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Research questionHow can video deepfake detectors adapt to new forgery patterns without forgetting prior spatial and temporal cues?New video forgery patterns can expose weaknesses in an existing detector, while updating it may erase knowledge of earlier forgeries. Because videos contain both spatial artifacts and temporal evidence, forgetting either type of cue can reduce detection reliability.
AI
Computer Vision
Image & Video Processing
Machine Learning
Research Paper
Latest papersRecent research connected to this question, newest first.Preserving Knowledge across Space and Time for Continual Video Deepfake DetectionThe source studies continual deepfake video detection across diverse sequential scenarios. It treats spatial, temporal, and spatiotemporal evidence as distinct modalities and reports experiments on adaptation and retention; the supplied evidence does not specify deployment constraints or access requirements.research paper · Sep 3, 2026
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