Diff-Privacy: Diffusion-based Face Privacy Protection
Xiao He Mingrui Zhu Dongxin Chen Nannan Wang Xinbo Gao
Privacy protection has become a top priority as the proliferation of AI
techniques has led to widespread collection and misuse of personal data.
Anonymization and visual identity information hiding are two important facial
privacy protection tasks that aim to remove identification characteristics from
facial images at the human perception level. However, they have a significant
difference in that the former aims to prevent the machine from recognizing
correctly, while the latter needs to ensure the accuracy of machine
recognition. Therefore, it is difficult to train a model to complete these two
tasks simultaneously. In this paper, we unify the task of anonymization and
visual identity information hiding and propose a novel face privacy protection
method based on diffusion models, dubbed Diff-Privacy. Specifically, we train
our proposed multi-scale image inversion module (MSI) to obtain a set of SDM
format conditional embeddings of the original image. Based on the conditional
embeddings, we design corresponding embedding scheduling strategies and
construct different energy functions during the denoising process to achieve
anonymization and visual identity information hiding. Extensive experiments
have been conducted to validate the effectiveness of our proposed framework in
protecting facial privacy.