2024/09/23 by Jin Huang, Huang, Jin, Subhadra Gopalakrishnan +7 · 1 citation
Computer Science · Neuroscience · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face Recognition and Perception #Face recognition and analysis
paper · pdf · doi:10.48550/arxiv.2409.15498
openalex publication_date 2024/09/23 · openalex created_date 2024/10/26 · openalex updated_date 2026/07/28
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.