Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models
Black-box membership inference attacks on fine-tuned TTS models can reveal whether specific voice samples were in training data—a direct biometric-privacy concern under GDPR Article 9.
Summary written by editorial AI · Source link below
arXiv:2609.01723v1 Announce Type: new Abstract: Text-to-Speech (TTS) foundation models are increasingly fine-tuned on private datasets to synthesize highly personalized voices, introducing severe privacy risks by exposing both biometric identities and sensitive speech content. Existing black-box membership inference attacks (MIAs) follow a two-stage pipeline of query generation and representation engineering, both of which face unique challenges when adapted to TTS. For query generation, dual c
Editorial Analysis
Any enterprise fine-tuning voice models on employee or customer data faces a demonstrable risk that training-set membership can be inferred, complicating GDPR biometric-data obligations.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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