Local Privacy Protection for Sensitive Areas in Multiface Images.

Chao Liu,Yining Zhang,Jing Yang,Fengjuan Miao,Weinan Zhao,Yukun Shao,Xuan Zhang,Daqing Gong

doi:10.1155/2022/5919522

Abstract

The privacy protection for face images aims to prevent attackers from accurately identifying target persons through face recognition. Inspired by goal-driven reasoning (reverse reasoning), this paper designs a goal-driven algorithm of local privacy protection for sensitive areas in multiface images (face areas) under the interactive framework of face recognition algorithm, regional growth, and differential privacy. The designed algorithm, named privacy protection for sensitive areas (PPSA), is realized in the following manner: Firstly, the multitask cascaded convolutional network (MTCNN) was adopted to recognize the region and landmark of each face. If the landmark overlaps a subgraph divided from the original image, the subgraph will be taken as the seed for regional growth in the face area, following the growth criterion of the fusion similarity measurement mechanism (FSMM). Different from single-face privacy protection, multiface privacy protection needs to deal with an unknown number of faces. Thus, the allocation of the privacy budget ε directly affects the operation effect of the PPSA algorithm. In our scheme, the total privacy budget ε is divided into two parts: ε_1 and ε_2. The former is evenly allocated to each seed, according to the estimated number of faces ρ contained in the image, while the latter is allocated to the other areas that may consume the privacy budget through dichotomization. Unlike the Laplacian (LAP) algorithm, the noise error of the PPSA algorithm will not change with the image size, for the privacy protection is limited to the face area. The results show that the PPSA algorithm meets the requirements ε-Differential privacy, and image classification is realized by using different image privacy protection algorithms in different human face databases. The verification results show that the accuracy of the PPSA algorithm is improved by at least 16.1%, the recall rate is improved by at least 2.3%, and F1-score is improved by at least 15.2%.

Highlights

In the information age, the importance of data becomes more and more prominent. e value of data is highlighted in various fields in our society
To achieve privacy protection of sensitive information in multiface images and reduce noise impact on the privacy protected images, this paper proposes a local privacy protection method for face images, which combines face recognition, regional growth, and differential privacy. e proposed algorithm is called the privacy protection for sensitive areas (PPSA)
The PPSA was tested on a 750 ∗ 1020 image containing 8 faces from the WIDER FACE dataset. e original image was divided into 7,650 subgraphs of the size 10 ∗ 10

Summary

Introduction

The importance of data becomes more and more prominent. e value of data is highlighted in various fields in our society. Anks to the fast development of information technology and multimedia technology, it is easier to acquire and share digital face images. Statistics show that more than 3.2 billion face images are shared by users of major social network platforms around the world. E privacy protection of image data often relies on techniques like k-anonymity, access control, and privacy encryption. For a given image X, the gray matrix Xmn can be obtained by normalizing the image. Differential privacy For a given random algorithm M of image data publication, with the output range of Range(M), the algorithm can provide ε-differential privacy, if its arbitrary outputs on two adjacent gray images X and X′ satisfy the following: Pr[M(X) ∈ S] ≤ exp(ε) × Pr􏼂M X′􏼁 ∈ S􏼃,

Methods

Results

Conclusion