# Yours or Mine? Overwriting Attacks Against Neural Audio Watermarking

arXiv: https://arxiv.org/abs/2509.05835 · HTML: https://mlsanity.com/paper/2509.05835/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2025.

## Built on by (most cited first, 5 of 5)

- [Fingerprinting Deep Neural Networks for Ownership Protection: An Analytical Approach](https://mlsanity.com/paper/2603.21411/) (2026)
- [Latent-Mark: An Audio Watermark Robust to Neural Codec Compression](https://mlsanity.com/paper/2603.05310/) (2026)
- [The Watermark Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection](https://mlsanity.com/paper/2606.23335/) (2026)
- [Audio Pirates: Black-box Audio Watermark Removal via Diffusion Priors](https://mlsanity.com/paper/2605.30614/) (2026)
- [AudioNoisePrints: Model-free audio watermarking using spatial correlation in flow matching TTS](https://mlsanity.com/paper/2608.22186/) (2026)

## Abstract (arXiv)

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.