Image Steganalysis Based on Dual-Path Enhancement and Fractal Downsampling

Abstract

Image steganalysis has always been an important topic in the field of information security, and researchers have designed many excellent steganalysis models. However, the existing steganalysis models tend to construct a single path and increase the convolution kernels to reduce the size of feature maps, which is not comprehensive enough to extract the features and may boost the number of parameters. In addition, the single residual block stacking may pay attention to protecting stego signals and neglect the mining of hidden features. To address these issues, we propose a steganalysis model based on dual-path enhancement and fractal downsampling, which is suitable for both spatial and JPEG domains. The model reuses and strengthens noise residuals through two dual-path enhancement blocks, and designs a fractal downsampling block for downsampling at multiple levels, angles, and composition structures. The experimental results demonstrate that the proposed model achieves the best detection performance in both spatial and JPEG domains compared with other start-of-the-art methods. Besides, we design a series of ablation experiments to verify the rationality of each component.