Interpretable and Reliable Detection of AI-Generated Images via Grounded
  Reasoning in MLLMs
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.
68494f9542e4f9106973f4c7
Yikun Ji
admin_assigned
2025-08-04T18:29:50.390Z
67583d89cc09297033cbeff7
https://cdn-avatars.huggingface.co/v1/production/uploads/67583d89cc09297033cbeff7/o9SnMO5kN-0cyDX_5rIgL.png
Kanade
user
KanadeK
68494f9542e4f9106973f4c8
Hong Yan
68494f9542e4f9106973f4c9
Jun Lan
68494f9542e4f9106973f4ca
Huijia Zhu
68494f9542e4f9106973f4cb
Weiqiang Wang
68494f9542e4f9106973f4cc
Qi Fan
claimed_verified
2025-06-11T16:02:12.458Z
64b8a72952b7353d8c669086
https://cdn-avatars.huggingface.co/v1/production/uploads/64b8a72952b7353d8c669086/3PUTNmx9kd17gtZ9-Yviw.jpeg
Qi Fan
user
fanqiNO1
68494f9542e4f9106973f4cd
Liqing Zhang
68494f9542e4f9106973f4ce
Jianfu Zhang
Interpretable and Reliable Detection of AI-Generated Images via Grounded
  Reasoning in MLLMs
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.
A dataset with annotations aids in fine-tuning MLLMs for accurate detection and localization of AI-generated images with meaningful explanations.
Multi-modal Large Language Models MLLMs forgery detection AI-generated images bounding boxes descriptive captions synthesis artifacts human-aligned visual-textual grounded reasoning multi-stage optimization strategy visual localization coherent textual explanation