Source-Guided Inference-Time Rectification for More Generalizable AI-Generated Image Detection

Rapid advances in AI-generated imagery (AIGI) call for reliable and generalizable detectors capable of identifying increasingly diverse and realistic synthetic imagery. However, existing detectors often suffer substantial performance degradation when confronted with unseen generators, manipulation techniques, or post-processing operations. We revisit this problem from the perspective of internal representations and observe a consistent association between detection failures and test representations deviating from reliable source-domain regions, while such deviations are partially rectifiable at inference time. Motivated by these observations, we propose Source-Guided Subspace Rectification (SGSR), a plug-and-play inference-time representation rectification framework that can be integrated into existing AIGI detectors to improve their cross-domain generalization. SGSR models an observed test representation as a perturbed observation of an underlying reliable representation and rectifies it according to a reliable representation distribution estimated from correctly predicted source-domain samples, whose covariance structure enables direction-dependent rectification. SGSR requires no target statistics, pseudo-labels, cross-sample memory, or parameter updates during inference, while introducing only marginal additional computational and storage overhead. Extensive cross-dataset and cross-generator experiments on both Deepfake and synthetic image detection benchmarks demonstrate consistent improvements, with average AUC gains of 0.80 and 4.08 percentage points across 19 Deepfake detectors and 13 synthetic image detectors, respectively.