}

[28]
@Article{Ji2025,
AUTHOR = {Ji, Peng and Zhang, Youlue and Lv, Zhongyou},
TITLE = {Edge-Guided Dual-Stream U-Net for Secure Image Steganography},
JOURNAL = {Applied Sciences},
VOLUME = {15},
YEAR = {2025},
NUMBER = {8},
ARTICLE-NUMBER = {4413},
URL = {https://www.mdpi.com/2076-3417/15/8/4413},
ISSN = {2076-3417},
ABSTRACT = {Steganography, a technique for concealing information, often faces challenges such as low decoding accuracy and inadequate extraction of edge and global features. To overcome these limitations, we propose a dual-stream U-Net framework with integrated edge enhancement for image steganography. Our main contributions include the adoption of a dual-stream U-Net structure in the encoder, integrating an edge-enhancement stream with the InceptionDMK module for multi-scale edge detail extraction, and incorporating a multi-scale median attention (MSMA) module into the original input stream to enhance feature representation. This dual-stream design promotes deep feature fusion, thereby improving the edge details and embedding capacity of stego images. Moreover, an iterative optimization strategy is employed to progressively refine the selection of cover images and the embedding process, achieving enhanced stego quality and decoding performance. Experiments show that our method produces high-quality stego images across multiple public datasets, achieving near-100% decoding accuracy. It also surpasses existing methods in visual quality metrics like PSNR and SSIM. This framework offers a promising approach for enhancing steganographic security in real-world applications such as secure communication and data protection.},
DOI = {10.3390/app15084413}
}


[29]
@article{Sanjalawe2025,
	title = {A deep learning-driven multi-layered steganographic approach for enhanced data security},
	volume = {15},
	copyright = {2025 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-025-89189-5},
	doi = {10.1038/s41598-025-89189-5},
	abstract = {In the digital era, ensuring data integrity, authenticity, and confidentiality is critical amid growing interconnectivity and evolving security threats. This paper addresses key limitations of traditional steganographic methods, such as limited payload capacity, susceptibility to detection, and lack of robustness against attacks. A novel multi-layered steganographic framework is proposed, integrating Huffman coding, Least Significant Bit (LSB) embedding, and a deep learning-based encoder–decoder to enhance imperceptibility, robustness, and security. Huffman coding compresses data and obfuscates statistical patterns, enabling efficient embedding within cover images. At the same time, the deep learning encoder adds layer of protection by concealing an image within another. Extensive evaluations using benchmark datasets, including Tiny ImageNet, COCO, and CelebA, demonstrate the approach’s superior performance. Key contributions include achieving high visual fidelity with Structural Similarity Index Metrics (SSIM) consistently above 99\%, robust data recovery with text recovery accuracy reaching 100\% under standard conditions, and enhanced resistance to common attacks such as noise and compression. The proposed framework significantly improves robustness, security, and computational efficiency compared to traditional methods. By balancing imperceptibility and resilience, this paper advances secure communication and digital rights management, addressing modern challenges in data hiding through an innovative combination of compression, adaptive embedding, and deep learning techniques.},
	language = {en},
	number = {1},
	urldate = {2025-06-28},
	journal = {Scientific Reports},
	author = {Sanjalawe, Yousef and Al-E’mari, Salam and Fraihat, Salam and Abualhaj, Mosleh and Alzubi, Emran},
	month = feb,
	year = {2025},
	keywords = {Computer science, Information technology},
	pages = {4761},
}



[30]
@Article{Duan20202,
AUTHOR = {Duan, Xintao and Liu, Nao and Gou, Mengxiao and Wang, Wenxin and Qin, Chuan},
TITLE = {SteganoCNN: Image Steganography with Generalization Ability Based on Convolutional Neural Network},
JOURNAL = {Entropy},
VOLUME = {22},
YEAR = {2020},
NUMBER = {10},
ARTICLE-NUMBER = {1140},
URL = {https://www.mdpi.com/1099-4300/22/10/1140},
PubMedID = {33286909},
ISSN = {1099-4300},
ABSTRACT = {Image-to-image steganography is hiding one image in another image. However, hiding two secret images into one carrier image is a challenge today. The application of image steganography based on deep learning in real-life is relatively rare. In this paper, a new Steganography Convolution Neural Network (SteganoCNN) model is proposed, which solves the problem of two images embedded in a carrier image and can effectively reconstruct two secret images. SteganoCNN has two modules, an encoding network, and a decoding network, whereas the decoding network includes two extraction networks. First, the entire network is trained end-to-end, the encoding network automatically embeds the secret image into the carrier image, and the decoding network is used to reconstruct two different secret images. The experimental results show that the proposed steganography scheme has a maximum image payload capacity of 47.92 bits per pixel, and at the same time, it can effectively avoid the detection of steganalysis tools while keeping the stego-image undistorted. Meanwhile, StegaoCNN has good generalization capabilities and can realize the steganography of different data types, such as remote sensing images and aerial images.},
DOI = {10.3390/e22101140}
}

