arXiv:2502.04230v3 Announce Type: replace-cross Abstract: The rapid proliferation of generative audio synthesis and editing technologies has raised serious concerns about copyright infringement, data provenance, and the spread of misinformation via deepfake audio. Watermarking offers a proactive solution by embedding imperceptible yet identifiable and traceable signals into audio content. While recent neural network-based watermarking methods like WavMark and AudioSeal have improved robustness and quality, they struggle to jointly optimize both robust detection and accurate attribution. This p
The rapid proliferation of generative audio synthesis and deepfake audio necessitates robust watermarking solutions, which current methods struggle to provide effectively.
This development addresses critical concerns around copyright infringement, data provenance, and misinformation in the rapidly evolving landscape of AI-generated audio, which impacts media, law, and national security.
The ability to more effectively embed and detect watermarks, even in the presence of adversarial attacks, offers a new defense mechanism against malicious and unauthorized use of generative audio.
- · Digital content creators
- · Copyright holders
- · Forensic analysis firms
- · AI ethics and safety researchers
- · Deepfake audio perpetrators
- · Unregulated generative audio platforms
Increased trust and security in audio content due to improved provenance tracking and copyright protection.
Potential for new regulatory frameworks and industry standards for generative audio content based on watermarking capabilities.
The development of 'watermark-aware' generative AI that either preserves or actively bypasses such marks, leading to an arms race in audio authenticity.
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