A Labeled Intermediate Domain Aided Two-Domain Correlation Fusion for Mismatched Steganalysis

When the target images to be detected and the source images used to train the steganalyzer come from different distributions, the cover-source mismatch (CSM) occurs, which often leads to a sharp decrease in detection accuracy. To alleviate the problem, this letter proposes a four-stage steganalyzer, named ICSNet. The first three stages concentrate on generating a labeled intermediate domain to build a bridge between source/target domains. To be specific, the labeled intermediate domain is constructed by first adding the noise to target samples using a noise adding module to generate intermediate samples following the source domain distribution, and subsequently performing data embedding to these intermediate samples to generate the stego intermediate samples. The last stage focuses on strengthening the fusion of channel-wise and spatial correlations between source/target domains by presenting a coarse-to-fine two-domain channel-wise correlation fusion (CCF) module and a source-guided two-domain spatial correlation fusion (SCF) module. In CCF, the labeled intermediate domain works as a supplementary to the target domain, so that source/target domains can guide each other to reinforce the fusion of channel-wise correlations. In SCF, the labeled source domain guides the target domain to consolidate the fusion of spatial correlations. The four stages work together to reduce the distribution shift between source/target domains, thereby bringing performance improvement. Experimental results demonstrate that ICSNet significantly outperforms existing methods across various CSM scenarios.