Self Voice Conversion as an Attack against Neural Audio Watermarking
Abstract
Audio watermarking embeds auxiliary information into speech while maintaining speaker identity, linguistic content, and perceptual quality. Although recent advances in neural and digital signal processing-based watermarking methods have improved imperceptibility and embedding capacity, robustness is still primarily assessed against conventional distortions such as compression, additive noise, and resampling. However, the rise of deep learning-based attacks introduces novel and significant threats to watermark security. In this work, we investigate self voice conversion as a universal, content-preserving attack against audio watermarking systems. Self voice conversion remaps a speaker's voice to the same identity while altering acoustic characteristics through a voice conversion model. We demonstrate that this attack severely degrades the reliability of state-of-the-art watermarking approaches and highlight its implications for the security of modern audio watermarking techniques.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a significant advancement in understanding the vulnerabilities of audio watermarking systems against modern voice conversion techniques. The research effectively combines theoretical insights with practical evaluations, making it a valuable contribution to the fields of audio processing and security.
Methodology
The paper introduces a novel attack method, self voice conversion (VC), which effectively preserves speaker identity and linguistic content while degrading the performance of audio watermarking systems. The methodology is well-structured, detailing the attack framework and the specific voice conversion models employed (kNN-VC and RVC). The authors provide a clear rationale for the choice of methods and their relevance to the threat model, demonstrating a deep understanding of both the watermarking and voice conversion domains. However, the methodology could benefit from more extensive comparisons with other potential attack strategies to further validate its effectiveness.
Results & evaluation
The experimental evaluation is robust, employing a variety of watermarking systems and assessing their performance under the proposed self VC attack. The results are clearly presented in tables, showing the degradation of watermark extraction accuracy across different methods. The use of standard datasets (LibriTTS) and metrics (WER, UTMOS) adds credibility to the findings. However, the paper could improve by including more detailed statistical analyses to support the significance of the results.
Reproducibility
The paper lacks specific implementation details or access to code and models, which limits reproducibility. While the authors mention that source code and model checkpoints will not be publicly released due to potential security implications, providing at least some implementation details or pseudo-code would enhance reproducibility and allow other researchers to validate the findings.
Limitations
One limitation is the lack of real-world testing scenarios; the experiments are conducted under controlled conditions that may not fully capture the complexities of real-world audio processing and watermarking. Additionally, the reliance on specific voice conversion models may limit the generalizability of the findings to other models or methods not considered in the study.
Broader impact
The implications of this research are significant, as it highlights vulnerabilities in current audio watermarking techniques in the face of advanced voice conversion technologies. This work could inform the development of more robust watermarking methods and raise awareness about the potential misuse of voice conversion technologies in various applications, including copyright infringement and misinformation. This paper presents a significant advancement in understanding the vulnerabilities of audio watermarking systems against modern voice conversion techniques. The research effectively combines theoretical insights with practical evaluations, making it a valuable contribution to the fields of audio processing and security.
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