PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via
  Secure Flow
Lin Yuan Kai Liang Xiao Pu Yan Zhang Jiaxu Leng Tao Wu Nannan Wang Xinbo Gao
This paper proposes a novel paradigm for facial privacy protection that
unifies multiple characteristics including anonymity, diversity, reversibility
and security within a single lightweight framework. We name it PRO-Face S,
short for Privacy-preserving Reversible Obfuscation of Face images via Secure
flow-based model. In the framework, an Invertible Neural Network (INN) is
utilized to process the input image along with its pre-obfuscated form, and
generate the privacy protected image that visually approximates to the
pre-obfuscated one, thus ensuring privacy. The pre-obfuscation applied can be
in diversified form with different strengths and styles specified by users.
Along protection, a secret key is injected into the network such that the
original image can only be recovered from the protection image via the same
model given the correct key provided. Two modes of image recovery are devised
to deal with malicious recovery attempts in different scenarios. Finally,
extensive experiments conducted on three public image datasets demonstrate the
superiority of the proposed framework over multiple state-of-the-art
approaches.