  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.
