Mitigating Unauthorized Speech Synthesis for Voice Protection (2410.20742v1)

Abstract: With just a few speech samples, it is possible to perfectly replicate a speaker's voice in recent years, while malicious voice exploitation (e.g., telecom fraud for illegal financial gain) has brought huge hazards in our daily lives. Therefore, it is crucial to protect publicly accessible speech data that contains sensitive information, such as personal voiceprints. Most previous defense methods have focused on spoofing speaker verification systems in timbre similarity but the synthesized deepfake speech is still of high quality. In response to the rising hazards, we devise an effective, transferable, and robust proactive protection technology named Pivotal Objective Perturbation (POP) that applies imperceptible error-minimizing noises on original speech samples to prevent them from being effectively learned for text-to-speech (TTS) synthesis models so that high-quality deepfake speeches cannot be generated. We conduct extensive experiments on state-of-the-art (SOTA) TTS models utilizing objective and subjective metrics to comprehensively evaluate our proposed method. The experimental results demonstrate outstanding effectiveness and transferability across various models. Compared to the speech unclarity score of 21.94% from voice synthesizers trained on samples without protection, POP-protected samples significantly increase it to 127.31%. Moreover, our method shows robustness against noise reduction and data augmentation techniques, thereby greatly reducing potential hazards.

• The paper introduces the POP method that embeds imperceptible perturbations to disrupt deepfake TTS training by targeting the reconstruction loss.
• Experimental results show significant increases in mel-cepstral distortion and word error rate, reducing the synthesized speech's quality and intelligibility.
• The method’s robustness and transferability across diverse TTS models underline its practical application for protecting personal voice data.

An Expert Overview of "Mitigating Unauthorized Speech Synthesis for Voice Protection"

The paper "Mitigating Unauthorized Speech Synthesis for Voice Protection" addresses a crucial concern in the domain of text-to-speech (TTS) synthesis: the potential threats posed by unauthorized imitation of personal speech. The research introduces the Pivotal Objective Perturbation (POP) method, a proactive data protection strategy designed to safeguard voice data from being accurately replicated by deepfake audio synthesis models. Given the increased sophistication of generative TTS models, which are capable of creating highly realistic synthetic speech, this paper innovates by developing a mechanism to render speech data unlearnable and thus unusable for malicious purposes.

Technical Contributions

The authors present several key contributions through their work:

1. Introduction of POP: The POP method offers a mechanism to embed imperceptible perturbations within audio data, aimed at degrading the model's performance when such data is used for training TTS models. Unlike earlier methods focused on deceiving speaker verification systems without affecting the deepfake synthesis quality, POP ensures the synthesis models are unable to generate high-quality outputs from perturbed data.
2. Objective Function Utilization: By targeting the reconstruction loss—a common objective across various TTS models—the method effectively optimizes perturbations to impair the training outcome. This choice showcases an understanding of TTS models' structural commonalities, benefiting from the shared optimization strategies such models employ (e.g., VITS, GlowTTS).
3. Position-Based Perturbation: Implementing fixed-position perturbations allows for efficient and less perceptible noise generation, aligning with windowed generator training (WGT) strategies prevalent in modern TTS models. This consideration ensures the POP method maintains auditory imperceptibility while optimizing computational resources.
4. Robustness and Transferability Evaluation: The paper systematically appraises the robustness of the generated unlearnable examples against perturbation removal and data augmentation techniques. Results indicate the POP method's resilience, showcasing protection effectiveness across different TTS models without prior model-specific customization.

Experimental Insights

From an empirical standpoint, the POP method demonstrates significant efficacy across various state-of-the-art models including MB-iSTFT-VITS and VITS. The experimental results highlight a noteworthy increase in mel-cepstral distortion (MCD) and word error rate (WER) metrics when POP-protected datasets are used in training, thereby underscoring its impact on degrading synthesized audio quality. The researchers also provide user paper results through mean opinion scores (MOS), confirming that POP causes the generated speech to lose perceptual intelligibility and fidelity, thus verifying its protective potential objectively and subjectively.

Additionally, the transferability of the method across models developed on different architectures and objectives attests to its generalized applicability in the TTS ecosystem, suggesting that the perturbations crafted through one model are effective in thwarting others.

Theoretical and Practical Implications

Theoretically, the introduction of POP can influence future TTS model training and deployment strategies by integrating proactive defense mechanisms earlier in the data lifecycle. Practically, this research holds significance for entities concerned with privacy and data security, particularly content creators and organizations whose audio assets could be susceptible to unauthorized replication.

In conclusion, the research provides a viable pathway for mitigating the risks associated with speech synthesis abuse. The POP method not only enhances security against voice cloning but also adds a valuable tool for safeguarding personal voiceprint data in an era of advanced AI-driven speech technologies. The future development of AI defenses will likely incorporate such perturbative strategies, extending beyond TTS to other dimensions of personal data protection.