$\mu$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors

Excerpt

arXiv:2606.30528v2 Announce Type: replace-cross Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security. To ensure real-world applicability, deepfake detectors must generalise effectively to unseen generators. However, most existing approaches rely on supervised training with both real and fake images, which limits their generalisation especially across generators categories (e.g