Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection
The paper proposes FADNet, a one-class learning method for face authenticity detection that trains exclusively on real faces. It uses an evidential layer based on Dirichlet distributions to model uncertainty, distinguishing authentic images from forged ones. This approach yields superior performance and calibrated uncertainty scores, making it reliable for forensic applications.