Few-Shot Learner Generalizes Across AI-Generated Image Detection

• URL: http://arxiv.org/abs/2501.08763v1
• Date: Wed, 15 Jan 2025 12:33:11 GMT
• Title: Few-Shot Learner Generalizes Across AI-Generated Image Detection
• Authors: Shiyu Wu, Jing Liu, Jing Li, Yequan Wang,
• Abstract summary: Few-Shot Detector (FSD) is a novel AI-generated image detector which learns a specialized metric space to effectively distinguish unseen fake images. Experiments show FSD state-of-the-art performance by $+7.4%$ average ACC on GenImage dataset.
• Score: 14.069833211684715
• License:
• Abstract: 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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