Image steganalysis based on CNN-Transformer

Current convolutional neural network (CNN) steganalysis models primarily focus on the local features of steganographic images. Although CNNs expand their receptive field by stacking deeper convolutional layers, their ability to extract global features remains limited. For large images, focusing on global features can significantly improve steganalysis performance. We propose a hybrid model named CTS-Net (CNN-Transformer image steganography network) for image steganalysis. This model effectively captures both local and global features dependencies of the steganographic signals. In the preprocessing stage, multi-scale residual extraction and information fusion are applied to improve the signal to noise ratio. In the feature extraction stage, CNN and Transformer are combined to extract both local and global features, enhancing detection accuracy for large size steganographic images. Finally, a fully connected layer is used for classification. Experiments on the public dataset BOSSbase1.01, using the WOW, HILL, and S-UNIWARD steganography algorithms at different embedding rates for detection, show that at a low embedding rate (0.1 bpp), the CTS-Net model achieves the best detection accuracy. On the public dataset ALASKA#2, the WOW steganography algorithm is used for steganalysis across 16 image sets of different sizes. The results demonstrate that the CTS-Net model effectively leverages global features of the steganographic signals on both fixed-size and heterogeneous datasets, achieving superior detection accuracy compared to SRNet, SiaStegNet, CvT Net, and CVTS.