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arXiv · 2601.20432

Self Voice Conversion as an Attack against Neural Audio Watermarking

Yigitcan Özer·Wanying Ge·Zhe Zhang·Xin Wang·Junichi Yamagishi
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Abstract

Audio watermarking embeds auxiliary information into speech while maintaining speaker identity, linguistic content, and perceptual quality. Although recent advances in neural and digital signal processing-based watermarking methods have improved imperceptibility and embedding capacity, robustness is still primarily assessed against conventional distortions such as compression, additive noise, and resampling. However, the rise of deep learning-based attacks introduces novel and significant threats to watermark security. In this work, we investigate self voice conversion as a universal, content-preserving attack against audio watermarking systems. Self voice conversion remaps a speaker's voice to the same identity while altering acoustic characteristics through a voice conversion model. We demonstrate that this attack severely degrades the reliability of state-of-the-art watermarking approaches and highlight its implications for the security of modern audio watermarking techniques.

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Yigitcan Özer, Wanying Ge, Zhe Zhang, Xin Wang, Junichi Yamagishi. 2026-01-28. Self Voice Conversion as an Attack against Neural Audio Watermarking. https://arxiv.org/abs/2601.20432

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