Introduces the LDD task, ListenForge dataset built from five listening head generation methods, and MANet model that detects listening forgeries via motion inconsistencies guided by audio semantics.
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cs.CV 2years
2026 2representative citing papers
A training-free dual-system framework refines anomaly score ordering on uncertain samples from self-supervised talking head forgery detectors to improve detection performance.
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Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis
Introduces the LDD task, ListenForge dataset built from five listening head generation methods, and MANet model that detects listening forgeries via motion inconsistencies guided by audio semantics.
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Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework
A training-free dual-system framework refines anomaly score ordering on uncertain samples from self-supervised talking head forgery detectors to improve detection performance.