Detector-Guided Fake Synthesis Enhancement for Generalizable AI-Generated Image Detection

AI-generated image (AIGI) detectors often generalize poorly to unseen generators and image domains because they may exploit shortcut cues specific to the training distribution. Recent data-alignment methods reduce real-fake discrepancies but can introduce new pattern-specific artifacts through pixel-space synthesis. To address this limitation, we propose a feature-space fake synthesis framework that constructs additional fake-labeled training samples by interpolating aligned real and synthetic representations, avoiding additional pixel-level transformations while producing challenging fake features closer to the real distribution. We further introduce detector-guided adversarial optimization, where detector learning and fake-feature synthesis are alternated so that the evolving detector guides the generation of informative hard examples. To enable efficient adversarial optimization and pair-adaptive fake synthesis, we design a lightweight mixing coefficient generator that predicts a bounded interpolation coefficient for each real-fake pair. Additionally, we introduce a coefficient diversity constraint to mitigate generator collapse during adversarial training. Extensive experiments on nine benchmarks demonstrate improved generalization to unseen generators and image domains.