FADNet reformulates face forgery detection as one-class learning on real faces only, using EDL uncertainty and a PFIG to achieve 96.63% average accuracy and 98.83% precision on DF40 and ASFD benchmarks.
Explain vision focus: Blending human saliency into synthetic face images
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Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection
FADNet reformulates face forgery detection as one-class learning on real faces only, using EDL uncertainty and a PFIG to achieve 96.63% average accuracy and 98.83% precision on DF40 and ASFD benchmarks.