Asymmetry-Guided Iterative Adaptation for Generalizable AI-Generated Image Detection

The continual emergence of new generative models challenges AI-generated image detectors whose forensic cues may fail beyond known sources. Empirical analysis indicates that target images initially predicted as synthetic provide useful signals for recognizing missed images from the same source, allowing partial cross-generator generalization to seed same-source adaptation. Based on this insight, we propose an iterative framework that repeatedly adapts with synthetic predictions to expand detection coverage. The framework uses asymmetric supervision: predicted-synthetic target images provide source-specific signals, while trusted real-image replay supplies real supervision without reinforcing missed synthetic images as real and suppresses false positives during iterative expansion. To overcome the limited coverage of initial synthetic predictions on difficult source distributions, we further introduce stochastic exploration, provisionally labeling a small subset of the remaining target images as synthetic while replay constrains noisy updates. Exploration accelerates recall expansion and improves final recall on difficult sources. Extensive experiments across unseen generators and diverse synthesis paradigms demonstrate state-of-the-art performance, outperforming recent methods by a substantial margin.