Improving CoatNet for Spatial and JPEG Domain Steganalysis

The covert communication technology represented by image steganography can realize the safe and reliable information transmission over an open network. Steganalysis is an important way to measure the security of steganography. The application of deep learning in the field of steganalysis has begun to emerge and achieved certain results. However, the existing steganalysis methods based on deep learning extract limited global features and the detection accuracy still needs to be improved. Therefore, this manuscript proposes an improving CoatNet for spatial and JPEG domain steganalysis (IMCoatNet). Firstly, the SKAttention structure is used to extract fine-grained features. Then, the convolution layer, Transformer layer and the pyramid squeezed attention is used to capture multi-scale spatial features. Finally, a weighted loss is designed to enhance the expression ability of the model. The results show that compared with the traditional Spatial Rich Model (SRM) method, the classical deep residual network for steganalysis (SRNet), and the latest siamese CNN for steganalysis (SiaStegNet), the detection accuracy of the proposed CoatNet method for WOW steganography is improved by 19.61%, 4.33% and 5.04%, respectively; The proposed IMCoatNet is also superior on JPEG domain detection.