Explainable AI-Generated Image Detection with Multimodal Large Language Models

Detecting AI-generated images has become increasingly challenging as modern generative models produce highly realistic content. While existing detectors achieve reasonable classification accuracy, they often suffer from limited interpretability and poor generalization to unseen generators, especially in real-world scenarios. Recently, multimodal large language models (MLLMs) have shown promise for explainable AI-generated image detection; however, their effectiveness is constrained by the lack of large-scale, high-quality explanatory datasets. In this work, we introduce Fake-Reason, a large-scale and high-quality dataset for AI-generated image detection with 80K grounded samples provide fine-grained artifact explanations and precise bounding boxes. Unlike datasets dominated by recent generators, Fake-Reason deliberately spans early GANs and diffusion models as well as modern generators. Based on Fake-Reason, we fine-tune a lightweight open-source MLLM and identify an off-region visual shortcut in standard supervised training: the model can rely on unrelated image regions to infer the global label and then fabricate a plausible explanation for the target region. To alleviate this problem, we introduce region-level attention supervision that aligns the attention of each explanation with its annotated bounding box. Extensive generalization experiments and real-world evaluations demonstrate that our resulting detector, RAFE, achieves state-of-the-art detection performance while providing accurate localization and human-verifiable explanations. We believe that Fake-Reason and the proposed training strategy offer a strong foundation for advancing explainable and robust AI-generated image detection.