  With the popularity of social media platforms such as Instagram and TikTok,
and the widespread availability and convenience of retouching tools, an
increasing number of individuals are utilizing these tools to beautify their
facial photographs. This poses challenges for fields that place high demands on
the authenticity of photographs, such as identity verification and social
media. By altering facial images, users can easily create deceptive images,
leading to the dissemination of false information. This may pose challenges to
the reliability of identity verification systems and social media, and even
lead to online fraud. To address this issue, some work has proposed makeup
removal methods, but they still lack the ability to restore images involving
geometric deformations caused by retouching. To tackle the problem of facial
retouching restoration, we propose a framework, dubbed Face2Face, which
consists of three components: a facial retouching detector, an image
restoration model named FaceR, and a color correction module called
Hierarchical Adaptive Instance Normalization (H-AdaIN). Firstly, the facial
retouching detector predicts a retouching label containing three integers,
indicating the retouching methods and their corresponding degrees. Then FaceR
restores the retouched image based on the predicted retouching label. Finally,
H-AdaIN is applied to address the issue of color shift arising from diffusion
models. Extensive experiments demonstrate the effectiveness of our framework
and each module.
