A Single Simple Patch is All You Need for AI-generated Image Detection
Jiaxuan Chen Jieteng Yao Li Niu
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. Nevertheless, existing methods usually suffer from poor
generalizability across different generators. In this work, we propose an
embarrassingly simple approach named SSP, i.e., feeding the noise pattern of a
Single Simple Patch (SSP) to a binary classifier, which could achieve 14.6%
relative improvement over the recent method on GenImage dataset. Our SSP method
is very robust and generalizable, which could serve as a simple and competitive
baseline for the future methods.