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[5]
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  title={Security enhancement of shares generation process for multimedia counting-based secret-sharing technique},
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  pages={16283--16310},
  year={2019},
  publisher={Springer}
}






@article{rustad2022optimization,
  title={Optimization of cross diagonal pixel value differencing and modulus function steganography using edge area block patterns},
  author={Rustad, Supriadi and De Rosal, Ignatius Moses Setiadi and Andono, Pulung Nurtantio and Syukur, Abdul and others},
  journal={Cybernetics and Information Technologies},
  volume={22},
  number={2},
  pages={145--159},
  year={2022}
}

@article{das2021multi,
  title={Multi-image steganography using deep neural networks},
  author={Das, Abhishek and Wahi, Japsimar Singh and Anand, Mansi and Rana, Yugant},
  journal={arXiv preprint arXiv:2101.00350},
  year={2021}
}






@article{wang2024gan,
  title={GAN-based adaptive cost learning for enhanced image steganography security},
  author={Wang, Dewang and Yang, Gaobo and Chen, Jiyou and Ding, Xiangling},
  journal={Expert Systems with Applications},
  volume={249},
  pages={123471},
  year={2024},
  publisher={Elsevier}
}



#









[6]
@article{al2022securing,
  title={Securing matrix counting-based secret-sharing involving crypto steganography},
  author={Al-Shaarani, Faiza and Gutub, Adnan},
  journal={Journal of King Saud University-Computer and Information Sciences},
  volume={34},
  number={9},
  pages={6909--6924},
  year={2022},
  publisher={Elsevier}
}

[7]
@article{gutub2019counting,
  title={Counting-based secret sharing technique for multimedia applications},
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  volume={78},
  pages={5591--5619},
  year={2019},
  publisher={Springer}
}



[9]
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  title={Image Based Steganography Using Truth Table Based and Determinate Array on RGB Indicator.},
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  number={3},
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}

[10]
@article{ahmadoh2015utilization,
  title={Utilization of two diacritics for Arabic text steganography to enhance performance},
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  volume={3},
  number={1},
  year={2015}
}

[11]
@article{alaseri2018merging,
  title={Merging secret sharing within Arabic text steganography for practical retrieval},
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  volume={4},
  pages={1--17},
  year={2018}
}

[12]
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  title={Enhancing PC data security via combining RSA cryptography and video based steganography},
  author={Al-Juaid, Nouf A and Gutub, Adnan A and Khan, Esam A},
  journal={Journal of Information Security and Cybercrimes Research},
  volume={1},
  number={1},
  pages={5--13},
  year={2018}
}

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  title={Integrity verification for digital Holy Quran verses using cryptographic hash function and compression},
  author={Almazrooie, Mishal and Samsudin, Azman and Gutub, Adnan Abdul-Aziz and Salleh, Muhammad Syukri and Omar, Mohd Adib and Hassan, Shahir Akram},
  journal={Journal of King Saud University-Computer and Information Sciences},
  volume={32},
  number={1},
  pages={24--34},
  year={2020},
  publisher={Elsevier}
}

[14]
@article{al2016merging,
  title={Merging two steganography techniques adjusted to improve arabic text data security},
  author={Al-Nofaie, Safia Meteb and Fattani, Manal and Gutub, Adnan},
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[3]
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  journal={Journal of King Saud University-Computer and Information Sciences},
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  pages={106--110},
  year={1978},
  publisher={IEEE}
}

[27]
@inproceedings{mignotte1983share,
  title={How to share a secret},
  author={Mignotte, Maurice},
  booktitle={Cryptography: Proceedings of the Workshop on Cryptography Burg Feuerstein, Germany, March 29--April 2, 1982 1},
  pages={371--375},
  year={1983},
  organization={Springer}
}

