REED: Post-Training Representation Editing for Cross-Domain Linguistic Steganalysis
For practitioners of linguistic steganalysis, REED provides a lightweight solution to domain shift that requires no retraining, though improvements are incremental over existing cross-domain methods.
REED addresses cross-domain linguistic steganalysis by post-training editing of intermediate representations without modifying the detector architecture or updating parameters. It achieves high F1-scores across unseen domains, outperforming advanced methods.
In real-world scenarios of linguistic steganalysis, tested texts usually come from unseen domains with different vocabularies, topics, writing styles, and steganographic generation patterns, which can significantly degrade the detection performance. Although existing cross-domain steganalysis methods can effectively alleviate this problem through distribution alignment, domain-invariant feature learning, etc., the detection performance is not satisfactory. In this paper, we propose a post-training representation editing method for cross-domain linguistic steganalysis. Specifically, the detector is first trained on source-domain data, and then the feature extractor and classifier are kept frozen, and the intermediate representations are deterministically edited before classification. For domain adaptation, we construct a domain-offset vector from marginal source and target representations. For domain generalization, we derive a source-domain cover-to-stego direction to guide sample-specific editing. Experimental results show that compared with the advanced methods, the proposed method can achieve high cross-domain detection performance, especially in terms of F1-score, while requiring no architecture modification or parameter updates after source-domain training.