High-level semantic inconsistencies in social gaze (eye direction, head-eye alignment) are more reliable for detecting AI-generated images than low-level pixel artifacts, and this signal transfers across different generative models.
This paper shows that AI-generated images often fail at maintaining realistic gaze patterns between people—like consistent eye direction and head-eye alignment. The researchers built a detection system using this semantic weakness, along with a carefully designed dataset and training approach, achieving better detection across multiple AI image generators.