[28]
@article{asmuth1983modular,
  title={A modular approach to key safeguarding},
  author={Asmuth, Charles and Bloom, John},
  journal={IEEE transactions on information theory},
  volume={29},
  number={2},
  pages={208--210},
  year={1983},
  publisher={IEEE}
}




[31]
@inproceedings{brickell1989some,
  title={Some ideal secret sharing schemes},
  author={Brickell, Ernest F},
  booktitle={Workshop on the Theory and Application of of Cryptographic Techniques},
  pages={468--475},
  year={1989},
  organization={Springer}
}
[32]
@article{tassa2007hierarchical,
  title={Hierarchical threshold secret sharing},
  author={Tassa, Tamir},
  journal={Journal of cryptology},
  volume={20},
  pages={237--264},
  year={2007},
  publisher={Springer}
}
[33]
@inproceedings{beimel2005characterizing,
  title={Characterizing ideal weighted threshold secret sharing},
  author={Beimel, Amos and Tassa, Tamir and Weinreb, Enav},
  booktitle={Theory of Cryptography: Second Theory of Cryptography Conference, TCC 2005, Cambridge, MA, USA, February 10-12, 2005. Proceedings 2},
  pages={600--619},
  year={2005},
  organization={Springer}
}





[36]
@article{shambour2022progress,
  title={Progress of IoT research technologies and applications serving Hajj and Umrah},
  author={Shambour, Mohd Khaled and Gutub, Adnan},
  journal={Arabian Journal for Science and Engineering},
  pages={1--21},
  year={2022},
  publisher={Springer}
}



[38]
@article{gutub2020hiding,
  title={Hiding shares by multimedia image steganography for optimized counting-based secret sharing},
  author={Gutub, Adnan and Al-Ghamdi, Maimoona},
  journal={Multimedia Tools and Applications},
  volume={79},
  number={11},
  pages={7951--7985},
  year={2020},
  publisher={Springer}
}

[39]
@article{al2022increasing,
  title={Increasing participants using counting-based secret sharing via involving matrices and practical steganography},
  author={Al-Shaarani, Faiza and Gutub, Adnan},
  journal={Arabian Journal for Science and Engineering},
  volume={47},
  number={2},
  pages={2455--2477},
  year={2022},
  publisher={Springer}
}

[40]
@article{alkhodaidi2021refining,
  title={Refining image steganography distribution for higher security multimedia counting-based secret-sharing},
  author={AlKhodaidi, Taghreed and Gutub, Adnan},
  journal={Multimedia Tools and Applications},
  volume={80},
  pages={1143--1173},
  year={2021},
  publisher={Springer}
}

[41]
@article{gutub2023dynamic,
  title={Dynamic smart random preference for higher medical image confidentiality},
  author={Gutub, Adnan},
  journal={Journal of Engineering Research},
  volume={11},
  number={3},
  year={2023}
}



[43]
@article{qin2021verifiable,
  title={A Verifiable Steganography-Based Secret Image Sharing Scheme in 5G Networks},
  author={Qin, Shiyue and Tan, Zhenhua and Zhou, Fucai and Xu, Jian and Zhang, Zongye},
  journal={Security and Communication Networks},
  volume={2021},
  number={1},
  pages={6629726},
  year={2021},
  publisher={Wiley Online Library}
}

[44]
@article{gutub2024novel,
  title={Novel key-integration to safeguard counting-based secret-sharing from possibilities of cyberattack breaches},
  author={Gutub, Adnan and Al-Shaarani, Faiza and Alharthi, Khoulood},
  journal={Multimedia Tools and Applications},
  pages={1--27},
  year={2024},
  publisher={Springer}
}

[45]
@article{patil2022improved,
  title={Improved resilience of secret sharing scheme with augmented multifarious features},
  author={Patil, Sonali and Bhandari, Sheetal and Thepade, Sudeep and Raut, Roshani and Athawale, Shashikant V},
  journal={International Journal of Information Technology},
  volume={14},
  number={5},
  pages={2633--2644},
  year={2022},
  publisher={Springer}
}

