arXiv (All)
open access
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-st
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models#632797
- Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models#160720
- Black-Box Membership Inference via Word-Level Probability Estimation#997613
- "What I See is What I Hear": Deepfake Detection Across Diverse Hearing Abilities#1076315
- CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking#1122405
Record · ID 822153
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive