CRAW: Codec Robust Audio Watermarking

• URL: http://arxiv.org/abs/2609.03107v1
• Date: Wed, 02 Sep 2026 19:44:04 GMT
• Title: CRAW: Codec Robust Audio Watermarking
• Abstract summary: CRAW is an audio watermarking framework that improves robustness against neural re-synthesis while maintaining high perceptual quality.<n> Experiments demonstrate that CRAW achieves state-of-the-art robustness against neural codecs, denoisers, and vocoders.
• Score: 9.852158589873419
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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