De-mark: Watermark Removal in Large Language Models
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913923288006656 |
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| author | Chen, Ruibo Wu, Yihan Guo, Junfeng Huang, Heng |
| author_facet | Chen, Ruibo Wu, Yihan Guo, Junfeng Huang, Heng |
| contents | Watermarking techniques offer a promising way to identify machine-generated content via embedding covert information into the contents generated from language models (LMs). However, the robustness of the watermarking schemes has not been well explored. In this paper, we present De-mark, an advanced framework designed to remove n-gram-based watermarks effectively. Our method utilizes a novel querying strategy, termed random selection probing, which aids in assessing the strength of the watermark and identifying the red-green list within the n-gram watermark. Experiments on popular LMs, such as Llama3 and ChatGPT, demonstrate the efficiency and effectiveness of De-mark in watermark removal and exploitation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13808 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | De-mark: Watermark Removal in Large Language Models Chen, Ruibo Wu, Yihan Guo, Junfeng Huang, Heng Computation and Language Watermarking techniques offer a promising way to identify machine-generated content via embedding covert information into the contents generated from language models (LMs). However, the robustness of the watermarking schemes has not been well explored. In this paper, we present De-mark, an advanced framework designed to remove n-gram-based watermarks effectively. Our method utilizes a novel querying strategy, termed random selection probing, which aids in assessing the strength of the watermark and identifying the red-green list within the n-gram watermark. Experiments on popular LMs, such as Llama3 and ChatGPT, demonstrate the efficiency and effectiveness of De-mark in watermark removal and exploitation tasks. |
| title | De-mark: Watermark Removal in Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.13808 |