  The aim of this work is to explore the potential of pre-trained
vision-language models (VLMs) for universal detection of AI-generated images.
We develop a lightweight detection strategy based on CLIP features and study
its performance in a wide variety of challenging scenarios. We find that,
contrary to previous beliefs, it is neither necessary nor convenient to use a
large domain-specific dataset for training. On the contrary, by using only a
handful of example images from a single generative model, a CLIP-based detector
exhibits surprising generalization ability and high robustness across different
architectures, including recent commercial tools such as Dalle-3, Midjourney
v5, and Firefly. We match the state-of-the-art (SoTA) on in-distribution data
and significantly improve upon it in terms of generalization to
out-of-distribution data (+6% AUC) and robustness to impaired/laundered data
(+13%). Our project is available at
https://grip-unina.github.io/ClipBased-SyntheticImageDetection/
