A parallel adapter with boundary and contrastive losses turns frozen CLIP into the top generalizing face forgery detector on FF++-trained cross-dataset benchmarks, and a text-prompt variant adds about 1.3% average AUC.
Denoising diffusion probabilistic models,
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Forensics Adapter: Unleashing CLIP for Generalizable Face Forgery Detection
A parallel adapter with boundary and contrastive losses turns frozen CLIP into the top generalizing face forgery detector on FF++-trained cross-dataset benchmarks, and a text-prompt variant adds about 1.3% average AUC.