Image steganalysis based on cover selection

Image steganalysis aims to identify the presence of secret information by excavating the statistical feature differences of carrier images. However, with the rapid development of deep learning steganography technology, its detection effectiveness in complex heterogeneous environments faces severe challenges. Traditional methods, due to the lack of the ability to adaptively process the heterogeneity of image content, find it difficult to effectively deal with the diverse and mixed image types in real-world scenarios. Aiming at this problem, this paper proposes the Cover Selection Network CSSNet. On the one hand, it realizes the semantic pre-classification of image scenes through a forensic-assisted algorithm, and on the other hand, it achieves differential feature representation from heterogeneous images. This algorithm effectively balances the capabilities of global semantic perception and local detail analysis through a hierarchical feature fusion strategy. Experimental results show that, on a mixed dataset of multiple types of samples, the detection accuracy of CSSNet is higher than that of existing methods, and it shows significant advantages in cross-scene generalization ability, providing a new solution for steganalysis in complex environments.