SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness

Abstract:Semantic-level watermarking (SWM) improves robustness against text modifications by treating sentences as the basic unit. However, robustness to paragraph-level paraphrasing remains difficult because such attacks globally disrupt watermark signals by changing sentence order. In this work, we propose SAMark, a self-anchored watermarking framework that removes the dependency on sentence order by establishing a step-independent green region in semantic space. To improve detectability, we introduce a multi-channel hyperbolic scoring mechanism that amplifies watermark signals while suppressing noise from weakly aligned candidates. We further propose a diversity-aware filtering strategy that combines hard filtering with soft regularization, extending beyond simple n-gram repetition filters to address semantic redundancy. Experimental results show that SAMark achieves up to 90.2% TP@FP1% under typical paragraph-level paraphrasing attacks, outperforming the strongest prior baseline by more than 30% on average, while maintaining generation quality competitive with unwatermarked text and breaking the robustness-quality trade-off that limits prior methods.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2605.25796 [cs.CR]

(or arXiv:2605.25796v2 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2605.25796 Focus to learn more arXiv-issued DOI via DataCite

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