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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2504.00035 |
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| _version_ | 1866914271546310656 |
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| author | Zhang, Ziwei Wen, Juan Peng, Wanli Wu, Zhengxian Zhou, Yinghan Xue, Yiming |
| author_facet | Zhang, Ziwei Wen, Juan Peng, Wanli Wu, Zhengxian Zhou, Yinghan Xue, Yiming |
| contents | Efficient knowledge injection methods for Large Language Models (LLMs), such as In-Context Learning, knowledge editing, and efficient parameter fine-tuning, significantly enhance model utility on downstream tasks. However, they also pose substantial risks of unauthorized imitation and compromised data provenance for high-value unstructured data assets like creative works. Current copyright protection methods for creative works predominantly focus on visual arts, leaving a critical and unaddressed data engineering challenge in the safeguarding of creative writing. In this paper, we propose WIND (Watermarking via Implicit and Non-disruptive Disentanglement), a novel zero-watermarking, verifiable and implicit scheme that safeguards creative writing databases by providing verifiable copyright protection. Specifically, we decompose creative essence into five key elements, which are extracted utilizing LLMs through a designed instance delimitation mechanism and consolidated into condensed-lists. These lists enable WIND to convert core copyright attributes into verifiable watermarks via implicit encoding within a disentanglement creative space, where 'disentanglement' refers to the separation of creative-specific and creative-irrelevant features. This approach, utilizing implicit encoding, avoids distorting fragile textual content. Extensive experiments demonstrate that WIND effectively verifies creative writing copyright ownership against AI imitation, achieving F1 scores above 98% and maintaining robust performance under stringent low false-positive rates where existing state-of-the-art text watermarking methods struggle. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00035 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Is Your Writing Being Mimicked by AI? Unveiling Imitation with Invisible Watermarks in Creative Writing Zhang, Ziwei Wen, Juan Peng, Wanli Wu, Zhengxian Zhou, Yinghan Xue, Yiming Cryptography and Security Artificial Intelligence Efficient knowledge injection methods for Large Language Models (LLMs), such as In-Context Learning, knowledge editing, and efficient parameter fine-tuning, significantly enhance model utility on downstream tasks. However, they also pose substantial risks of unauthorized imitation and compromised data provenance for high-value unstructured data assets like creative works. Current copyright protection methods for creative works predominantly focus on visual arts, leaving a critical and unaddressed data engineering challenge in the safeguarding of creative writing. In this paper, we propose WIND (Watermarking via Implicit and Non-disruptive Disentanglement), a novel zero-watermarking, verifiable and implicit scheme that safeguards creative writing databases by providing verifiable copyright protection. Specifically, we decompose creative essence into five key elements, which are extracted utilizing LLMs through a designed instance delimitation mechanism and consolidated into condensed-lists. These lists enable WIND to convert core copyright attributes into verifiable watermarks via implicit encoding within a disentanglement creative space, where 'disentanglement' refers to the separation of creative-specific and creative-irrelevant features. This approach, utilizing implicit encoding, avoids distorting fragile textual content. Extensive experiments demonstrate that WIND effectively verifies creative writing copyright ownership against AI imitation, achieving F1 scores above 98% and maintaining robust performance under stringent low false-positive rates where existing state-of-the-art text watermarking methods struggle. |
| title | Is Your Writing Being Mimicked by AI? Unveiling Imitation with Invisible Watermarks in Creative Writing |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2504.00035 |