Face Anonymization Made Simple
Authors: name: Han-Wei Kung, name: Tuomas Varanka, name: Sanjay Saha, name: Terence Sim, name: Nicu Sebe

Current face anonymization techniques often depend on identity loss
calculated by face recognition models, which can be inaccurate and unreliable.
Additionally, many methods require supplementary data such as facial landmarks
and masks to guide the synthesis process. In contrast, our approach uses
diffusion models with only a reconstruction loss, eliminating the need for
facial landmarks or masks while still producing images with intricate,
fine-grained details. We validated our results on two public benchmarks through
both quantitative and qualitative evaluations. Our model achieves
state-of-the-art performance in three key areas: identity anonymization, facial
attribute preservation, and image quality. Beyond its primary function of
anonymization, our model can also perform face swapping tasks by incorporating
an additional facial image as input, demonstrating its versatility and
potential for diverse applications. Our code and models are available at
https://github.com/hanweikung/face_anon_simple .