发表机构
The University of Texas at Austin（德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将SynthID锦标赛水印嵌入基因组语言模型，实现无需位置信息的DNA来源追踪，在1,544个提示中检测到全部标记序列，假阳性率低，且不影响模型输出质量。
AI 中文摘要
基因组语言模型可以生成不携带其来源记录的合成DNA。如果验证者仅使用DNA序列、密钥和公开的检测器设置，无需知道生成区域的起始位置、所在链或如何将其划分为六碱基标记，那么生成时水印可以提供来源信号。我们将SynthID锦标赛水印嵌入到两个基因组语言模型Carbon和GENERator-v2中。验证者搜索两条链、每个起始位置和四种窗口长度，并纠正整个搜索的决策。在包含1,544个保留提示的检测队列中，它找到了每个模型的所有3,088个标记序列，包括未编辑的和经过一个碱基替换、插入或删除后的序列。对于普通序列，单侧95%置信上限的假阳性率最多为0.850%；错误密钥对照的一个置信上限达到1.015%（Carbon，删除后）。在开发队列中，两个模型在似然性或预定义序列度量上均未显示出可检测的变化。在随机编辑（不参考检测器）下，检测在每碱基编辑率高达2%时保持完整或几乎完整。这些结果表明统计检测，而非生物功能、密钥安全性或对能看到检测器的编辑者的鲁棒性。
英文摘要
Genomic language models can write synthetic DNA that carries no record of its origin. A generation-time watermark could provide a provenance signal, if a verifier can detect it using only the DNA sequence, a key, and published detector settings, without knowing where the generated region starts, which strand it lies on, or how it was divided into six-base tokens. We embed the SynthID tournament watermark in two genomic language models, Carbon and GENERator-v2. The verifier searches both strands, every start position, and four window lengths, and corrects its decision for the whole search. In a detection cohort of 1,544 held-out prompts, it found all 3,088 marked sequences per model, unedited and after one substituted, inserted, or deleted base. For ordinary sequences, the one-sided 95% upper confidence bound on the false-positive rate was at most 0.850%; one bound for the wrong-key control reached 1.015% (Carbon, after a deletion). In a development cohort, neither model showed a detectable change in likelihood or in predefined sequence measures. Under random edits placed without reference to the detector, detection stayed complete or nearly complete up to a 2% per-base edit rate. These results show statistical detection, not biological function, secret-key security, or robustness to an editor who sees the detector.
Comments16 pages
