Rethinking Shortcuts in AI-Generated Image Detection

We identify a computational shortcut in AI-generated image detection: linear classifiers on the frozen visual encoders of two trained multimodal large language model (MLLM) detectors recover much of their accuracy without language decoding. Further analysis reveals that these detectors are sensitive to input resolution, suggesting potential dataset shortcuts. For example, matching fake-image aspect ratios to the real-image distribution lowers Deep-VRM's AIGI-Now Semantic accuracy from 93.8% to 63.8%. This sensitivity motivates a systematic metadata-only audit of 12 datasets and training resources, revealing label-correlated differences in file format, encoding, and image geometry. Guided by these findings, we propose an aligned visual learning framework based on image editing. Pairing real photographs with complete text-guided editing outputs under consistent preprocessing suppresses dataset shortcuts and enables joint learning of pixel-level and semantic forgery features in a single visual representation space. Our DINOv3-L detector achieves 91.3% mean accuracy across 12 in-the-wild domains and 92.4% on AIGI-Now, retaining over 90% on both subsets after resolution alignment. It also transfers to full-image synthesis and local manipulation on DailyBench.