Yours or Mine? Overwriting Attacks Against Neural Audio Watermarking

As generative audio models are rapidly evolving, AI-generated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on state-of-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

Authors

• Lingfeng Yao University of Houston
• Chenpei Huang University of Houston
• Shengyao Wang Waseda University
• Junpei Xue Waseda University
• Hanqing Guo Amazon
• Jiang Liu Research Institute of Trustworthy Adaptive Systems, Southern University of Science and Technology Department of Computer Science and Engineering, Southern University of Science and Technology
• Phone Lin National Taiwan University
• Tomoaki Ohtsuki Keio University
• Miao Pan Department of Electrical and Computer Engineering, University of Houston

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