Invertible Mask Network for Face Privacy-Preserving

TL;DR

This paper introduces an invertible mask network that ensures natural, privacy-protected face images while allowing authorized recovery of the original face, balancing privacy and reusability.

Contribution

The paper proposes a novel invertible mask network (IMN) that generates reversible face masks, enabling privacy protection and accurate recovery of original faces.

Findings

Effective privacy protection demonstrated

High-quality face recovery achieved

Masked faces are visually indistinguishable from masks

Abstract

Face privacy-preserving is one of the hotspots that arises dramatic interests of research. However, the existing face privacy-preserving methods aim at causing the missing of semantic information of face and cannot preserve the reusability of original facial information. To achieve the naturalness of the processed face and the recoverability of the original protected face, this paper proposes face privacy-preserving method based on Invertible "Mask" Network (IMN). In IMN, we introduce a Mask-net to generate "Mask" face firstly. Then, put the "Mask" face onto the protected face and generate the masked face, in which the masked face is indistinguishable from "Mask" face. Finally, "Mask" face can be put off from the masked face and obtain the recovered face to the authorized users, in which the recovered face is visually indistinguishable from the protected face. The experimental results show that the proposed method can not only effectively protect the privacy of the protected face, but also almost perfectly recover the protected face from the masked face.