Pair-wise Confidence Difference-based Pseudo-Label Selection for Universal Mismatched Steganalysis

摘要

Image steganalysis is a detection task to distinguish whether a secret message is embedded in a digital image. Due to the domain inconsistency caused by Cover Source Mismatch(CSM) and Steganographic Algorithm Mismatch (SAM), most of them suffer from significant performance degradation. Recent mismatched steganalysis focused on extracting domain invariant features by domain adversarial training or feature alignment. However these schemes are limited to unstable performance in diverse domain mismatch scenarios, and are even ineffective in some cases. In this paper, we propose a Universal Mismatched Steganalysis PCD-UMS via pair-wise confidence difference-based pseudo-label selection from the perspective of optimizing target training data. Specifically, we reveal a strong positive correlation commonality between pair-wise confidence difference and the detection performance of steganalysis among various mismatch scenarios. Based on this, a novel pseudo-label selection strategy consisting of maximum confidence difference first (MCDF) rule and pair-wise label differential storage (PLDS) rule is designed to select and filter the reliable target pseudo-labels. Furthermore, a multi-perspective pair-wise feature alignment loss is designed to initially transfer the classification ability of source steganalysis, thus solving the problem that source steganalysis fails completely under some domain mismatch scenarios. Comprehensive experiments show that our PCD-UMS outperforms the existing mismatched steganalysis by 12.07% and 3.40% in terms of detection performance under CSM and SAM scenarios.