AntiFake: Using Adversarial Audio to Prevent Unauthorized Speech Synthesis

The rapid development of deep neural networks and generative AI has catalyzed growth in realistic speech synthesis. While this technology has great potential to improve lives, it also leads to the emergence of “DeepFake” where synthesized speech can be misused to deceive humans and machines for nefarious purposes. In response to this evolving threat, there has been a significant amount of interest in mitigating this threat by DeepFake detection. Complementary to the existing work, we propose to take the preventative approach and introduce AntiFake, a defense mechanism that relies on adversarial examples to prevent unauthorized speech synthesis. To ensure the transferability to attackers' unknown synthesis models, an ensemble learning approach is adopted to improve the generalizability of the optimization process. To validate the efficacy of the proposed system, we evaluated AntiFake against five state-of-the-art synthesizers using real-world DeepFake speech samples. The experiments indicated that AntiFake achieved over 95% protection rate even to unknown black-box models. We have also conducted usability tests involving 24 human participants to ensure the solution is accessible to diverse populations.