Few-Shot Learner Generalizes Across AI-Generated Image Detection
Shiyu Wu, Jing Liu, Jing Li, Yequan Wang
Abstract
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.
BibTeX
@inproceedings{
wu2025fewshot,
title={Few-Shot Learner Generalizes Across {AI}-Generated Image Detection},
author={Shiyu Wu and Jing Liu and Jing Li and Yequan Wang},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=uvU29AfoNT}
}