Defending Your Voice: Adversarial Attack on Voice Conversion

TL;DR

This paper introduces the first adversarial attack method on voice conversion systems, using imperceptible noise to prevent voice impersonation, with promising results on state-of-the-art models.

Contribution

It presents a novel adversarial attack approach on voice conversion, enhancing privacy protection by making converted voices distinguishably different.

Findings

Adversarial noise effectively disrupts voice conversion.

Converted utterances differ significantly from the defended speaker.

Adversarial examples are indistinguishable from authentic speech.

Abstract

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.