AudioNoisePrints: Model-free audio watermarking using spatial correlation in flow matching TTS

2026-08-22

AI Summary

提出了一种无需训练的音频水印方法AudioNoisePrints，利用扩散和流匹配模型中初始噪声与输出间的空间相关性进行水印嵌入，适用于多种TTS及声码器模型。

Abstract

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 under strong augmentations. We experimented on F5TTS and other TTS and vocoder models, and concluded that they all exhibit similar spatial correlation properties, suggesting our watermarking scheme can be used for more flow-matching TTS models and even vocoders in the future.

Problem

• AudioNoisePrints
• watermarking
• flow matching TTS
• diffusion models
• spatial correlation

Innovation

• model-free watermarking
• spatial correlation
• flow matching TTS
• diffusion models
• lightweight detector