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---
license: cc-by-nc-sa-4.0
datasets:
- HorizonTEL/AIGIBench
---
<div align="center">
<br>
<h1>Is Artificial Intelligence Generated Image Detection a Solved Problem?</h1>
[Ziqiang Li](https://scholar.google.com/citations?user=mj5a8WgAAAAJ&hl=zh-CN)<sup>1</sup>, [Jiazhen Yan](https://scholar.google.com/citations?user=QkURh8EAAAAJ&hl=zh-CN)<sup>1</sup>, [Ziwen He](https://scholar.google.com/citations?user=PjkDK9cAAAAJ&hl=zh-CN)<sup>1</sup>, [Kai Zeng](https://scholar.google.com.hk/citations?user=TsI93SIAAAAJ&hl=zh-CN)<sup>2</sup>, [Weiwei Jiang](https://scholar.google.co.jp/citations?user=mbPN0hgAAAAJ&hl=zh-CN)<sup>1</sup>, [Lizhi Xiong](https://scholar.google.com/citations?user=-FzrEP4AAAAJ&hl=zh-CN)<sup>1</sup>, [Zhangjie Fu](https://scholar.google.com/citations?user=fO9NmagAAAAJ&hl=zh-CN)<sup>1‡</sup>
<div class="is-size-6 publication-authors">
<p class="footnote">
<span class="footnote-symbol"><sup>‡</sup></span>Corresponding author
</p>
</div>
<sup>1</sup>Nanjing University of Information Science and Technology <sup>2</sup>University of Siena
</div>
**This repository is the official pre-trained checkpoints of the AIGIBench in Setting-II**: Training on 144K images generated by both SD-v1.4 and ProGAN, covering the same four object categories.
Of course, if you need the code from the original paper, the following is the corresponding detection code in the paper：
- [ResNet-50](https://github.com/huggingface/pytorch-image-models/tree/v0.6.12/timm): Deep Residual Learning for Image Recognition
- [CNNDetection](https://github.com/PeterWang512/CNNDetection): CNN-generated images are surprisingly easy to spot...for now
- [GramNet](https://github.com/liuzhengzhe/Global_Texture_Enhancement_for_Fake_Face_Detection_in_the-Wild): Global Texture Enhancement for Fake Face Detection in the Wild
- [LGrad](https://github.com/chuangchuangtan/LGrad): Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
- [CLIPDetection](https://github.com/WisconsinAIVision/UniversalFakeDetect): Towards Universal Fake Image Detectors that Generalize Across Generative Models
- [FreqNet](https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection): FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform
- [NPR](https://github.com/chuangchuangtan/NPR-DeepfakeDetection): Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
- [DFFreq](https://arxiv.org/abs/2501.15253): Dual Frequency Branch Framework with Reconstructed Sliding Windows Attention for AI-Generated Image Detection
- [LaDeDa](https://github.com/barcavia/RealTime-DeepfakeDetection-in-the-RealWorld): Real-Time Deepfake Detection in the Real-World
- [AIDE](https://github.com/shilinyan99/AIDE): A Sanity Check for AI-generated Image Detection
- [SAFE](https://github.com/Ouxiang-Li/SAFE): Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspectives
If this project helps you, please fork, watch, and give a star to this repository.
## Contact
If you have any question about this project, please feel free to contact 247918horizon@gmail.com |