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New attack reveals severe privacy risks in fine-tuned TTS models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new black-box membership inference attack (MIA) framework specifically designed for fine-tuned Text-to-Speech (TTS) models. This framework addresses challenges in query generation and representation engineering inherent to TTS systems. Evaluations on three state-of-the-art TTS models demonstrated significant privacy leakage, with speaker-level AUC scores reaching up to 1.0 and record-level AUC scores between 0.80 and 0.90, even in difficult scenarios. AI

IMPACT Highlights significant privacy vulnerabilities in personalized voice synthesis, potentially impacting user trust and data security practices in TTS development.

RANK_REASON The cluster contains a research paper detailing a new attack methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New attack reveals severe privacy risks in fine-tuned TTS models

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The cluster contains a research paper detailing a new attack methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kunlin Cai, Kaiyuan Zhang, Zihang Xiang, Jinghuai Zhang, Abeer Alwan, Fnu Suya, Yuan Tian ·

    Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models

    arXiv:2609.01723v1 Announce Type: cross 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. …

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