Invertible Mask Network for Face Privacy-Preserving
This addresses privacy concerns for individuals in facial data applications, but it is incremental as it builds on existing methods by adding recoverability.
The paper tackles the problem of face privacy-preserving by proposing an Invertible Mask Network (IMN) that generates a masked face to protect privacy while allowing near-perfect recovery of the original face for authorized users, with experimental results showing effective protection and almost perfect recovery.
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.