PSDR-SNet: Siamese network of potential steganographic signal difference regions for image steganalysis

Image steganalysis aims to detect whether an image has been processed with steganography to carry hidden information. The steganalysis algorithm based on Siamese network determines whether an image contains hidden information by calculating the dissimilarity between its left and right partitions, offering a new approach. However, it does not consider that steganographic signals often embed in the edges or texture-complex regions of the cover image, leaving significant room for improvement. To address this, this paper proposes a Siamese network of potential steganographic signal difference regions for image steganalysis. The proposed method fully considers the distribution characteristics of steganographic signals by segmenting the target image into texture-complex regions that are likely to contain more steganographic signals and texture-smooth regions that are likely to contain fewer signals. This provides a more effective regional division strategy for the subsequent network to analyze the differences in steganographic signals between different regions. In addition, a redesigned similarity loss function is introduced to guide the network to focus more on the subtle differences in potential steganographic signals between the segmented regions rather than differences in image content. The presence of steganographic information is determined by calculating the differences in potential steganographic signals across the divided regions. Experimental results on the BOSSbase dataset show that the proposed method achieves a maximum detection accuracy of 91.71% for the SUNIWARD steganographic algorithm at a payload of 0.4 bpp, representing an improvement of 1.58% over the baseline model SiaStegNet. The proposed method also achieves a 1.39% accuracy improvement when detecting the WOW steganographic algorithm. These results fully demonstrate the superior detection performance and robustness of the proposed method in image steganalysis.