%% Journal article







[3]
@article{al2022securing,
  title={Securing matrix counting-based secret-sharing involving crypto steganography},
  author={Al-Shaarani, Faiza and Gutub, Adnan},
  journal={Journal of King Saud University-Computer and Information Sciences},
  volume={34},
  number={9},
  pages={6909--6924},
  year={2022},
  publisher={Elsevier}
}



[16]
@book{pei1996chinese,
  title={Chinese remainder theorem: applications in computing, coding, cryptography},
  author={Pei, Dingyi and Salomaa, Arto and Ding, Cunsheng},
  year={1996},
  publisher={World Scientific}
}









[10]
@article{alaseri2018merging,
  title={Merging secret sharing within Arabic text steganography for practical retrieval},
  author={Alaseri, Khaled and Gutub, Adnan},
  journal={International Journal of Research \& Development Organisation (IJRDO)-Journal of Computer Science and Engineering},
  volume={4},
  pages={1--17},
  year={2018}
}




[14]
@phdthesis{binu2016secret,
  title={Secret Sharing Schemes with Extended Capabilities and Applications},
  author={Binu, VP and Sreekumar, A},
  year={2016},
  school={Cochin University of Science and Technology}
}

[15]
@inproceedings{castiglione2014hierarchical,
  title={Hierarchical and shared key assignment},
  author={Castiglione, Arcangelo and De Santis, Alfredo and Masucci, Barbara},
  booktitle={2014 17th International Conference on Network-Based Information Systems},
  pages={263--270},
  year={2014},
  organization={IEEE}
}




[18]
@article{smid2010statistical,
  title={A statistical test suite for random and pseudorandom number generators for cryptographic applications},
  author={Smid, Elaine Barker and Leigh, Stefan and Levenson, Mark and Vangel, Mark and DavidBanks, A and JamesDray, SanVo},
  journal={Her research interest includes Computer security, secure operating systems, Access control, Distributed systems, Intrusion detection systems},
  year={2010}
}


[19]
@article{iftene2007general,
  title={General secret sharing based on the chinese remainder theorem with applications in e-voting},
  author={Iftene, Sorin},
  journal={Electronic Notes in Theoretical Computer Science},
  volume={186},
  pages={67--84},
  year={2007},
  publisher={Elsevier}
}



[25]
@inproceedings{wu2001rsa,
  title={RSA cryptosystem design based on the Chinese remainder theorem},
  author={Wu, Chung-Hsien and Hong, Jin-Hua and Wu, Cheng-Wen},
  booktitle={Proceedings of the 2001 Asia and South Pacific Design automation conference},
  pages={391--395},
  year={2001}
}

[26]
@article{pohlig1978improved,
  title={An improved algorithm for computing logarithms over GF (p) and its cryptographic significance (corresp.)},
  author={Pohlig, Stephen and Hellman, Martin},
  journal={IEEE Transactions on information Theory},
  volume={24},
  number={1},
  pages={106--110},
  year={1978},
  publisher={IEEE}
}

[27]
@inproceedings{mignotte1983share,
  title={How to share a secret},
  author={Mignotte, Maurice},
  booktitle={Cryptography: Proceedings of the Workshop on Cryptography Burg Feuerstein, Germany, March 29--April 2, 1982 1},
  pages={371--375},
  year={1983},
  organization={Springer}
}

[28]
@article{asmuth1983modular,
  title={A modular approach to key safeguarding},
  author={Asmuth, Charles and Bloom, John},
  journal={IEEE transactions on information theory},
  volume={29},
  number={2},
  pages={208--210},
  year={1983},
  publisher={IEEE}
}




[31]
@inproceedings{brickell1989some,
  title={Some ideal secret sharing schemes},
  author={Brickell, Ernest F},
  booktitle={Workshop on the Theory and Application of of Cryptographic Techniques},
  pages={468--475},
  year={1989},
  organization={Springer}
}






[36]
@article{shambour2022progress,
  title={Progress of IoT research technologies and applications serving Hajj and Umrah},
  author={Shambour, Mohd Khaled and Gutub, Adnan},
  journal={Arabian Journal for Science and Engineering},
  pages={1--21},
  year={2022},
  publisher={Springer}
}



[38]
@article{gutub2020hiding,
  title={Hiding shares by multimedia image steganography for optimized counting-based secret sharing},
  author={Gutub, Adnan and Al-Ghamdi, Maimoona},
  journal={Multimedia Tools and Applications},
  volume={79},
  number={11},
  pages={7951--7985},
  year={2020},
  publisher={Springer}
}

