Yours or Mine? Overwriting Attacks Against Neural Audio Watermarking
Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Journal Volume
40
Journal Issue
33
Start Page
27756
End Page
27763
ISSN
2374-3468
2159-5399
ISBN (of the container)
978-157735906-7
Date Issued
2026-03-14
Author(s)
Yao, Lingfeng
Huang, Chenpei
Wang, Shengyao
Xue, Junpei
Guo, Hanqing
Liu, Jiang
Ohtsuki, Tomoaki
Pan, Miao
Abstract
As generative audio models are rapidly evolving, AIgenerated 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 stateof-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.
Event(s)
40th AAAI Conference on Artificial Intelligence, AAAI 2026
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Type
conference paper