Is Artificial Intelligence Generated Image Detection a Solved Problem?
Ziqiang Li1, Jiazhen Yan1, Ziwen He1, Kai Zeng2, Weiwei Jiang1, Lizhi Xiong1, Zhangjie Fu1‡
1Nanjing University of Information Science and Technology 2University of Siena
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: Deep Residual Learning for Image Recognition
 - CNNDetection: CNN-generated images are surprisingly easy to spot...for now
 - GramNet: Global Texture Enhancement for Fake Face Detection in the Wild
 - LGrad: Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
 - CLIPDetection: Towards Universal Fake Image Detectors that Generalize Across Generative Models
 - FreqNet: FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform
 - NPR: Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
 - DFFreq: Dual Frequency Branch Framework with Reconstructed Sliding Windows Attention for AI-Generated Image Detection
 - LaDeDa: Real-Time Deepfake Detection in the Real-World
 - AIDE: A Sanity Check for AI-generated Image Detection
 - SAFE: Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspectives
 
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