[39]
@article{al2022increasing,
  title={Increasing participants using counting-based secret sharing via involving matrices and practical steganography},
  author={Al-Shaarani, Faiza and Gutub, Adnan},
  journal={Arabian Journal for Science and Engineering},
  volume={47},
  number={2},
  pages={2455--2477},
  year={2022},
  publisher={Springer}
}

[40]
@article{alkhodaidi2021refining,
  title={Refining image steganography distribution for higher security multimedia counting-based secret-sharing},
  author={AlKhodaidi, Taghreed and Gutub, Adnan},
  journal={Multimedia Tools and Applications},
  volume={80},
  pages={1143--1173},
  year={2021},
  publisher={Springer}
}

[41]
@article{gutub2023dynamic,
  title={Dynamic smart random preference for higher medical image confidentiality},
  author={Gutub, Adnan},
  journal={Journal of Engineering Research},
  volume={11},
  number={3},
  year={2023}
}



[43]
@article{qin2021verifiable,
  title={A Verifiable Steganography-Based Secret Image Sharing Scheme in 5G Networks},
  author={Qin, Shiyue and Tan, Zhenhua and Zhou, Fucai and Xu, Jian and Zhang, Zongye},
  journal={Security and Communication Networks},
  volume={2021},
  number={1},
  pages={6629726},
  year={2021},
  publisher={Wiley Online Library}
}

[44]
@article{gutub2024novel,
  title={Novel key-integration to safeguard counting-based secret-sharing from possibilities of cyberattack breaches},
  author={Gutub, Adnan and Al-Shaarani, Faiza and Alharthi, Khoulood},
  journal={Multimedia Tools and Applications},
  pages={1--27},
  year={2024},
  publisher={Springer}
}

[45]
@article{patil2022improved,
  title={Improved resilience of secret sharing scheme with augmented multifarious features},
  author={Patil, Sonali and Bhandari, Sheetal and Thepade, Sudeep and Raut, Roshani and Athawale, Shashikant V},
  journal={International Journal of Information Technology},
  volume={14},
  number={5},
  pages={2633--2644},
  year={2022},
  publisher={Springer}
}



[3]
@article{al2022securing,
  title={Securing matrix counting-based secret-sharing involving crypto steganography},
  author={Al-Shaarani, Faiza and Gutub, Adnan},
  journal={Journal of King Saud University-Computer and Information Sciences},
  volume={34},
  number={9},
  pages={6909--6924},
  year={2022},
  publisher={Elsevier}
}



[16]
@book{pei1996chinese,
  title={Chinese remainder theorem: applications in computing, coding, cryptography},
  author={Pei, Dingyi and Salomaa, Arto and Ding, Cunsheng},
  year={1996},
  publisher={World Scientific}
}









[10]
@article{alaseri2018merging,
  title={Merging secret sharing within Arabic text steganography for practical retrieval},
  author={Alaseri, Khaled and Gutub, Adnan},
  journal={International Journal of Research \& Development Organisation (IJRDO)-Journal of Computer Science and Engineering},
  volume={4},
  pages={1--17},
  year={2018}
}




[14]
@phdthesis{binu2016secret,
  title={Secret Sharing Schemes with Extended Capabilities and Applications},
  author={Binu, VP and Sreekumar, A},
  year={2016},
  school={Cochin University of Science and Technology}
}

[15]
@inproceedings{castiglione2014hierarchical,
  title={Hierarchical and shared key assignment},
  author={Castiglione, Arcangelo and De Santis, Alfredo and Masucci, Barbara},
  booktitle={2014 17th International Conference on Network-Based Information Systems},
  pages={263--270},
  year={2014},
  organization={IEEE}
}




[18]
@article{smid2010statistical,
  title={A statistical test suite for random and pseudorandom number generators for cryptographic applications},
  author={Smid, Elaine Barker and Leigh, Stefan and Levenson, Mark and Vangel, Mark and DavidBanks, A and JamesDray, SanVo},
  journal={Her research interest includes Computer security, secure operating systems, Access control, Distributed systems, Intrusion detection systems},
  year={2010}
}


