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（SWM）通过将句子作为基本单元来提升对文本篡改的鲁棒性。然而，对段落级释义的鲁棒性仍然困难，此类攻击通过改变句子顺序全局性地破坏水印信号。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来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，在放大水印信号的同时抑制弱对齐候选者带来的噪声。我们进一步提出多样性感知过滤策略，结合硬过滤与软正则化，超越简单的n元重复过滤以处理语义冗余。实验结果表明，在典型段落级释义攻击下，SAMark在1%假阳性率条件下真实阳性率可达90.2%，平均性能超越最强基线方法30%以上，同时保持与无水印文本竞争性的生成质量，突破了已有方法在鲁棒性与质量间的权衡限制。