Self-attention enhanced deep residual network for spatial image steganalysis.

Xie, Guoliang; Ren, Jinchang; Marshall, Stephen; Zhao, Huimin; Li, Rui; Chen, Rongjun

Abstract

As a specially designed tool and technique for the detection of image steganography, image steganalysis conceals information under the carriers for covert communications. Being developed on the BOSSbase dataset and released a decade ago, most of the Convolutional Neural Network (CNN) architectures for spatial image steganalysis fail to achieve satisfactory performance on new challenging datasets, i.e. ALASKA#2, which was released recently and is more complex yet consistent with the real scenarios. In this paper, we propose an enhanced residual network (ERANet) with self-attention ability, which utilizes a more complex residual method and a global self-attention technique, to alleviate the problem. Compared to the residual network that was widely used in the state-of-the-art, the enhanced residual network mathematically employed a more sophisticated way to extract more effective features in the images and hence it is suitable for more complex situations in the new dataset. Our proposed Enhanced Low-Level Feature Representation Module can be easily mounted on other CNNs in selecting the most representative features. Although it comes with a slightly extra computational cost, comprehensive experiments on the BOSSbase and ALASKA#2 datasets at various sizes have demonstrated the effectiveness of the proposed methodology. In short, ERANet provides an improvement of about 3.77% on average, compared to a few state-of-the-art CNNs.

Citation

XIE, G., REN, J., MARSHALL, S., ZHAO, H., LI, R. and CHEN, R. 2023. Self-attention enhanced deep residual network for spatial image steganalysis. Digital signal processing [online], 139, article 104063. Available from: https://doi.org/10.1016/j.dsp.2023.104063

• Journal Article Type: Article
• Acceptance Date: May 10, 2023
• Online Publication Date: May 25, 2023
• Publication Date: Jul 31, 2023
• Deposit Date: May 19, 2023
• Publicly Available Date: May 26, 2024
• Journal: Digital signal processing
• Print ISSN: 1051-2004
• Electronic ISSN: 1095-4333
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 139
• Article Number: 104063
• DOI: https://doi.org/10.1016/j.dsp.2023.104063
• Keywords: Image steganography; Spatial image steganalysis; Res2Net; BoTNet; Convolutional Neural Network (CNN)
• Public URL: https://rgu-repository.worktribe.com/output/1961803