2024/06/25 by Nela Petrželková, Petrzelkova, Nela, Jan Čech +1
Computer Science · #Advanced Computational Techniques in Science and Engineering #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Face recognition and analysis
paper · pdf · doi:10.48550/arxiv.2406.17547
openalex publication_date 2024/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
An experimental study on detecting synthetic face images is presented. We collected a dataset, called FF5, of five fake face image generators, including recent diffusion models. We find that a simple model trained on a specific image generator can achieve near-perfect accuracy in separating synthetic and real images. The model handles common image distortions (reduced resolution, compression) by using data augmentation. Moreover, partial manipulations, where synthetic images are blended into real ones by inpainting, are identified and the area of the manipulation is localized by a simple model of YOLO architecture. However, the model turned out to be vulnerable to adversarial attacks and does not generalize to unseen generators. Failure to generalize to detect images produced by a newer generator also occurs for recent state-of-the-art methods, which we tested on Realistic Vision, a fine-tuned version of StabilityAI's Stable Diffusion image generator.