arXiv (All)
open access
Physics-Aware Deepfake Detection via Distance-Speech Consistency
Recent progress in generative models has made talking-head deepfakes highly realistic and easy to create, jeopardizing public trust. However, most audio-visual deepfake detectors rely on lip-speech synchronization and are designed for static, frontal videos, limiting their reliability in dynamic, in
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
- Language Orthogonalization for Zero-Shot Cross-Lingual Audio Deepfake Detection#1122503
- PIVOT: Physics-Grounded Verification for AI-Generated Audio-Video Detection#1109554
- Deepfake News Detection: A Multimodal Framework Integrating LipNet, DeepSpeech and ResNET for Enhanced Audio-Visual Analysis#394335
- BabelFake: A Multilingual Audio-Visual DeepFake Benchmark#1202758
- An Initial Study of Physiological Markers for Deepfake Speech Detection#1344757
Record · ID 1343990
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