RCDD: Contrastive domain discrepancy with reliable steganalysis labeling for cover source mismatch

Abstract

The cover source mismatch (CSM) can be very challenging for steganalysis because different distribution between source and target inevitably leads to poor performance of the steganalyzer on the target domain. In general, some methods from unsupervised domain adaptation, such as contrastive domain discrepancy (CDD), can be directly applied to steganalysis for addressing the CSM problem, but they cannot achieve satisfactory detection accuracy due to the neglect of steganographic characteristics. To solve this problem, reliable steganalysis labeling (RSL)-based CDD (RCDD) taking steganographic characteristics in account is proposed in this paper, which relies on RSL to generate reliable labels for extended target images, rather than utilizing clustering in CDD to obtain unreliable pseudo labels for target images. Through detailed deduction process, we know that RCDD draws closer the distribution of source and target classwisely so as to enhance the classification performance on the target domain. Simultaneously, a corresponding steganalysis network RCDD-Net is yielded by incorporating some backbone into RCDD. A large number of experiments verify that RCDD-Net is an innovation in steganalysis that effectively alleviates performance degradation when CSM occurs. Moreover, RCDD-Net provides better detection performance than several advanced steganalysis networks.