arXiv (All)
open access
Not All Attacks Are Learned Equally in Speech Deepfake Detection
Speech deepfake detection (SDD) models are trained on multi-attack datasets containing diverse spoofing systems, such as text-to-speech (TTS) and voice conversion (VC). In standard classifier training on multi-attack datasets, all attacks are treated as one spoofed class, and performance is reported
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
- ML-ITW: A Multilingual in-the-wild Benchmark for Speech Deepfake Detection#1014627
- SPADE: A Multilingual Dataset for Speech Partial Deepfake Detection and Localization#1051690
- SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision#1144646
- Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models#822153
- "What I See is What I Hear": Deepfake Detection Across Diverse Hearing Abilities#1076315
Record · ID 999451
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