[19]
@article{iftene2007general,
  title={General secret sharing based on the chinese remainder theorem with applications in e-voting},
  author={Iftene, Sorin},
  journal={Electronic Notes in Theoretical Computer Science},
  volume={186},
  pages={67--84},
  year={2007},
  publisher={Elsevier}
}




[25]
@inproceedings{wu2001rsa,
  title={RSA cryptosystem design based on the Chinese remainder theorem},
  author={Wu, Chung-Hsien and Hong, Jin-Hua and Wu, Cheng-Wen},
  booktitle={Proceedings of the 2001 Asia and South Pacific Design automation conference},
  pages={391--395},
  year={2001}
}

[26]
@article{pohlig1978improved,
  title={An improved algorithm for computing logarithms over GF (p) and its cryptographic significance (corresp.)},
  author={Pohlig, Stephen and Hellman, Martin},
  journal={IEEE Transactions on information Theory},
  volume={24},
  number={1},
  pages={106--110},
  year={1978},
  publisher={IEEE}
}

[27]
@inproceedings{mignotte1983share,
  title={How to share a secret},
  author={Mignotte, Maurice},
  booktitle={Cryptography: Proceedings of the Workshop on Cryptography Burg Feuerstein, Germany, March 29--April 2, 1982 1},
  pages={371--375},
  year={1983},
  organization={Springer}
}

[28]
@article{asmuth1983modular,
  title={A modular approach to key safeguarding},
  author={Asmuth, Charles and Bloom, John},
  journal={IEEE transactions on information theory},
  volume={29},
  number={2},
  pages={208--210},
  year={1983},
  publisher={IEEE}
}




[31]
@inproceedings{brickell1989some,
  title={Some ideal secret sharing schemes},
  author={Brickell, Ernest F},
  booktitle={Workshop on the Theory and Application of of Cryptographic Techniques},
  pages={468--475},
  year={1989},
  organization={Springer}
}






[36]
@article{shambour2022progress,
  title={Progress of IoT research technologies and applications serving Hajj and Umrah},
  author={Shambour, Mohd Khaled and Gutub, Adnan},
  journal={Arabian Journal for Science and Engineering},
  pages={1--21},
  year={2022},
  publisher={Springer}
}



[38]
@article{gutub2020hiding,
  title={Hiding shares by multimedia image steganography for optimized counting-based secret sharing},
  author={Gutub, Adnan and Al-Ghamdi, Maimoona},
  journal={Multimedia Tools and Applications},
  volume={79},
  number={11},
  pages={7951--7985},
  year={2020},
  publisher={Springer}
}

[39]
@article{al2022increasing,
  title={Increasing participants using counting-based secret sharing via involving matrices and practical steganography},
  author={Al-Shaarani, Faiza and Gutub, Adnan},
  journal={Arabian Journal for Science and Engineering},
  volume={47},
  number={2},
  pages={2455--2477},
  year={2022},
  publisher={Springer}
}

[40]
@article{alkhodaidi2021refining,
  title={Refining image steganography distribution for higher security multimedia counting-based secret-sharing},
  author={AlKhodaidi, Taghreed and Gutub, Adnan},
  journal={Multimedia Tools and Applications},
  volume={80},
  pages={1143--1173},
  year={2021},
  publisher={Springer}
}

[41]
@article{gutub2023dynamic,
  title={Dynamic smart random preference for higher medical image confidentiality},
  author={Gutub, Adnan},
  journal={Journal of Engineering Research},
  volume={11},
  number={3},
  year={2023}
}



[43]
@article{qin2021verifiable,
  title={A Verifiable Steganography-Based Secret Image Sharing Scheme in 5G Networks},
  author={Qin, Shiyue and Tan, Zhenhua and Zhou, Fucai and Xu, Jian and Zhang, Zongye},
  journal={Security and Communication Networks},
  volume={2021},
  number={1},
  pages={6629726},
  year={2021},
  publisher={Wiley Online Library}
}

[44]
@article{gutub2024novel,
  title={Novel key-integration to safeguard counting-based secret-sharing from possibilities of cyberattack breaches},
  author={Gutub, Adnan and Al-Shaarani, Faiza and Alharthi, Khoulood},
  journal={Multimedia Tools and Applications},
  pages={1--27},
  year={2024},
  publisher={Springer}
}

