HorizonTEL/AIGIBench

Is Artificial Intelligence Generated Image Detection a Solved Problem?

Ziqiang Li¹, Jiazhen Yan¹, Ziwen He¹, Kai Zeng², Weiwei Jiang¹, Lizhi Xiong¹, Zhangjie Fu^1‡

^‡Corresponding author

¹Nanjing University of Information Science and Technology ²University of Siena

Paper | GitHub Repository

This repository is the official dataset of the AIGIBench.

AIGIBench dataset contains two types of training and 25 test subsets. This dataset has the following advantages:

• Comprehensive generate types: including GAN-based Noise-to-Image Generation, Diffusion for Text-to-Image Generation, GANs for Deepfake, Diffusion for Personalized Generation, and Open-source Platforms.
• State-of-the-art Generators: MidjourneyV6, Stable Diffusion 3, Imagen, DALLE3, InstantID, FaceSwap, StyleGAN-XL and so on.
• Completely unknown generation method: Crawl pictures from communities and social media to build datasets CommunityAI & SocialRF, making detection more challenging.

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📚Dataset

Each folder contains compressed files. After unzip the file, files under the data root directory can be organized as follows.

Train

AIGIBench introduces two training dataset settings: (i) Setting-I: Training on 144K images generated by ProGAN across four object categories—car, cat, chair, and horse. (ii) Setting-II: Training on 144K images generated by both SD-v1.4 and ProGAN, covering the same four object categories. The data of ProGAN comes from ForenSynths, and the data of sdv1.4 comes from GenImage. In order to maintain the fairness of the training data, we randomly select the sdv1.4 training images of GenImage to keep the same number as ProGAN, and then merge the data. The file directory is as follows:

├── train
│   ├── car
│   │   ├── 0_real
│   │   ├── 1_fake
│   ├── cat
│   │   ├── ...
│   ├── chair
│   │   ├── ...
│   ├── horse
│   │   ├── ...
│   ├── sdv1.4
│   │   ├── 0_real
│   │   ├── 1_fake
├── val
│   ├── ...
│   │   ├── 0_real
│   │   ├── 1_fake
│   │   ...

Test

AIGIBench comprehensively tests the performance of the detector and builds a test dataset from five perspectives: GAN-based Noise-to-Image Generation, Diffusion for Text-to-Image Generation, GANs for Deepfake, Diffusion for Personalized Generation, and Open-source Platforms. The file directory is as follows:

├── test
│   ├── ProGAN
│   │   ├── 0_real
│   │   ├── 1_fake
│   ├── R3GAN
│   │   ├── ...
│   │   ...
│   ├── BlendFace
│   │   ├── 0_real
│   │   ├── 1_fake
│   ├── InSwap
│   │   ├── ...
│   │   ...
│   ├── FLUX1-dev
│   │   ├── 0_real
│   │   ├── 1_fake
│   ├── Midjourney-V6
│   │   ├── ...
│   │   ...
│   ├── BLIP
│   │   ├── 0_real
│   │   ├── 1_fake
│   ├── Infinite-ID
│   │   ├── ...
│   │   ...
│   ├── CommunityAI
│   │   ├── 0_real
│   │   ├── 1_fake
│   ├── SocialRF
│   │   ├── ...

Citation

@inproceedings{li2025artificial,
  title={Is Artificial Intelligence Generated Image Detection a Solved Problem?},
  author={Li, Ziqiang and Yan, Jiazhen and He, Ziwen and Zeng, Kai and Jiang, Weiwei and Xiong, Lizhi and Fu, Zhangjie},
  booktitle={Advances in Neural Information Processing Systems},
  year={2025}
}

Contact

If you have any question about this project, please feel free to contact 247918horizon@gmail.com