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" perturbations to prevent unauthorized speech synthesis. By optimizing and applying noises to the original audio, the processed speech sample will still sound like the original speaker to humans; when it is used for speech synthesis, the resulting synthetic speech would resemble others' voices. Consequently, the generated DeepFake audio is less likely to deceive humans or machines for nefarious purposes. 

On the one hand, speech synthesis has great potential to improve our lives...

However, it can also be used for nefarious purposes. It is reported that such tools are already used to conduct financial scams, spread misinformation, bypass speech-based authentication systems, or gain commercially by infringing on voice actors' rights☹️

Some examples that show the astonishing performance of contemporary synthesizers are as follows. 

Can we proactively protect our voice by minimally changing the voice pieces available to attackers?

Librispeech Speakers

Human Testers