Adversarial Embedding Steganography via Progressive Probability Optimizing and Discarded Stego Recycling

Fan Wang, Zhangjie Fu, Xiang Zhang, Junjie Lu

Abstract: Adversarial embedding for image steganography is a novel technology to effectively enhance the steganographic security of the traditional steganographic algorithms. However, the existing schemes still have room for further improvement in the design of optimization strategy and the steganographic post-processing of optimization failure. In this paper, we design the progressive probability optimizing strategy (PPO). It dynamically selects more efficient gradients to guide the optimization of the probability optimization in a progressive manner. Moreover, we propose a discarded stego recycling mechanism (DSR) to re-select the stego from the discarded stego set that have failed to deceive the target steganalyzer after the optimzation fails. In such way, the statistical distribution of the stego can still further approximate the cover, thus further improving the steganographic security on re-trained steganalyzers in adversary-aware scenario. Comprehensive experiments show that compared with the existing advanced schemes, the proposed method boosts the security improvement against both the re-trained hand-crafted feature-based and deep leanring-based steganalysis models.

External IDs:doi:10.1109/lsp.2024.3478109