AudioNoisePrints: Model-free audio watermarking using spatial correlation in flow matching TTS
We present AudioNoisePrints, a training-free watermarking pipeline for flow matching and diffusion TTS models, which requires minimal extra computation during inference and does not require retraining the TTS model or reducing the generation quality. We exploited the fact that there are strong correlations between the initial Gaussian noises and the generated outputs in diffusion and flow matching models, such that a simple cosine correlation between the initial noise and the generated output can be used to perform watermaking. Moreover, we train a lightweight detector on top for more aggressive augmentations. Our method outperforms AudioSeal, a strong baseline for audio watermarking unde...
| Authors | Timothy Tin-Long, Jian Zhu, Aidan Pine et al. |
|---|---|
| Primary category | cs.SD |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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