A two-stream-network based steganalysis network: TSNet

Abstract

The three-stage framework consisting of preprocessing, feature extraction and classification is the most commonly used structure in traditional deep learning based steganalysis networks (SNs). Preprocessing, as the core part of the three-stage framework, typically adopts fixed high-pass filters to generate filter residuals, and exploits a feature enhancement module following high-pass filters to improve signal-to-noise ratio of the stego signal, thus strengthening the expression of steganographic features. However, fixed high-pass filters can only provide filter residuals through the linear relationships, rather than complex dependencies automatically learned, among local pixels, which suggests that fixed high-pass filters lack powerful ability of extracting comprehensive steganographic features from the beginning of SNs. Inspired by the motivation, a two-stream-network based SN called TSNet is proposed to extract comprehensive steganographic features from two different perspectives, namely automatic learning and fixed filtering. The discrete cosine transform attention mechanism (DCTAM)-based fusion module (DCTAM-FM) with considering the steganalysis properties is proposed to fuse two streams and enhance channel representation using DCTAM, which is conductive to improving the detection accuracy. Two streams aim to explore different types of residuals from two perspectives, but inevitably lead to a considerable number of parameters and high computational complexity. To this end, the feature extraction stage contains lightweight depthwise separable convolution blocks and the linearly increasing style of the number of channels. Combining the two-stream network, DCTAM-FM as well as the lightweight feature extraction stage, TSNet obtains deep composite features, which can fully express the comprehensive steganographic features, and thus brings a high detection accuracy. Experimental results from three benchmark datasets demonstrate that TSNet outperforms current state-of-the-art methods in detection accuracy and has an acceptable computational complexity.