Interpretable and Reliable Detection of AI-Generated Images via Grounded
  Reasoning in MLLMs
Authors: name: Yikun Ji, name: Hong Yan, name: Jun Lan, name: Huijia Zhu, name: Weiqiang Wang, name: Qi Fan, name: Liqing Zhang, name: Jianfu Zhang

The rapid advancement of image generation technologies intensifies the demand
for interpretable and robust detection methods. Although existing approaches
often attain high accuracy, they typically operate as black boxes without
providing human-understandable justifications. Multi-modal Large Language
Models (MLLMs), while not originally intended for forgery detection, exhibit
strong analytical and reasoning capabilities. When properly fine-tuned, they
can effectively identify AI-generated images and offer meaningful explanations.
However, existing MLLMs still struggle with hallucination and often fail to
align their visual interpretations with actual image content and human
reasoning. To bridge this gap, we construct a dataset of AI-generated images
annotated with bounding boxes and descriptive captions that highlight synthesis
artifacts, establishing a foundation for human-aligned visual-textual grounded
reasoning. We then finetune MLLMs through a multi-stage optimization strategy
that progressively balances the objectives of accurate detection, visual
localization, and coherent textual explanation. The resulting model achieves
superior performance in both detecting AI-generated images and localizing
visual flaws, significantly outperforming baseline methods.