  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 first
FRR dataset tailored for training the deep FRR models. Then, we propose a novel
diffusion-based FRR approach (FRRffusion) for the FRR task. Our FRRffusion
consists of a coarse-to-fine two-stage network: A diffusion-based Facial
Morpho-Architectonic Restorer (FMAR) is constructed to generate the basic
contours of low-resolution faces in the first stage, while a Transformer-based
Hyperrealistic Facial Detail Generator (HFDG) is designed to create
high-resolution facial details in the second stage. Tested on deepFRR, our
FRRffusion surpasses the GP-UNIT and Stable Diffusion methods by a large margin
in four widespread quantitative metrics. Especially, the de-retouched images by
our FRRffusion are visually much closer to the raw face images than both the
retouched face images and those restored by the GP-UNIT and Stable Diffusion
methods in terms of qualitative evaluation with 85 subjects. These results
sufficiently validate the efficacy of our work, bridging the recently-standing
gap between the FRR and generic image restoration tasks. The dataset and code
are available at https://github.com/GZHU-DVL/FRRffusion.
