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RestegNet: a residual steganalytic network

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Abstract

Deep convolutional networks bring new energy to image steganography. It is an opportunity for steganalysis research. However, the operations to widen the gap between covers and stegos are only in the preprocessing layers for most existing networks. In this paper, a residual steganalytic network (RestegNet) is proposed to overcome this limitation. We design a novel building block group, which consists of two alternating building blocks: 1) A sharpening block based on residual connections (ShRC), which makes the noise of steganography overwhelm the image content, and aims to enhance steganographic signal detectability. 2) A smoothing block based on residual connections (SmRC), which seeks to downsample the feature maps to boil them down to useful data. First, we use the same preprocessing layers as previous methods to ensure minimum performance. Then, we use these building block groups to exaggerate the traces of steganography further and make the difference between covers and stegos in the feature extraction layers. Contrastive experiments with previous methods conducted on the BOSSbase 1.01 demonstrate the effectiveness and the superior performance of the proposed network.

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Notes

  1. http://github.com/spadeke/RestegNet

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Acknowledgments

This work was supported by NSFC under 61802393, U1736214, U1636102 and 61872356, National Key Technology R&D Program under 2016YFB0801003 and 2016QY15Z2500, and Project of Beijing Municipal Science & Technology Commission under Z181100002718001.

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Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Weike You, Xianfeng Zhao, Sai Ma & Yaqi Liu

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, 100093, China

    Weike You, Xianfeng Zhao, Sai Ma & Yaqi Liu

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  1. Weike You
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Correspondence to Xianfeng Zhao.

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You, W., Zhao, X., Ma, S. et al. RestegNet: a residual steganalytic network. Multimed Tools Appl 78, 22711–22725 (2019). https://doi.org/10.1007/s11042-019-7601-9

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