A non-intrusive and adaptive speaker de-identification scheme using adversarial examples

Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma while enjoying convenient voice services. Existing studies employ direct modification or text-based re-synthesis to de-identify users' voices, but resulting in inconsistent audibility for human participants and not adaptive to informed attacks. In this poster, we propose a non-intrusive and adaptive speaker de-identification scheme to balance the privacy and utility of voice services. We generate adversarial examples to conceal user identity from exposure by Automatic Speaker Identification (ASI). By learning a compact distribution with a conditional variational auto-encoder, our system enables on-demand target sampling and diverse identity transformation. We also introduce the acoustic masking effect to construct inaudible perturbations, thus preserving the speech content and perceptual quality. Experiments on 50 speakers show our system could achieve 98.2% successful de-identification on 4 mainstream ASIs with an objective perceptual quality of 4.38 and a subjective mean opinion score of 4.56.