RestegNet: a residual steganalytic network

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.