μFlow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors

Current generative models, including GANs and diffusionmodels, have reached an outstanding level of photorealism, posing sig-nificant risks to privacy and security. To ensure real-world applicability,deepfake detectors must generalise effectively to unseen generators. How-ever, most existing approaches rely on supervised training with both realand fake images, which limits their generalisation especially across gener-ators categories (e.g. GANs vs DMs). In this work, we introduce µFlow ,a one-class deepfake detector trained only on real images without relyingon pseudo-deepfakes or synthetic artifacts. Our approach builds on theobservation that averaging multiple images amplifies consistent genera-tive traces, producing highly discriminative feature representations. Weleverage this property by modelling the distribution of features extractedfrom averaged images and training a normalizing flow to align the featurespace of individual images with this distribution. This alignment yieldsa likelihood-based criterion that separates real and fake samples whilepromoting strong generalisation. We evaluate µFlow on a fully out-of-distribution setting, where both real and fake datasets are unseen duringtraining. Experimental results show that our method significantly out-performs SOTA detectors. Project page: opontorno.github.io/MuFlow.