Analysis of Human Perception in Distinguishing Real and AI-Generated Faces: An Eye-Tracking Based Study
This addresses concerns about AI-generated media misuse by analyzing human perceptual capabilities, though it is incremental as it builds on existing eye-tracking and AI face generation research.
The study investigated how humans distinguish real faces from AI-generated ones using eye-tracking, finding participants achieved 76.80% accuracy and scrutinized suspected fakes more closely.
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.