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 matches state-of-the-art detection performance on unaltered watermarked text, while significantly improving robustness against paraphrasing, increasing detection AUC by up to 13.9 percentage points compared to prior methods. These results demonstrate that SWAN's approach of anchoring watermarks in AMR semantic structures provides a simple, effective, and prompt-based method for robust text provenance verification under paraphrasing, opening new avenues for semantic-level watermarking research.

翻译：我们提出SWAN（基于抽象语义表示（AMR）的语义水印）这一新颖框架，通过利用抽象语义表示（AMR）将水印签名嵌入句子的语义结构中。与现有通常在文本生成过程中通过调整词元选择偏好来编码签名的水印方法不同，SWAN直接将签名编码在句子的语义表示层。由于签名在语义结构层面进行编码，任何保留原意的改写都会自动保留该签名。SWAN无需训练：水印注入通过提示大语言模型（LLM）在保持上下文连贯性的同时，依据选定的AMR模板生成文本来实现；检测则采用现成的AMR解析器，再辅以简单的单比例z检验。在RealNews基准上的实证评估表明，SWAN在未经修改的水印文本上的检测性能达到当前最优水平，同时显著提升了对抗改写攻击的鲁棒性：与先前方法相比，检测AUC最高提升了13.9个百分点。这些结果证明，SWAN将水印锚定在AMR语义结构中的方法，提供了一种简单、有效且基于提示的解决方案，可在改写场景下实现稳健的文本来源验证，为语义层面水印研究开辟了新途径。