Zhenxiang He^*, Rulin Wu, Xinyuan Wang

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1631-1651, 2025, DOI:10.32604/cmc.2025.067252 - 29 August 2025

Abstract With the continuous advancement of steganographic techniques, the task of image steganalysis has become increasingly challenging, posing significant obstacles to the fields of information security and digital forensics. Although existing deep learning methods have achieved certain progress in steganography detection, they still encounter several difficulties in real-world applications. Specifically, current methods often struggle to accurately focus on steganography sensitive regions, leading to limited detection accuracy. Moreover, feature information is frequently lost during transmission, which further reduces the model’s generalization ability. These issues not only compromise the reliability of steganography detection but also hinder its applicability… More >

Xiang Zhang^1,2,*, Shenyan Han^1,2, Wenbin Huang^1,2, Daoyong Fu^1,2

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1149-1171, 2025, DOI:10.32604/cmc.2025.060876 - 26 March 2025

Abstract Generative image steganography is a technique that directly generates stego images from secret information. Unlike traditional methods, it theoretically resists steganalysis because there is no cover image. Currently, the existing generative image steganography methods generally have good steganography performance, but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information extraction. Therefore, this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping rule. Firstly, the reference image is disentangled by a content and an attribute encoder to obtain content features… More >

Kening Wang^1,#, Feipeng Gao^2,#, Jie Yang^1,2,*, Hao Zhang¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4797-4820, 2025, DOI:10.32604/cmc.2025.060042 - 06 March 2025

Abstract With the rapid advancement of Voice over Internet Protocol (VoIP) technology, speech steganography techniques such as Quantization Index Modulation (QIM) and Pitch Modulation Steganography (PMS) have emerged as significant challenges to information security. These techniques embed hidden information into speech streams, making detection increasingly difficult, particularly under conditions of low embedding rates and short speech durations. Existing steganalysis methods often struggle to balance detection accuracy and computational efficiency due to their limited ability to effectively capture both temporal and spatial features of speech signals. To address these challenges, this paper proposes an Efficient Sliding Window… More >

Qianqian Zhang^1,2,3, Yi Zhang^1,2, Yuanyuan Ma³, Yanmei Liu^1,2, Xiangyang Luo^1,2,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 105-131, 2024, DOI:10.32604/cmc.2024.055735 - 15 October 2024

Abstract The rapid development of the internet and digital media has provided convenience while also posing a potential risk of steganography abuse. Identifying steganographer is essential in tracing secret information origins and preventing illicit covert communication online. Accurately discerning a steganographer from many normal users is challenging due to various factors, such as the complexity in obtaining the steganography algorithm, extracting highly separability features, and modeling the cover data. After extensive exploration, several methods have been proposed for steganographer identification. This paper presents a survey of existing studies. Firstly, we provide a concise introduction to the More >

Zinah Khalid Jasim Jasim^*, Sefer Kurnaz^*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4643-4662, 2024, DOI:10.32604/cmc.2024.055195 - 12 September 2024

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 ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect. The contemporary methods of steganography are at best a compromise between these two. In this paper, we present our approach, entitled Ant Colony Optimization (ACO)-Least Significant Bit (LSB), which attempts to optimize the capacity in steganographic embedding. The approach makes use… More >

Jun Li^1,2, Minqing Zhang^1,2,*, Ke Niu¹, Yingnan Zhang¹, Xiaoyuan Yang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2085-2103, 2024, DOI:10.32604/cmc.2024.048095 - 15 May 2024

Abstract Among steganalysis techniques, detection against MV (motion vector) domain-based video steganography in the HEVC (High Efficiency Video Coding) standard remains a challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis method that can perfectly detect MV-based steganography in HEVC. Firstly, we define the local optimality of MVP (Motion Vector Prediction) based on the technology of AMVP (Advanced Motion Vector Prediction). Secondly, we analyze that in HEVC video, message embedding either using MVP index or MVD (Motion Vector Difference) may destroy the above optimality of MVP. And then, we define More >

Arslan Akram^1,2, Imran Khan¹, Javed Rashid^2,3, Mubbashar Saddique^4,*, Muhammad Idrees⁴, Yazeed Yasin Ghadi⁵, Abdulmohsen Algarni⁶

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1311-1328, 2024, DOI:10.32604/cmc.2023.040512 - 30 January 2024

Abstract Algorithms for steganography are methods of hiding data transfers in media files. Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information, and these methods have made it feasible to handle a wide range of problems associated with image analysis. Images with little information or low payload are used by information embedding methods, but the goal of all contemporary research is to employ high-payload images for classification. To address the need for both low- and high-payload images, this work provides a machine-learning approach to steganography image classification… More >

Chengyu Mo^1,2, Fenlin Liu^1,2, Ma Zhu^1,2,*, Gengcong Yan³, Baojun Qi^1,2, Chunfang Yang^1,2

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2921-2936, 2023, DOI:10.32604/cmc.2023.039540 - 08 October 2023

Abstract The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this effect by discarding some features related to image contents. Inevitably, this should lose much helpful information and cause low detection accuracy. This paper proposes an image steganalysis method based on deep content features clustering to solve this problem. Firstly, the wavelet transform is used to remove the high-frequency noise of the image, and the deep convolutional neural network is used to extract the content features of the… More >

Yuchen Li¹, Baohong Ling^1,2,*, Donghui Hu¹, Shuli Zheng¹, Guoan Zhang³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2213-2225, 2023, DOI:10.32604/iasc.2023.029983 - 21 June 2023

Abstract The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms. The traditional steganalysis detector is trained on the stego images created by a certain type of steganographic algorithm, whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm. This phenomenon is called as steganographic algorithm mismatch in steganalysis. To resolve this problem, we propose a deep learning driven feature-based approach. An advanced steganalysis neural network is used to extract steganographic features, different pairs of training images embedded with steganographic More >

Lingyun Xiang^1,*, Rong Wang¹, Yuhang Liu¹, Yangfan Liu¹, Lina Tan^2,3

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2383-2399, 2023, DOI:10.32604/csse.2023.037067 - 09 February 2023

Abstract Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts, by performing binary classification. While it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts coexist. In this paper, we propose a general linguistic steganalysis framework named LS-MTL, which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic texts. LS-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently… More >