Few-Shot Synthetic Image Attribution: Identifying Unseen Generators with Limited Samples

AI-generated image (AIGI) attribution presents a pressingchallenge that goes beyond mere AIGI detection, aiming to identify thesource model or technique responsible for a synthetic image. However,most previous source attribution methods operate in a closed-set manner,which necessitates retraining to recognize any novel category, preventingadaptation to the rapid evolution of image generation. In this work, wepropose a new paradigm for synthetic image attribution, termed few-shotattribution. This paradigm targets the reliable identification of unseengenerators using only limited samples, making it highly suitable for real-world applications. To facilitate this work, we construct OmniFake, alarge-scale, well-categorized synthetic image dataset that contains 1.17million images from 45 distinct generators. We further introduce Om-niDFA (Omni Detector and Few-shot Attributor), a few-shot attributionbaseline that not only assesses the authenticity of images but also deter-mines their synthesis origins. Experiments demonstrate that OmniDFAexhibits excellent capability in few-shot attribution and achieves state-of-the-art generalization performance in AIGI detection. Our dataset andcode are available at https://github.com/teheperinko541/OmniDFA.