SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis

The steganalysis for JPEG image is an important research topic, as the enormous popularity of JPEG image on Internet. However, the stego noise feature extraction process of the existing deep learning-based steganalytic methods are not adaptive enough to the content of the image, which may lead to suboptimal steganalysis performance. In order to solve this issue, an adaptive stego noise extraction network, named SNENet, for JPEG image steganalysis is proposed. The stego noise extraction module of the network is specifically designed for steganalysis, which consists of parallel dilated convolutional layer and inverted bottleneck layer. This specific design expands the receptive field of the network, which makes the extraction of the stego noise more global and adaptive to the content of the image. The experimental results indicate that proposed network outperforms the state-of-the-art steganalytic method by as much as 6.25% for UED-JC and 3.35% for J-UNIWARD. The design of the network is also justified in the extensive ablation experiments.