WAKE: Watermarking Audio with Key Enrichment
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912417280163840 |
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| author | Xu, Yaoxun Yu, Jianwei Chen, Hangting Wu, Zhiyong Wu, Xixin Yu, Dong Gu, Rongzhi Luo, Yi |
| author_facet | Xu, Yaoxun Yu, Jianwei Chen, Hangting Wu, Zhiyong Wu, Xixin Yu, Dong Gu, Rongzhi Luo, Yi |
| contents | As deep learning advances in audio generation, challenges in audio security and copyright protection highlight the need for robust audio watermarking. Recent neural network-based methods have made progress but still face three main issues: preventing unauthorized access, decoding initial watermarks after multiple embeddings, and embedding varying lengths of watermarks. To address these issues, we propose WAKE, the first key-controllable audio watermark framework. WAKE embeds watermarks using specific keys and recovers them with corresponding keys, enhancing security by making incorrect key decoding impossible. It also resolves the overwriting issue by allowing watermark decoding after multiple embeddings and supports variable-length watermark insertion. WAKE outperforms existing models in both watermarked audio quality and watermark detection accuracy. Code, more results, and demo page: https://thuhcsi.github.io/WAKE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05891 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WAKE: Watermarking Audio with Key Enrichment Xu, Yaoxun Yu, Jianwei Chen, Hangting Wu, Zhiyong Wu, Xixin Yu, Dong Gu, Rongzhi Luo, Yi Sound Audio and Speech Processing As deep learning advances in audio generation, challenges in audio security and copyright protection highlight the need for robust audio watermarking. Recent neural network-based methods have made progress but still face three main issues: preventing unauthorized access, decoding initial watermarks after multiple embeddings, and embedding varying lengths of watermarks. To address these issues, we propose WAKE, the first key-controllable audio watermark framework. WAKE embeds watermarks using specific keys and recovers them with corresponding keys, enhancing security by making incorrect key decoding impossible. It also resolves the overwriting issue by allowing watermark decoding after multiple embeddings and supports variable-length watermark insertion. WAKE outperforms existing models in both watermarked audio quality and watermark detection accuracy. Code, more results, and demo page: https://thuhcsi.github.io/WAKE. |
| title | WAKE: Watermarking Audio with Key Enrichment |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.05891 |