The Geometry of Fakeness: AI-image detection in the open world

AI-generated image detection is commonly formulated as a binary classification problem between real and fake images. We argue that this formulation is fundamentally misaligned with the open-world nature of the task. While AI-generated images originate from a relatively limited family of generators and thus form a compact, learnable distribution, real images arise from diverse cameras, scenes, acquisition pipelines, and post-processing operations, making them too heterogeneous to be faithfully captured by finite training samples. This asymmetry creates a posterior mismatch between the training and deployment distributions, so that even a near-optimal binary classifier can suffer amplified open-world generalization error. We therefore reformulate AI-generated image detection as an out-of-distribution (OOD) detection problem, treating AI-generated images as in-distribution and real images as outliers. Yet OOD learning alone is insufficient, since the learned representation may still rely on unstable generator-specific shortcuts, such as low-level artifacts or compression traces, that fail to transfer across models and domains. Motivated by our theoretical analysis that information bottleneck (IB) can mitigate such bias amplification by filtering out spurious nuisance features, we propose an IB-regularized one-class objective that learns compact representations of AI-generated images while preserving stable generative cues. Experiments on both classical diffusion models and recent generation systems show consistent improvements over strong baselines, including a 12.90% absolute gain in average accuracy over the best prior detector. Our results suggest that AI-generated image detection is better understood as open-world distribution modeling rather than closed-world binary classification.