DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
<|reference_start|>DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis: 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}.<|reference_end|>
@article{chen2024diffusionface:,
    title={DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face
  Forgery Analysis},
    author={Zhongxi Chen, Ke Sun, Ziyin Zhou, Xianming Lin, Xiaoshuai Sun, Liujuan
  Cao, Rongrong Ji},
    journal={arXiv preprint arXiv:2403.18471},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18471},
    primaryClass={cs.CV}
}