Papers › Few-Shot Learner Generalizes Across AI-Generated Image Detection

Few-Shot Learner Generalizes Across AI-Generated Image Detection

15 Jan 2025arXiv:2501.08763archive 2025-07-28

Shiyu Wu, Jing Liu, Jing Li, Yequan Wang

Current fake image detectors trained on large synthetic image datasets perform satisfactorily on limited studied generative models. However, they 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 to effectively distinguish unseen fake images by utilizing very few samples. Experiments show FSD achieves state-of-the-art performance by +7.4% average ACC on GenImage dataset. More importantly, our method is better capable of capturing the intra-category common features in unseen images without further training.

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