An Image Steganalysis Algorithm Based on Multi-Resolution Feature Fusion

The misuse of image steganography poses significant risks to societal security. Whether images include concealed data is a critical problem of information security. Traditional convolutional layers often fail to adequately capture the global correlation of steganographic features as network depth increases, leading to redundant model parameters and missing key features, thereby weakening steganographic signal detection. To solve the problems of highly covert steganographic algorithms and the weakness of traditional methods, a steganography detection solution using multi-resolution feature fusion is presented. This approach uses a multi-resolution network to increase the interactivity from higher to lower resolution. The results of the experiments confirm that the proposed algorithm allows for a maximum accuracy of 90.56% for the embedding rate of 0.4bpp. The overall results prove that the proposed model achieves higher accuracy and better performance than some leading steganalysis models available when applied to different steganographic algorithms and embedding rate conditions.