Less is More: Fewer Tokens and Blocks Make AIGI Detection Faster and More Generalizable

AI-Generated Image (AIGI) Detection is becoming critical as generative models proliferate, yet existing detectors often generalize poorly by relying on narrow, dataset-specific artifacts. Many prior works improve generalization through additions, such as introducing extra artifact cues, pretraining models, training constraints, or test-time operations, which may increase inference cost and model complexity. In this paper, we take the opposite route and study generalization through subtractions and propose AID--, a plug-and-play framework that improves transformer-based AIGI detectors from both feature and parameter perspectives. At the feature level, we introduce high-order dependency ranking to identify and prune redundant or potentially misleading tokens for progressive token reduction; and at the parameter level, we design relative-advantage head supervision and a class-asymmetric exit rule to train depth-specific early-exit heads. AID-- can be seamlessly integrated into transformer-based AIGI detectors to improve both efficiency and out-of-distribution generalizability, which is experimentally validated on six public datasets. For example, AID-- helps the detector Effort achieve up to a 2.2× speedup while improving generalization accuracy by 3.6% on GenImages.