Meta-Learning for AI-Generated Image Detection

Detectors of AI-generated images are usually trained once and then deployed unchanged.Their accuracy drops on generators that appear after training. We recast detection as a meta-learning problem. The detector conditions every prediction on a small labelled support set from the current deployment context instead of fixing a decision boundary at training time. We give a probabilistic account of how training episodes are built. The standard explicit construction keeps one class for real images and one class per generator. It needs every generator to be known and labelled in advance, as if given by an oracle. It also never pairs a query with support images of another generator, so it does not reward generalisation to a new generator. We propose an implicit construction over the binary real and AI-generated label space. Episode diversity then comes from a prior over the class-conditional distributions rather than from the choice of classes. This prior is stated apart from the data, and we give two instances of it. An empirical prior draws the class-conditional distributions from the domains of the training data. A synthetic prior draws them from Gaussian mixtures in embedding space and needs no images at all. We cast twelve meta-learning methods into this framework. With a nearest-centroid rule, they give thirteen context-aware detectors, which we compare with eleven static detectors. Our implicit construction degrades least when the support set comes from generators other than the query's. In this case, it matches or improves on the oracle explicit construction. Conditioning on the support set matters most on in-the-wild images, where static detectors lose their accuracy. A detector trained on the synthetic prior alone detects these images better than a zero-shot static detector.