This is the source code for FakeReasoning: Towards Generalizable Forgery Detection and Reasoning.
Abstract: Accurate and interpretable detection of AI-generated images is essential for mitigating risks associated with AI misuse. However, the substantial domain gap among generative models makes it challenging to develop a generalizable forgery detection model. Moreover, since every pixel in an AI-generated image is synthesized, traditional saliency-based forgery explanation methods are not well suited for this task. To address these challenges, we propose modeling AI-generated image detection and explanation as a Forgery Detection and Reasoning task (FDR-Task), leveraging vision-language models (VLMs) to provide accurate detection through structured and reliable reasoning over forgery attributes. To facilitate this task, we introduce the Multi-Modal Forgery Reasoning dataset (MMFR-Dataset), a large-scale dataset containing 100K images across 10 generative models, with 10 types of forgery reasoning annotations, enabling comprehensive evaluation of FDR-Task. Additionally, we propose FakeReasoning, a forgery detection and reasoning framework with two key components. First, Forgery-Aligned Contrastive Learning enhances VLMs' understanding of forgery-related semantics through both cross-modal and intra-modal contrastive learning between images and forgery attribute reasoning. Second, a Classification Probability Mapper bridges the optimization gap between forgery detection and language modeling by mapping the output logits of VLMs to calibrated binary classification probabilities.
- Apr 15 2025: The Project Page of our paper has been published! Click to find more about performance of FakeReasoning and samples in MMFR-Dataset.
- Mar 27 2025: Our Paper is released on arXiv.
- Release paper
- Release project page
- Write environment setting
- Release checkpoints and inference code
If you find this work useful for your research, please kindly cite our paper:
@article{gao2025fakereasoning,
title={FakeReasoning: Towards Generalizable Forgery Detection and Reasoning},
author={Gao, Yueying and Chang, Dongliang and Yu, Bingyao and Qin, Haotian and Chen, Lei and Liang, Kongming and Ma, Zhanyu},
journal={arXiv preprint arXiv:2503.21210},
year={2025},
url={https://arxiv.org/abs/2503.21210}
}