JPEG steganalysis based on denoising network and attention module. Issue 8 (16th November 2021)

• Record Type: Journal Article
• Authors: Wu, Tian; Ren, Weixiang; Li, Dewei; Wang, Lina; Jia, Ju
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 8(2022)
• Page Start: 5011
• Page End: 5030
• Publication Date: 2021-11-16
• Subjects: attention module -- deep learning -- noise extraction -- steganalysis; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22749
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The core objective of image steganalysis is to explore the presence of weak image steganographic signals. Extracting effective steganographic signal features will play an essential role in digital image steganalysis. However, existing networks rely more on spatial rich model kernels or random learnable kernels to obtain noise residuals during the stage of steganographic signal features extraction. In this paper, we proposed a JPEG steganalysis network which based on denoising network and attention module, mainly including a noise extract block, a noise analysis block, and a judgment block. Specifically, a professional denoising convolutional neural network is first introduced in noise extract block to obtain better steganalysis features. The noise analysis block is integrated with the attention module to finely extract the steganographic signals hidden in the complex texture regions, which is quite effective in improving the signal‐to‐noise ratio of the stego signal. The judgment block is primarily a classifier to distinguish between cover images and stego images. Comprehensive experiments show a significant improvement in performance over the state‐of‐the‐art steganalysis scheme. Moreover, the proposed network has better generalization capability than the compared steganalysis network for the case of cover‐source and quality factor mismatch, which is critical for future steganalysis systems.