E2E-VGuard: Adversarial Prevention for Production LLM-based End-To-End Speech Synthesis
Abstract
Recent advancements in speech synthesis technology have enriched our daily lives, with high-quality and human-like audio widely adopted across real-world applications. However, malicious exploitation like voice-cloning fraud poses severe security risks. Existing defense techniques struggle to address the production large language model (LLM)-based speech synthesis. While previous studies have considered the protection for fine-tuning synthesizers, they assume manually annotated transcripts. Given the labor intensity of manual annotation, end-to-end (E2E) systems leveraging automatic speech recognition (ASR) to generate transcripts are becoming increasingly prevalent, e.g., voice cloning via commercial APIs. Therefore, this E2E speech synthesis also requires new security mechanisms. To tackle these challenges, we propose E2E-VGuard, a proactive defense framework for two emerging threats: (1) production LLM-based speech synthesis, and (2) the novel attack arising from ASR-driven E2E scenarios. Specifically, we employ the encoder ensemble with a feature extractor to protect timbre, while ASR-targeted adversarial examples disrupt pronunciation. Moreover, we incorporate the psychoacoustic model to ensure perturbative imperceptibility. For a comprehensive evaluation, we test 16 open-source synthesizers and 3 commercial APIs across Chinese and English datasets, confirming E2E-VGuard's effectiveness in timbre and pronunciation protection. Real-world deployment validation is also conducted. Our code and demo page are available at https://wxzyd123.github.io/e2e-vguard/.
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 E2E-VGuard, a novel framework that proactively defends against adversarial attacks in production LLM-based speech synthesis, addressing both timbre and pronunciation vulnerabilities. This work is a substantial step forward in securing speech synthesis technologies, with implications for both academic research and practical applications in the field.
Methodology
The methodology presented in E2E-VGuard is innovative, utilizing an encoder ensemble with a feature extractor to enhance the protection of timbre in speech synthesis. The incorporation of a psychoacoustic model to ensure perturbative imperceptibility is a significant advancement, addressing the challenge of maintaining audio quality while implementing security measures. The approach to countering ASR-targeted adversarial examples is particularly noteworthy, as it reflects a deep understanding of the vulnerabilities in current systems.
Results & evaluation
The experimental evaluation is robust, involving 16 open-source synthesizers and 3 commercial APIs across diverse datasets in both Chinese and English. This breadth of testing enhances the credibility of the findings. The results demonstrate E2E-VGuard's effectiveness in protecting against both timbre and pronunciation attacks, which is critical for real-world applications. However, the paper could benefit from a more detailed discussion of the metrics used to evaluate effectiveness.
Reproducibility
The authors provide a demo page and mention that the code is available, which supports reproducibility. However, the paper could improve by including more detailed implementation instructions and specific configurations used during experiments to facilitate independent verification of results.
Limitations
One limitation is the potential dependency on the quality of the ASR systems used, as variations in ASR performance could impact the effectiveness of the proposed defenses. Additionally, the paper does not extensively discuss the scalability of the proposed solution in real-world scenarios or its performance against more sophisticated adversarial attacks.
Broader impact
The implications of this research are significant, as it addresses critical security concerns in speech synthesis technology, which is increasingly used in various applications, including virtual assistants and automated customer service. By providing a proactive defense framework, E2E-VGuard has the potential to enhance trust in voice-based systems and mitigate risks associated with voice-cloning fraud. The main contribution of this paper is the introduction of E2E-VGuard, a novel framework that proactively defends against adversarial attacks in production LLM-based speech synthesis, addressing both timbre and pronunciation vulnerabilities. This work is a substantial step forward in securing speech synthesis technologies, with implications for both academic research and practical applications in the field.
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