[45]
@article{patil2022improved,
  title={Improved resilience of secret sharing scheme with augmented multifarious features},
  author={Patil, Sonali and Bhandari, Sheetal and Thepade, Sudeep and Raut, Roshani and Athawale, Shashikant V},
  journal={International Journal of Information Technology},
  volume={14},
  number={5},
  pages={2633--2644},
  year={2022},
  publisher={Springer}
}

@article{gutub2019counting,
  title={Counting-based secret sharing technique for multimedia applications},
  author={Gutub, Adnan and Al-Juaid, Nouf and Khan, Esam},
  journal={Multimedia Tools and Applications},
  volume={78},
  pages={5591--5619},
  year={2019},
  publisher={Springer}
}


@article{ray2021image,
  title={Image steganography using deep learning based edge detection},
  author={Ray, Biswarup and Mukhopadhyay, Souradeep and Hossain, Sabbir and Ghosal, Sudipta Kr and Sarkar, Ram},
  journal={Multimedia Tools and Applications},
  volume={80},
  number={24},
  pages={33475--33503},
  year={2021},
  publisher={Springer}
}

@article{ghosal2021image,
  title={Image steganography based on Kirsch edge detection},
  author={Ghosal, Sudipta Kumar and Chatterjee, Agneet and Sarkar, Ram},
  journal={Multimedia Systems},
  volume={27},
  number={1},
  pages={73--87},
  year={2021},
  publisher={Springer}
}

@INPROCEEDINGS{9988162,
  author={Niu, Lianqiang and Zhang, Jing},
  booktitle={2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA)}, 
  title={An image steganography method based on texture perception}, 
  year={2022},
  volume={},
  number={},
  pages={625-628},
  keywords={Image quality;Steganography;Correlation;Convolution;Resists;Computer applications;Data science;Image steganography;Texture analysis;gray-level co-occurrence matrix;Channel attention},
  doi={10.1109/ICDSCA56264.2022.9988162}}

@article{harba2018advanced,
  title={Advanced password authentication protection by hybrid cryptography \& audio steganography},
  author={Harba, Eman Salim Ibrahim},
  journal={Iraqi Journal of Science},
  pages={600--606},
  year={2018}
}

@inproceedings{seno2019layer,
  title={Layer 2 Packet Authentication for IoT Sensor Networks},
  author={Seno, Shoichiro},
  booktitle={2019 Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU)},
  pages={1--6},
  year={2019},
  organization={IEEE}
}


@inproceedings{hazra2015file,
  title={File encryption using fisher-yates shuffle},
  author={Hazra, Tapan Kumar and Ghosh, Rumela and Kumar, Sayam and Dutta, Sagnik and Chakraborty, Ajoy Kumar},
  booktitle={2015 International Conference and Workshop on Computing and Communication (IEMCON)},
  pages={1--7},
  year={2015},
  organization={IEEE}
}

@article{wani2023deep,
  title={Deep learning based image steganography: A review},
  author={Wani, Mohd Arif and Sultan, Bisma},
  journal={Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
  volume={13},
  number={3},
  pages={e1481},
  year={2023},
  publisher={Wiley Online Library}
}


@article{al2025robust,
  title={Robust Image Steganography Approach based on Edge Detection Combined with CNN Algorithm},
  author={Al-Rawashdeh, Rana and Rahman, Md Mahfuzur and Niazi, Mahmood},
  journal={IEEE Access},
  year={2025},
  publisher={IEEE}
}

@inproceedings{khandelwal2023reversible,
  title={Reversible Image Steganography Using Deep Learning Method: A Review},
  author={Khandelwal, Jyoti and Sharma, Vijay Kumar},
  booktitle={International Conference on Human-Centric Smart Computing},
  pages={625--635},
  year={2023},
  organization={Springer}
}

@inproceedings{li2020generative,
  title={A generative steganography method based on WGAN-GP},
  author={Li, Jun and Niu, Ke and Liao, Liwei and Wang, Lijie and Liu, Jia and Lei, Yu and Zhang, Minqing},
  booktitle={International Conference on Artificial Intelligence and Security},
  pages={386--397},
  year={2020},
  organization={Springer}
}

@article{liu2025color,
  title={Color image steganography method based on RGB model and edge detection},
  author={Liu, Hsing-Han and Su, Yu-Ting},
  journal={Multimedia Tools and Applications},
  volume={84},
  number={21},
  pages={23833--23860},
  year={2025},
  publisher={Springer}
}

