  Recent generative models show impressive performance in generating
photographic images. Humans can hardly distinguish such incredibly
realistic-looking AI-generated images from real ones. AI-generated images may
lead to ubiquitous disinformation dissemination. Therefore, it is of utmost
urgency to develop a detector to identify AI generated images. Most existing
detectors suffer from sharp performance drops over unseen generative models. In
this paper, we propose a novel AI-generated image detector capable of
identifying fake images created by a wide range of generative models. We
observe that the texture patches of images tend to reveal more traces left by
generative models compared to the global semantic information of the images. A
novel Smash&Reconstruction preprocessing is proposed to erase the global
semantic information and enhance texture patches. Furthermore, pixels in rich
texture regions exhibit more significant fluctuations than those in poor
texture regions. Synthesizing realistic rich texture regions proves to be more
challenging for existing generative models. Based on this principle, we
leverage the inter-pixel correlation contrast between rich and poor texture
regions within an image to further boost the detection performance.
  In addition, we build a comprehensive AI-generated image detection benchmark,
which includes 17 kinds of prevalent generative models, to evaluate the
effectiveness of existing baselines and our approach. Our benchmark provides a
leaderboard for follow-up studies. Extensive experimental results show that our
approach outperforms state-of-the-art baselines by a significant margin. Our
project: https://fdmas.github.io/AIGCDetect
