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DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
March 28, 2024, 4:45 a.m. | Zhongxi Chen, Ke Sun, Ziyin Zhou, Xianming Lin, Xiaoshuai Sun, Liujuan Cao, Rongrong Ji
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 …
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