Effective JPEG Steganalysis Using Non-Linear Pre-Processing and Residual Channel-Spatial Attention

Nowadays, convolutional neural network (CNN)-based JPEG steganalyzers have demonstrated much better performance than the conventional steganalysis methods based on hand-crafted feature sets. By incorporating the selection-channel aware (SCA) knowledge, the performance of the deep learning-based approach could be further improved. For prac-tical applications, however, the SCA knowledge is usually u-navailable to steganalyzers. In this paper, a novel CNN model is proposed by including extra non-linear kernels in the first network layer to enhance stego signal and exploring the resid-ual channel-spatial attention (CSA) module which plays the same role as SCA to further improve the performance. In addition, instead of global average pooling, spatial pyramid pooling (SPP) is adopted to better preserve the hierarchical feature representation at various scales for JPEG steganaly-sis. Experimental results show that the proposed CNN model with extra non-linear kernels, CSA and SPP outperforms other state-of-the-art deep learning-based approaches for JPEG steganalysis.