MusicMark: A Robust Generative Watermarking Framework for Music Generation
AI music generation has rapidly advanced alongside commercial platforms, raising the need for reliable watermarking for provenance and attribution. However, existing audio watermarking research has largely focused on speech, and applying speech-oriented methods to music is challenging due to music's complex structure and rich acoustic texture. Most existing methods are post-hoc, adding imperceptible perturbations after generation rather than embedding watermarks as part of the content. This makes them fragile under transformations and especially vulnerable to neural codec re-synthesis, which can discard imperceptible residual signals. Moreover, since generation and watermarking are decoup...
| Authors | Seohwan Yun, Jeeyoung Yun, Yongjin Kim et al. |
|---|---|
| Primary category | cs.SD |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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