Haibo Hu

Recently, speech assistant and speech verification have been used in many fields, which brings much benefit and convenience for us. However, when we enjoy these speech applications, our speech may be collected by attackers for speech synthesis. For example, an attacker generates some inappropriate political opinions with the characteristic of the victim's voice by obtaining a piece of the victim's speech, which will greatly influence the victim's reputation. Specifically, with the appearance of some zero-shot voice conversion methods, the cost of speech synthesis attacks has been further reduced, which also brings greater challenges to user voice security and privacy. Some researchers have proposed the corresponding privacy-preserving methods. However, the existing approaches have some non-negligible drawbacks: low transferability and robustness, high computational overhead. These deficiencies seriously limit the existing method deployed in practical scenarios. Therefore, in this paper, we propose a lightweight, robust, plug-and-play privacy preservation method against speech synthesis attacks in a black-box setting. Our method generates and adds a frequency-domain perturbation to the original speech to achieve privacy protection and high speech quality. Then, we present a data augmentation strategy and noise smoothing mechanism to improve the robustness of the proposed method. Besides, to reduce the user's defense overhead, we also propose a novel identity-wise protection mechanism. It can generate a universal perturbation for one speaker and support privacy preservation for speech of any length. Finally, we conduct extensive experiments on 5 speech synthesis models, 5 speech verification models, 1 speech recognition model, and 2 datasets. The experimental results demonstrate that our method has satisfying privacy-preserving performance, high speech quality, and utility.