SceneGuard: Training-Time Voice Protection with Scene-Consistent Audible Background Noise
Abstract
Voice cloning technology poses significant privacy threats by enabling unauthorized speech synthesis from limited audio samples. Existing defenses based on imperceptible adversarial perturbations are vulnerable to common audio preprocessing such as denoising and compression. We propose SceneGuard, a training-time voice protection method that applies scene-consistent audible background noise to speech recordings. Unlike imperceptible perturbations, SceneGuard leverages naturally occurring acoustic scenes (e.g., airport, street, park) to create protective noise that is contextually appropriate and robust to countermeasures. We evaluate SceneGuard on text-to-speech training attacks, demonstrating 5.5% speaker similarity degradation with extremely high statistical significance (p < 10^{-15}, Cohen's d = 2.18) while preserving 98.6% speech intelligibility (STOI = 0.986). Robustness evaluation shows that SceneGuard maintains or enhances protection under five common countermeasures including MP3 compression, spectral subtraction, lowpass filtering, and downsampling. Our results suggest that audible, scene-consistent noise provides a more robust alternative to imperceptible perturbations for training-time voice protection. The source code are available at: https://github.com/richael-sang/SceneGuard.
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
The main contribution of this paper is the introduction of SceneGuard, a training-time voice protection method that utilizes scene-consistent audible background noise to enhance the robustness of defenses against voice cloning attacks. This innovative approach not only addresses existing vulnerabilities in audio preprocessing but also demonstrates significant potential for real-world applications in safeguarding personal audio data.
Methodology
The methodology presented in SceneGuard is innovative as it introduces a novel approach to voice protection by utilizing scene-consistent audible background noise. This contrasts with traditional methods that rely on imperceptible adversarial perturbations, which have shown vulnerabilities to common audio preprocessing techniques. The use of contextually appropriate noise enhances the robustness of the protection mechanism. The paper provides a clear description of how the background noise is generated and integrated into the speech recordings, which is a significant contribution to the field of audio security.
Results & evaluation
The experimental setup is thorough, evaluating the effectiveness of SceneGuard against various text-to-speech training attacks. The reported results, including a 5.5% degradation in speaker similarity and high speech intelligibility, are statistically significant and demonstrate the method's effectiveness. The robustness tests against common countermeasures, such as MP3 compression and spectral subtraction, further validate the method's practical applicability. However, the paper could benefit from a broader range of datasets to enhance generalizability.
Reproducibility
The paper includes a link to the source code, which is essential for reproducibility. However, the details regarding the experimental setup and specific parameters used in the experiments could be more explicitly stated to facilitate replication by other researchers.
Limitations
One limitation is the potential for the effectiveness of SceneGuard to vary across different languages or dialects, which is not addressed in the paper. Additionally, the reliance on specific acoustic scenes may limit the applicability of the method in less controlled environments.
Broader impact
The implications of this research are significant, as it addresses a critical privacy concern in voice cloning technology. By providing a more robust defense mechanism, SceneGuard could enhance user privacy in various applications, including voice assistants and automated customer service systems. The approach also opens avenues for further research into audio security and privacy protection. The main contribution of this paper is the introduction of SceneGuard, a training-time voice protection method that utilizes scene-consistent audible background noise to enhance the robustness of defenses against voice cloning attacks. This innovative approach not only addresses existing vulnerabilities in audio preprocessing but also demonstrates significant potential for real-world applications in safeguarding personal audio data.
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