  The rapid progress in deep learning has given rise to hyper-realistic facial
forgery methods, leading to concerns related to misinformation and security
risks. Existing face forgery datasets have limitations in generating
high-quality facial images and addressing the challenges posed by evolving
generative techniques. To combat this, we present DiffusionFace, the first
diffusion-based face forgery dataset, covering various forgery categories,
including unconditional and Text Guide facial image generation, Img2Img,
Inpaint, and Diffusion-based facial exchange algorithms. Our DiffusionFace
dataset stands out with its extensive collection of 11 diffusion models and the
high-quality of the generated images, providing essential metadata and a
real-world internet-sourced forgery facial image dataset for evaluation.
Additionally, we provide an in-depth analysis of the data and introduce
practical evaluation protocols to rigorously assess discriminative models'
effectiveness in detecting counterfeit facial images, aiming to enhance
security in facial image authentication processes. The dataset is available for
download at \url{https://github.com/Rapisurazurite/DiffFace}.
