  The recent development of generative models unleashes the potential of
generating hyper-realistic fake images. To prevent the malicious usage of fake
images, AI-generated image detection aims to distinguish fake images from real
images. However, existing method suffer from severe performance drop when
detecting images generated by unseen generators. We find that generative models
tend to focus on generating the patches with rich textures to make the images
more realistic while neglecting the hidden noise caused by camera capture
present in simple patches. In this paper, we propose to exploit the noise
pattern of a single simple patch to identify fake images. Furthermore, due to
the performance decline when handling low-quality generated images, we
introduce an enhancement module and a perception module to remove the
interfering information. Extensive experiments demonstrate that our method can
achieve state-of-the-art performance on public benchmarks.
