  The advent of generative AI images has completely disrupted the art world.
Distinguishing AI generated images from human art is a challenging problem
whose impact is growing over time. A failure to address this problem allows bad
actors to defraud individuals paying a premium for human art and companies
whose stated policies forbid AI imagery. It is also critical for content owners
to establish copyright, and for model trainers interested in curating training
data in order to avoid potential model collapse.
  There are several different approaches to distinguishing human art from AI
images, including classifiers trained by supervised learning, research tools
targeting diffusion models, and identification by professional artists using
their knowledge of artistic techniques. In this paper, we seek to understand
how well these approaches can perform against today's modern generative models
in both benign and adversarial settings. We curate real human art across 7
styles, generate matching images from 5 generative models, and apply 8
detectors (5 automated detectors and 3 different human groups including 180
crowdworkers, 4000+ professional artists, and 13 expert artists experienced at
detecting AI). Both Hive and expert artists do very well, but make mistakes in
different ways (Hive is weaker against adversarial perturbations while Expert
artists produce higher false positives). We believe these weaknesses will
remain as models continue to evolve, and use our data to demonstrate why a
combined team of human and automated detectors provides the best combination of
accuracy and robustness.
