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Volume 40 Issue 2
May  2025
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Wei KK, Luo WQ, Tan SQ et al. CTNet: A convolutional Transformer network for color image steganalysis. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 40(2): 413−427, Mar. 2025. DOI: 10.1007/s11390-023-3006-3
Citation: Wei KK, Luo WQ, Tan SQ et al. CTNet: A convolutional Transformer network for color image steganalysis. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 40(2): 413−427, Mar. 2025. DOI: 10.1007/s11390-023-3006-3
shu

CTNet: A Convolutional Transformer Network for Color Image Steganalysis

  • 1.

    School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China

  • 2.

    Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510006, China

  • 3.

    College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

  • 4.

    Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China

  • 5.

    Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen 518060, China

  • 6.

    Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518129, China

Funds: 

The work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61972430 and U19B2022.

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  • Author Bio:

    Kang-Kang Wei received his M.E. degree in computer technology from Jiangxi University of Finance and Economics, Nanchang, in 2020. He is currently a Ph.D. candidate of School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou. His research interests include image steganalysis, steganography, and deep learning

    Wei-Qi Luo received his Ph.D. degree in computer science and technology from Sun Yat-sen University, Guangzhou, in 2008. He is currently a professor with the School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, and also a researcher with the Guangdong Key Laboratory of Information Security Technology, Guangzhou. His current research interests include digital multimedia forensics, steganography, and steganalysis

    Shun-Quan Tan received his B.S. degree in computational mathematics and applied software and his Ph.D. degree in computer software and theory from Sun Yat-sen University, Guangzhou, in 2002 and 2007, respectively. In 2007, he joined the College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, where he is currently an associate professor. He is also the vice director of the Shenzhen Key Laboratory of Media Security. His current research interests include multimedia security, multimedia forensics, and machine learning

    Ji-Wu Huang received his B.S. degree from Xidian University, Xi’an, in 1982, his M.S. degree from Tsinghua University, Beijing, in 1987, and his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, Beijing, in 1998. He is currently a professor with the College of Electronics and Information Engineering, Shenzhen University, Shenzhen. His current research interests include multimedia forensics and security

  • Corresponding author:

    luoweiqi@mail.sysu.edu.cn

  • Received Date: December 02, 2022
  • Accepted Date: October 15, 2023
    Abstract

  • Compared with convolutional neural network (CNN), Transformer can obtain global receptive field features more effectively and has recently achieved great success in natural language processing and computer vision. Due to the particularity of steganography, however, almost all existing steganalytic networks just employ CNN with local receptive fields to detect embedding artifacts. In this paper, we propose a novel convolutional Transformer network for color image steganalysis. Specifically, we firstly obtain various image residuals for each color channel of an input image in the pre-processing module. To capture more comprehensive steganalytic features, the truncated residuals after channel concatenation will pass through a feature extraction module composed of a CNN group and a Transformer group. The CNN group aims to extract local receptive fields features, while the Transformer group with multi-head self-attention as the key tries to extract global steganalytic features. Finally, we employ a global covariance pooling (GCP) and two fully-connected (FC) layers with dropout for classification. Extensive comparative experiments demonstrate that the proposed method can significantly improve the detection performances in color image steganalysis and achieve state-of-the-art results. Although the proposed method is originally designed for color images, it can also obtain competitive results for grayscale images compared with the current best detector. In addition, we provide numerous ablation studies to verify the rationality of the proposed network architecture.

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    • References (41)
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