Abstract
AI-generated image (AIGI) detection and source model attribution remain central challenges in combating deepfake abuses, primarily due to the structural diversity of generative models. Current detection methods are prone to overfitting specific forgery traits, whereas source attribution offers a robust alternative through fine-grained feature discrimination. However, synthetic image attribution remains constrained by the scarcity of large-scale, well-categorized synthetic datasets, limiting its practicality and compatibility with detection systems. In this work, we propose a new paradigm for image attribution called open-set, few-shot source identification. This paradigm is designed to reliably identify unseen generators using only limited samples, making it highly suitable for real-world application. To this end, we introduce OmniDFA (Omni Detector and Few-shot Attributor), a novel framework for AIGI that not only assesses the authenticity of images, but also determines the synthesis origins in a few-shot manner. To facilitate this work, we construct OmniFake, a large class-aware synthetic image dataset that curates $1.17$ M images from $45$ distinct generative models, substantially enriching the foundational resources for research on both AIGI detection and attribution. Experiments demonstrate that OmniDFA exhibits excellent capability in open-set attribution and achieves state-of-the-art generalization performance on AIGI detection. Our dataset and code will be made available.
Abstract (translated)
AI生成图像（AIGI）检测和源头模型归属仍然是打击深度伪造滥用的关键挑战，主要原因是生成模型的结构多样性。当前的检测方法容易过度拟合特定伪造特征，而源归属则通过细粒度特征区分提供了一种更为稳健的替代方案。然而，合成图像归属受限于大规模、分类良好的合成数据集的缺乏，这限制了其实用性和与检测系统的兼容性。在这项工作中，我们提出了一种新的图像归属范式，称为开放集合、少量样本源头识别。该范式旨在仅通过有限数量的样本可靠地识别未见生成器，非常适合实际应用。为此，我们引入了OmniDFA（全方位检测器和少量归属者），这是一种全新的AIGI框架，不仅能评估图像的真实性，还能以少量样本的方式确定其合成来源。为了支持这项工作，我们构建了OmniFake，这是一个大规模的类别感知型合成图像数据集，包含来自45个不同生成模型的117万张图片，极大地丰富了针对AIGI检测和归属研究的基础资源。实验表明，OmniDFA在开放集合归属方面表现出色，并且在AIGI检测方面的泛化性能达到业界领先水平。我们的数据集和代码将公开提供。
URL
https://arxiv.org/abs/2509.25682
