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FRRffusion: Unveiling Authenticity with Diffusion-Based Face Retouching Reversal
May 14, 2024, 4:46 a.m. | Fengchuang Xing, Xiaowen Shi, Yuan-Gen Wang, Chunsheng Yang
cs.CV updates on arXiv.org arxiv.org
Abstract: Unveiling the real appearance of retouched faces to prevent malicious users from deceptive advertising and economic fraud has been an increasing concern in the era of digital economics. This article makes the first attempt to investigate the face retouching reversal (FRR) problem. We first collect an FRR dataset, named deepFRR, which contains 50,000 StyleGAN-generated high-resolution (1024*1024) facial images and their corresponding retouched ones by a commercial online API. To our best knowledge, deepFRR is the …
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