  Detecting diffusion-generated images has recently grown into an emerging
research area. Existing diffusion-based datasets predominantly focus on general
image generation. However, facial forgeries, which pose a more severe social
risk, have remained less explored thus far. To address this gap, this paper
introduces DiFF, a comprehensive dataset dedicated to face-focused
diffusion-generated images. DiFF comprises over 500,000 images that are
synthesized using thirteen distinct generation methods under four conditions.
In particular, this dataset leverages 30,000 carefully collected textual and
visual prompts, ensuring the synthesis of images with both high fidelity and
semantic consistency. We conduct extensive experiments on the DiFF dataset via
a human test and several representative forgery detection methods. The results
demonstrate that the binary detection accuracy of both human observers and
automated detectors often falls below 30%, shedding light on the challenges in
detecting diffusion-generated facial forgeries. Furthermore, we propose an edge
graph regularization approach to effectively enhance the generalization
capability of existing detectors.
