XAttnMark: Learning Robust Audio Watermarking with Cross-Attention

• URL: http://arxiv.org/abs/2502.04230v2
• Date: Fri, 07 Feb 2025 20:11:12 GMT
• Title: XAttnMark: Learning Robust Audio Watermarking with Cross-Attention
• Authors: Yixin Liu, Lie Lu, Jihui Jin, Lichao Sun, Andrea Fanelli,
• Abstract summary: Cross-Attention Robust Audio Watermark (XAttnMark)<n>This paper introduces Cross-Attention Robust Audio Watermark (XAttnMark), which bridges the gap by leveraging partial parameter sharing between the generator and the detector.<n>We propose a psychoacoustic-aligned temporal-frequency masking loss that captures fine-grained auditory masking effects, enhancing watermark imperceptibility.
• Score: 15.216472445154064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid proliferation of generative audio synthesis and editing technologies has raised significant concerns about copyright infringement, data provenance, and the spread of misinformation through deepfake audio. Watermarking offers a proactive solution by embedding imperceptible, identifiable, and traceable marks into audio content. While recent neural network-based watermarking methods like WavMark and AudioSeal have improved robustness and quality, they struggle to achieve both robust detection and accurate attribution simultaneously. This paper introduces Cross-Attention Robust Audio Watermark (XAttnMark), which bridges this gap by leveraging partial parameter sharing between the generator and the detector, a cross-attention mechanism for efficient message retrieval, and a temporal conditioning module for improved message distribution. Additionally, we propose a psychoacoustic-aligned temporal-frequency masking loss that captures fine-grained auditory masking effects, enhancing watermark imperceptibility. Our approach achieves state-of-the-art performance in both detection and attribution, demonstrating superior robustness against a wide range of audio transformations, including challenging generative editing with strong editing strength. The project webpage is available at https://liuyixin-louis.github.io/xattnmark/.

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