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AI Research
arXiv CS.AI·May 25·8 min read
XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
TL;DR
Generative audio tools enable copyright infringement and deepfakes, but existing watermarking methods struggle to simultaneously optimize robust detection and accurate attribution (identifying the watermark source). XAttnMark combines parameter sharing between encoder/decoder components, cross-attention mechanisms for message retrieval, and psychoacoustic-aligned masking loss to achieve state-of-the-art detection and attribution performance against audio transformations.
✦ Why It Matters
Engineers can implement XAttnMark to protect audio content from unauthorized use and verify authenticity in generative AI applications.
Key Takeaways
- Explore XAttnMark for robust audio watermarking solutions.
- Utilize cross-attention mechanisms to improve message retrieval.
- Implement psychoacoustic-aligned masking for better imperceptibility.
- Stay ahead of copyright challenges in generative audio technologies.
How It Works
XAttnMark leverages a cross-attention mechanism that allows the watermark generator and detector to share parameters partially. This design enhances the efficiency of message retrieval while maintaining the quality of the audio.
The temporal conditioning module further optimizes how watermarks are distributed over time, ensuring they remain imperceptible to listeners. The psychoacoustic-aligned time-frequency masking loss is specifically designed to align with human auditory perception, making the watermarks less detectable.