Spatial Steganalysis Based on Non-Local Block and Multi-Channel Convolutional Networks

Image steganalysis aims to detect whether secret information is hidden in images and is a means to solve the communication security. Recently, a series of convolutional neural network-based steganalysis models has been proposed and has achieved remarkable performance. Given that the steganalysis task is different from other computer vision tasks, the series of models is inevitably restricted by manual factors, and some other useful residual information will be missed in the process of feature extraction. To solve this problem, this paper proposes a network model based on non-local operations and multi-channel convolution as part of the basic block of feature extraction for spatial grayscale image steganalysis. The preprocessing layer is improved to compress the image content, further using the introduced non-local operations and multi-channel convolution modules to enhance the residual information in high-frequency regions and extract diverse steganographic features effectively. Extensive ablation study shows that the introduced extraction module can improve the steganographic detection accuracy effectively. Our method is generally better than other methods in terms of detection accuracy, especially in low payload detection.