他们提出μFlow，只用真实照片训练就能识别各种AI生成的假脸，在完全没见过的生成器上效果碾压现有方法。
μFlow是一种仅用真实图像训练的一类深度伪造检测器，无需依赖伪深度伪造或合成伪影。它通过平均多张图像放大GANs和扩散模型等生成器的一致生成痕迹，并训练归一化流将个体图像的特征空间与该分布对齐。在完全未见过生成器的测试集上，μFlow在F1分数等指标上显著优于当前最先进检测器。实验表明该方法对跨生成器类别（如GANs vs 扩散模型）具有良好的泛化能力。
$μ$Flow: Leveraging Average Images for Improving Generalisation of Deepfake Faces Detectors
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. GANs vs DMs). In this work, we introduce $μ$Flow, a one-class deepfake detector trained only on real images without relying on pseudo-deepfakes or synthetic artifacts. Our approach builds on the observation that averaging multiple images amplifies consistent generative traces, producing highly discriminative feature representations. We leverage this property by modelling the distribution of features extracted from averaged images and training a normalizing flow to align the feature space of individual images with this distribution. This alignment yields a likelihood-based criterion that separates real and fake samples while promoting strong generalisation. We evaluate $μ$Flow on a fully out-of-distribution setting, where both real and fake datasets are unseen during training. Experimental results show that our method significantly outperforms SOTA detectors. Project page: https://opontorno.github.io/MuFlow.