Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models
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 conditioning on synthesis text and reference speech creates a large and underexplored query design space with no established criterion for identifying an effective query. For representation engineering, the multi-level speech characteristics and t...
| Authors | Kunlin Cai, Kaiyuan Zhang, Zihang Xiang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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