Test-Time Curriculum for Open-Set AIGC Detection

AI-generated image detectors deployed in open-world environments inevitably face distribution shifts as new and stronger generative models continue to emerge. Although existing methods improve cross-generator generalization through better representations or training data construction, they typically follow a static train-once-and-deploy paradigm and cannot adapt after deployment. In this work, we study open-set AIGC image detection from a test-time adaptation perspective, where pretrained detectors adapt to unlabeled target data from unseen generators. We propose Test-Time Curriculum (TTC), a simple and model-agnostic framework that performs curriculum-based self-training for open-set AIGC detection. TTC separates pseudo-label reliability from adaptation difficulty by starting from reliable balanced samples and progressively incorporating harder informative cases under generator shift. We further introduce Cross-Scale Pseudo-Label Refinement to improve pseudo-label reliability by aggregating complementary evidence across multiple resolutions. In addition, we construct AIGCGuard, a benchmark containing 3,100 representative real images and 124,000 generated images from 40 of the most advanced open-source and proprietary text-to-image models. Extensive experiments on five benchmarks demonstrate that TTC substantially improves detection performance under diverse unseen-generator shifts.