@article{habiban2024hybrid,
  title={Hybrid edge detection methods in image steganography for high embedding capacity},
  author={Habiban, Marwah and Hamade, Fatima R and Mohsin, Nadia A},
  journal={Cybernetics and Information Technologies},
  volume={24},
  number={1},
  pages={157--170},
  year={2024},
  publisher={Sciendo}
}

@article{singh2025enhanced,
  title={Enhanced edge-based steganography using image segmentation and random LSB substitution for secure data hiding},
  author={Singh, Juhi and Singh, Arun Kumar and Chauhan, Shishir Singh},
  journal={International Journal on Smart Sensing and Intelligent Systems},
  volume={18},
  number={1},
  year={2025},
  publisher={De Gruyter Brill Sp. z oo, Paradigm Publishing Services}
}

@article{patwari2025blockchain,
  title={Blockchain-Integrated Image Steganography using xDoG Edge Detection for Authentication},
  author={Patwari, Biswajit and Nandi, Utpal and Dey, Srishti and Dhar, Sayani and Ghosal, Sudipta Kr and Si, Tapas},
  journal={Cyber Security and Applications},
  pages={100091},
  year={2025},
  publisher={Elsevier}
}

@phdthesis{chavan2024cloud,
  title={Cloud Data Security Improvement Using Cryptographic Steganography by Truly Random and Cryptographically Secure Random Number},
  author={Chavan, Rajendra Anil},
  year={2024},
  school={Dublin, National College of Ireland}
}

@inproceedings{niu2022image,
  title={An image steganography method based on texture perception},
  author={Niu, Lianqiang and Zhang, Jing},
  booktitle={2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA)},
  pages={625--628},
  year={2022},
  organization={IEEE}
}

@article{kumar2023encoder,
  title={Encoder-Decoder Architecture for Image Steganography using Skip Connections.},
  author={Kumar, Akshay and Rani, Rajneesh and Singh, Samayveer},
  journal={Procedia Computer Science},
  volume={218},
  pages={1122--1131},
  year={2023},
  publisher={Elsevier}
}

@article{vineethaimage,
  title={Image Steganography with CNN Based Encoder-Decoder},
  author={Vineetha, K and Pravallika, Annem and Manoj, Devarakonda and Ashok, Jyothi and Naveen, Rayudu}
}

@article{malik2025hybrid,
  title={A hybrid steganography framework using DCT and GAN for secure data communication in the big data era},
  author={Malik, Kaleem Razzaq and Sajid, Muhammad and Almogren, Ahmad and Malik, Tauqeer Safdar and Khan, Ali Haider and Altameem, Ayman and Rehman, Ateeq Ur and Hussen, Seada},
  journal={Scientific Reports},
  volume={15},
  number={1},
  pages={19630},
  year={2025},
  publisher={Nature Publishing Group UK London}
}

@article{li2024gan,
  title={GAN-based image steganography by exploiting transform domain knowledge with deep networks},
  author={Li, Xiao and Chen, Liquan and Lai, Jianchang and Fu, Zhangjie and Liu, Suhui},
  journal={Multimedia Systems},
  volume={30},
  number={4},
  pages={224},
  year={2024},
  publisher={Springer}
}

@inproceedings{ren2024secure,
  title={Secure Steganography Based on Wasserstein Generative Adversarial Networks with Gradient Penalty},
  author={Ren, Fang and Wang, Yiyuan and Zhu, Tingge and Gao, Bo},
  booktitle={2024 6th International Conference on Natural Language Processing (ICNLP)},
  pages={310--314},
  year={2024},
  organization={IEEE}
}



@incollection{krislock2012euclidean,
  title={Euclidean distance matrices and applications},
  author={Krislock, Nathan and Wolkowicz, Henry},
  booktitle={Handbook on semidefinite, conic and polynomial optimization},
  pages={879--914},
  year={2012},
  publisher={Springer}
}

@article{rustad2022optimization,
  title={Optimization of cross diagonal pixel value differencing and modulus function steganography using edge area block patterns},
  author={Rustad, Supriadi and De Rosal, Ignatius Moses Setiadi and Andono, Pulung Nurtantio and Syukur, Abdul and others},
  journal={Cybernetics and Information Technologies},
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