EviCal: Learning and Calibrating Evidence for Generalizable AI-Generated Image Detection

AI-generated image detectors can perform well within a benchmark yet degrade when generators and real-image sources change. Existing detectors may learn artifacts specific to their training generators, while their decision rule may mistake normal variation in unfamiliar real images for synthetic artifacts. We propose EviCal to address both problems by learning complementary detection cues across generators and calibrating their scores using real images as the reference. EviCal uses multiple lightweight experts and measures each expert’s usefulness by how much its contribution reduces the combined detector’s classification loss. This measure guides two training objectives: encouraging each expert to contribute on more than one source generator, and encouraging different experts to improve predictions on different images rather than duplicate one another. After training, EviCal calibrates global and local detection scores using held-out real images from the training sources. It learns how local scores normally relate to global responses on real images, then discounts local scores that are high but consistent with this relationship. Unexpected local deviations can therefore contribute to detection without treating every strong local response as suspicious. Trained on four GenImage generators, EviCal achieves 91.46% accuracy on Chameleon and 95.74% average accuracy on AIGI-Now Pixel, exceeding the strongest evaluated baselines by 12.77% and 3.47%, respectively, while retaining 98.66% average accuracy on GenImage. Cumulative ablations show additional cross-dataset gains from evidence regularization and real-image calibration. The resulting detector operates on individual images without generator identities or test-time adaptation.