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.

翻译：语义级水印（Semantic-level Watermarking, SWM）以句子为基本单元，增强了对抗文本修改的鲁棒性。然而，段落级别释义的鲁棒性仍是难题，因为此类攻击通过改变句子顺序全局扰乱水印信号。本文提出SAMark——一种自锚定水印框架，通过在语义空间中建立与步骤无关的绿色区域，消除对句子顺序的依赖。为提升可检测性，我们引入多通道双曲评分机制，该机制既能放大水印信号，又能抑制弱对齐候选者引入的噪声。进一步，我们提出多样性感知过滤策略，将硬过滤与软正则化相结合，超越简单的n-gram重复过滤器，以解决语义冗余问题。实验表明，在典型的段落级别释义攻击下，SAMark在TP@FP1%指标上达到90.2%，平均性能超越最强先前基线超过30%，同时维持与未加水印文本相当的生质量，突破了先前方法受限于鲁棒性与质量之间的权衡瓶颈。