Clustering-Driven Pseudo-Labeling in Source-Free Domain Adaptation for Linguistic Steganalysis

Linguistic steganalysis often encounters domain shift in practice, mainly due to differences in text sources and steganographic methods.These variations create distribution discrepancies between the training (source domain) and test (target domain) sets, reducing detection accuracy.Most existing domain adaptation methods for linguistic steganalysis rely on labeled source domain data to alleviate these issues, but due to data privacy or transmission costs, source domain data is often unavailable in many real-world scenarios.Without access to this data, models cannot directly compare feature distributions between the source and target domains, hindering the model's ability to learn the target domain's features and ultimately affecting detection performance for stego texts.In this paper, we propose a Clustering-driven Pseudo-labeling method for Source-free domain adaptation in Linguistic Steganalysis (CPSLS).During the adaptation phase, we leverage the clustering structure of the target domain data to generate pseudo-labels, helping the model identify stego features in the target domain.Additionally, we use a weighted classification loss function to reduce the impact of incorrect pseudo-labels.To prevent the model from overlooking the diversity between stego and cover texts during optimization, we introduce a prediction diversity loss, improving the model's ability to differentiate between the two.Experimental results show that CPSLS not only has stronger practical applicability but also outperforms existing domain adaptation linguistic steganalysis in terms of detection accuracy.