Defending Your Voice: Adversarial Attack on Voice Conversion
This addresses privacy and authentication concerns for individuals whose voices could be misused by voice conversion technologies, representing a novel defensive approach.
The paper tackles the problem of preventing unauthorized voice conversion by introducing imperceptible adversarial noise into a speaker's utterances, which disrupts state-of-the-art voice conversion models from mimicking that speaker's voice, as shown in objective and subjective evaluations.
Substantial improvements have been achieved in recent years in voice conversion, which converts the speaker characteristics of an utterance into those of another speaker without changing the linguistic content of the utterance. Nonetheless, the improved conversion technologies also led to concerns about privacy and authentication. It thus becomes highly desired to be able to prevent one's voice from being improperly utilized with such voice conversion technologies. This is why we report in this paper the first known attempt to perform adversarial attack on voice conversion. We introduce human imperceptible noise into the utterances of a speaker whose voice is to be defended. Given these adversarial examples, voice conversion models cannot convert other utterances so as to sound like being produced by the defended speaker. Preliminary experiments were conducted on two currently state-of-the-art zero-shot voice conversion models. Objective and subjective evaluation results in both white-box and black-box scenarios are reported. It was shown that the speaker characteristics of the converted utterances were made obviously different from those of the defended speaker, while the adversarial examples of the defended speaker are not distinguishable from the authentic utterances.