Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection

ArXi:2605.10040v1 Announce Type: new The rapid evolution of generative paradigms has enabled the creation of highly realistic imagery, which escalating the risks of identity fraud and the dissemination of disinformation. Most existing approaches frame face forgery detection as a fully supervised binary classification problem. Consequently, these models typically exhibit significant performance decay when tasked with detecting forgeries from previously unseen generative paradigms.