SWAN: Semantic Watermarking with Abstract Meaning Representation
Ziping Ye, Gourab Dey, Christos Christodoulopoulos, Charith Peris, Anil Ramakrishna, Weitong Ruan, Aram Galstyan, Kai-Wei Chang, Rahul Gupta, Ninareh Mehrabi

TL;DR
SWAN introduces a semantic watermarking framework embedding signatures into sentence structures via Abstract Meaning Representation, enhancing robustness against paraphrasing and enabling prompt-based, training-free text provenance verification.
Contribution
It is the first to embed watermarks directly in semantic structures using AMR, improving robustness and simplicity over existing token-based methods.
Findings
SWAN achieves state-of-the-art detection performance on unaltered text.
It significantly improves robustness against paraphrasing, with up to 13.9% higher detection AUC.
The method is training-free and relies on prompt-guided generation and AMR parsing.
Abstract
We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation), a novel framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). In contrast to existing watermarking methods, which typically encode signatures by adjusting token selection preferences during text generation, SWAN embeds the signature directly in the sentence's semantic representation. As the signature is encoded at the semantic structure level, any paraphrase that preserves meaning automatically preserves the signature. SWAN is training-free: watermark injection is achieved by prompting an LLM to generate sentences guided by a selected AMR template while maintaining contextual coherence, and detection uses an off-the-shelf AMR parser followed by a simple one-proportion z-test. Empirical evaluation on the RealNews benchmark shows SWAN…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
