CRAW: Codec Robust Audio Watermarking

2026-09-02

AI Summary

为解决生成语音难以辨别真伪的问题，提出CRAW框架，通过结合失真感知训练等方法提高水印对神经编解码器的鲁棒性。

Abstract

Recent advances in generative speech models have made it increasingly difficult to distinguish authentic from synthetic audio, enabling new forms of fraud and misinformation. Audio watermarking offers a promising defense by embedding an imperceptible signal into generated speech that can later be detected to verify its provenance. However, recent studies have shown that existing post-hoc watermarking methods fail under neural codecs and denoisers, transformations routinely applied during real-world storage, transmission, and processing, severely limiting their practical utility. Here we introduce CRAW, a codec-robust audio watermarking framework that jointly improves robustness against neural re-synthesis while maintaining high perceptual quality. CRAW combines distortion-aware training with an attention-based pooling mechanism, inference-time perceptual mask- ing, and an error-correcting code to recover the fidelity lost during robust training. Experiments demonstrate that CRAW achieves state-of-the-art robustness against neural codecs, denoisers, and vocoders while maintaining perceptual quality comparable to existing post-hoc watermarking methods. The code is available at https://github.com/DavidC1212/craw.

Problem

• audio watermarking
• neural codecs
• denoisers
• robustness
• perceptual quality

Innovation

• Codec Robust
• Attention-based Pooling
• Perceptual Masking
• Error-correcting Code