Image Steganalysis Network Based on Dual-Attention Mechanism

Most of the existing image steganography methods are content adaptive steganography, which prefer to modify the pixel values of texture information-rich regions, making them more difficult to be detected. Many steganography detectors have been developed using a CNN network with selected channels. However, the utilization and processing of the prior information inside the network may not be completely adequate. To address such problem, we propose a strategy based on a spatial and channel multiple attention mechanism for improving the accuracy of the network. To further exploit the texture information of the image itself, spatial attention is used throughout the network, which is achieved by fusing the Sobel operator features with the embedded probability map in a suitable way. On the other hand, channel attention uses the relationship between two-dimensional DCT and global average pooling to weight the features. Experimental results show that our strategy has competitive performance in both spatial and DCT domains on the ALASKA #2 dataset.