TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Sander, Tom Chang, Hongyan Souček, Tomáš Tran, Tuan Lacatusu, Valeriu Rebuffi, Sylvestre-Alvise Mourachko, Alexandre Parimi, Surya Ropers, Christophe Moritz, Rashel Stark, Vanessa Elsahar, Hady Fernandez, Pierre |
| author_facet | Sander, Tom Chang, Hongyan Souček, Tomáš Tran, Tuan Lacatusu, Valeriu Rebuffi, Sylvestre-Alvise Mourachko, Alexandre Parimi, Surya Ropers, Christophe Moritz, Rashel Stark, Vanessa Elsahar, Hady Fernandez, Pierre |
| contents | We introduce TextSeal, a state-of-the-art watermark for large language models. Building on Gumbel-max sampling, TextSeal introduces dual-key generation to restore output diversity, along with entropy-weighted scoring and multi-region localization for improved detection. It supports serving optimizations such as speculative decoding and multi-token prediction, and does not add any inference overhead. TextSeal strictly dominates baselines like SynthID-text in detection strength and is robust to dilution, maintaining confident localized detection even in heavily mixed human/AI documents. The scheme is theoretically distortion-free, and evaluation across reasoning benchmarks confirms that it preserves downstream performance; while a multilingual human evaluation (6000 A/B comparisons, 5 languages) shows no perceptible quality difference. Beyond its use for provenance detection, TextSeal is also ``radioactive'': its watermark signal transfers through model distillation, enabling detection of unauthorized use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12456 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection Sander, Tom Chang, Hongyan Souček, Tomáš Tran, Tuan Lacatusu, Valeriu Rebuffi, Sylvestre-Alvise Mourachko, Alexandre Parimi, Surya Ropers, Christophe Moritz, Rashel Stark, Vanessa Elsahar, Hady Fernandez, Pierre Cryptography and Security Computation and Language Machine Learning We introduce TextSeal, a state-of-the-art watermark for large language models. Building on Gumbel-max sampling, TextSeal introduces dual-key generation to restore output diversity, along with entropy-weighted scoring and multi-region localization for improved detection. It supports serving optimizations such as speculative decoding and multi-token prediction, and does not add any inference overhead. TextSeal strictly dominates baselines like SynthID-text in detection strength and is robust to dilution, maintaining confident localized detection even in heavily mixed human/AI documents. The scheme is theoretically distortion-free, and evaluation across reasoning benchmarks confirms that it preserves downstream performance; while a multilingual human evaluation (6000 A/B comparisons, 5 languages) shows no perceptible quality difference. Beyond its use for provenance detection, TextSeal is also ``radioactive'': its watermark signal transfers through model distillation, enabling detection of unauthorized use. |
| title | TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection |
| topic | Cryptography and Security Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2605.12456 |