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

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


Guoliang Xie

Jinchang Ren

Stephen Marshall

Huimin Zhao

Rui Li

Rongjun Chen


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.


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:

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
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 139
Article Number 104063
Keywords Image steganography; Spatial image steganalysis; Res2Net; BoTNet; Convolutional Neural Network (CNN)
Public URL


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