arXiv:2606.11828v1 Announce Type: cross Abstract: Audio watermarking aims to embed identifiable information into audio while remaining imperceptible. Existing methods adopt high-fidelity, low-energy designs to preserve perceptual quality, but the resulting watermarks lack robustness under suppression by speech reconstruction models. Improving robustness is challenging due to the inherent robustness-fidelity trade-off in existing designs, where increasing watermark energy improves robustness but reduces fidelity. To address this problem, we propose a feature-aligned watermarking method that ali
The proliferation of generative AI models capable of speech reconstruction creates an urgent need for robust watermarking techniques to preserve identifiable information amidst potential manipulation.
This development addresses a critical vulnerability in audio authenticity and intellectual property, enabling verifiable content in an age of synthetic media.
Audio watermarking can now maintain robustness against common AI-driven reconstruction distortions, improving the reliability of embedded information in speech.
- · Content creators
- · Intellectual property owners
- · Forensic analysis firms
- · Audio software developers
- · Malicious actors attempting to remove watermarks
- · Platforms lacking robust attribution technologies
Improved integrity and traceability of audio content in the era of advanced AI manipulation.
Increased trust in digital audio, potentially fostering new applications requiring verifiable sound.
New regulatory frameworks and industry standards may emerge around mandatory watermarking for AI-generated or AI-processed audio.
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Read at arXiv cs.AI