  The extraordinary ability of generative models to generate photographic
images has intensified concerns about the spread of disinformation, thereby
leading to the demand for detectors capable of distinguishing between
AI-generated fake images and real images. However, the lack of large datasets
containing images from the most advanced image generators poses an obstacle to
the development of such detectors. In this paper, we introduce the GenImage
dataset, which has the following advantages: 1) Plenty of Images, including
over one million pairs of AI-generated fake images and collected real images.
2) Rich Image Content, encompassing a broad range of image classes. 3)
State-of-the-art Generators, synthesizing images with advanced diffusion models
and GANs. The aforementioned advantages allow the detectors trained on GenImage
to undergo a thorough evaluation and demonstrate strong applicability to
diverse images. We conduct a comprehensive analysis of the dataset and propose
two tasks for evaluating the detection method in resembling real-world
scenarios. The cross-generator image classification task measures the
performance of a detector trained on one generator when tested on the others.
The degraded image classification task assesses the capability of the detectors
in handling degraded images such as low-resolution, blurred, and compressed
images. With the GenImage dataset, researchers can effectively expedite the
development and evaluation of superior AI-generated image detectors in
comparison to prevailing methodologies.
