Current fake image detectors trained on large synthetic image datasets perform satisfactorily on limited studied generative models. However, these detectors suffer a notable performance decline over unseen models. Besides, collecting adequate training data from online generative models is often expensive or infeasible. To overcome these issues, we propose Few-Shot Detector (FSD), a novel AI-generated image detector which learns a specialized metric space for effectively distinguishing unseen fake images using very few samples. Experiments show that FSD achieves state-of-the-art performance by +11.6% average accuracy on the GenImage dataset with only 10 additional samples. More importantly, our method is better capable of capturing the intra-category commonality in unseen images without further training. Our code is available at https://github.com/teheperinko541/Few-Shot-AIGI-Detector.
@article{arxiv.2501.08763,
title = {Few-Shot Learner Generalizes Across AI-Generated Image Detection},
author = {Shiyu Wu and Jing Liu and Jing Li and Yequan Wang},
journal= {arXiv preprint arXiv:2501.08763},
year = {2025}
}