Automated In-the-Wild Data Collection for Continual AI Generated Image Detection

The rapid advancement of generative artificial intelligence has introduced significant challenges for reliable AI-generated image detection. Existing detectors often suffer from performance degradation under distribution shifts and when encountering newly emerging generative models. In this work, we propose a data-centric continual adaptation framework for updating detectors in evolving environments. We introduce an automated, weakly supervised pipeline for constructing in-the-wild datasets through fact-check article retrieval. Combining this with generator-driven data within a continual learning framework enables robust adaptation and mitigates catastrophic forgetting. Extensive experiments on two state-of-the-art detectors, RINE and SPAI, show significant improvements of +9.14% and +8.01% in average accuracy, respectively.