Privacy-Utility Balanced Voice De-Identification Using Adversarial
Examples
- URL: http://arxiv.org/abs/2211.05446v1
- Date: Thu, 10 Nov 2022 09:35:58 GMT
- Title: Privacy-Utility Balanced Voice De-Identification Using Adversarial
Examples
- Authors: Meng Chen, Li Lu, Jiadi Yu, Yingying Chen, Zhongjie Ba, Feng Lin, Kui
Ren
- Abstract summary: We propose a voice de-identification system to balance the privacy and utility of voice services.
Our system could achieve 98% and 79% successful de-identification on mainstream ASIs and commercial systems.
- Score: 32.3274243128532
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Faced with the threat of identity leakage during voice data publishing, users
are engaged in a privacy-utility dilemma when enjoying convenient voice
services. Existing studies employ direct modification or text-based
re-synthesis to de-identify users' voices, but resulting in inconsistent
audibility in the presence of human participants. In this paper, we propose a
voice de-identification system, which uses adversarial examples to balance the
privacy and utility of voice services. Instead of typical additive examples
inducing perceivable distortions, we design a novel convolutional adversarial
example that modulates perturbations into real-world room impulse responses.
Benefit from this, our system could preserve user identity from exposure by
Automatic Speaker Identification (ASI) while remaining the voice perceptual
quality for non-intrusive de-identification. Moreover, our system learns a
compact speaker distribution through a conditional variational auto-encoder to
sample diverse target embeddings on demand. Combining diverse target generation
and input-specific perturbation construction, our system enables any-to-any
identify transformation for adaptive de-identification. Experimental results
show that our system could achieve 98% and 79% successful de-identification on
mainstream ASIs and commercial systems with an objective Mel cepstral
distortion of 4.31dB and a subjective mean opinion score of 4.48.
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