|
|
--- |
|
|
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 |
