Analysis of Human Perception in Distinguishing Real and AI-Generated Faces: An Eye-Tracking Based Study

Jin Huang, Subhadra Gopalakrishnan, Trisha Mittal, Jake Zuena, Jaclyn Pytlarz
9/23/2024
cs.CV

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Abstract

Recent advancements in Artificial Intelligence have led to remarkable improvements in generating realistic human faces. While these advancements demonstrate significant progress in generative models, they also raise concerns about the potential misuse of these generated images. In this study, we investigate how humans perceive and distinguish between real and fake images. We designed a perceptual experiment using eye-tracking technology to analyze how individuals differentiate real faces from those generated by AI. Our analysis of StyleGAN-3 generated images reveals that participants can distinguish real from fake faces with an average accuracy of 76.80%. Additionally, we found that participants scrutinize images more closely when they suspect an image to be fake. We believe this study offers valuable insights into human perception of AI-generated media.

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Cite this paper

@article{huang2024analysis,
  title  = {Analysis of Human Perception in Distinguishing Real and AI-Generated Faces: An Eye-Tracking Based Study},
  author = {Jin Huang and Subhadra Gopalakrishnan and Trisha Mittal and Jake Zuena and Jaclyn Pytlarz},
  year   = {2024},
  eprint = {2409.15498},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.15498v1}
}

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