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Audio Pirates: Black-box Audio Watermark Removal via Diffusion Priors

arXiv Security Archived Jun 01, 2026 ✓ Full text saved

arXiv:2605.30614v1 Announce Type: new Abstract: With the rise of AI-generated audio, watermarking has become widely used for detecting misuse and protecting intellectual property. However, adversaries may try to remove these watermarks, making it critical to evaluate how well watermarking schemes withstand removal attacks. Existing attacks are often impractical: they either noticeably degrade perceptual quality or require access to the watermarking scheme. We propose DiffErase, a black-box water

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    Computer Science > Cryptography and Security [Submitted on 28 May 2026] Audio Pirates: Black-box Audio Watermark Removal via Diffusion Priors Lingfeng Yao, Xincong Zhong, Chenpei Huang, Xuandong Zhao, Hanqing Guo, Aohan Li, Jiang Liu, Tomoaki Ohtsuki, Miao Pan With the rise of AI-generated audio, watermarking has become widely used for detecting misuse and protecting intellectual property. However, adversaries may try to remove these watermarks, making it critical to evaluate how well watermarking schemes withstand removal attacks. Existing attacks are often impractical: they either noticeably degrade perceptual quality or require access to the watermarking scheme. We propose DiffErase, a black-box watermark removal attack that assumes no knowledge of the target watermarking scheme while maintaining perceptual quality. DiffErase perturbs watermarked audio to an intermediate diffusion noise level and regenerates it using a pretrained denoising model, effectively suppressing watermark signals. Theoretical analysis and extensive experiments demonstrate that inaudible audio watermarks are highly vulnerable: across multiple audio domains, DiffErase consistently removes watermarks while preserving perceptual quality. These findings highlight the need for future audio watermarking designs to consider diffusion-based threats. Code and demos are available at this https URL. Subjects: Cryptography and Security (cs.CR); Sound (cs.SD) Cite as: arXiv:2605.30614 [cs.CR]   (or arXiv:2605.30614v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.30614 Focus to learn more Submission history From: Lingfeng Yao [view email] [v1] Thu, 28 May 2026 22:07:32 UTC (2,414 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.SD References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 01, 2026
    Archived
    Jun 01, 2026
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