  Photos serve as a way for humans to record what they experience in their
daily lives, and they are often regarded as trustworthy sources of information.
However, there is a growing concern that the advancement of artificial
intelligence (AI) technology may produce fake photos, which can create
confusion and diminish trust in photographs. This study aims to comprehensively
evaluate agents for distinguishing state-of-the-art AI-generated visual
content. Our study benchmarks both human capability and cutting-edge fake image
detection AI algorithms, using a newly collected large-scale fake image dataset
Fake2M. In our human perception evaluation, titled HPBench, we discovered that
humans struggle significantly to distinguish real photos from AI-generated
ones, with a misclassification rate of 38.7%. Along with this, we conduct the
model capability of AI-Generated images detection evaluation MPBench and the
top-performing model from MPBench achieves a 13% failure rate under the same
setting used in the human evaluation. We hope that our study can raise
awareness of the potential risks of AI-generated images and facilitate further
research to prevent the spread of false information. More information can refer
to https://github.com/Inf-imagine/Sentry.
