College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China.
College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China.
Comput Intell Neurosci. 2022 Mar 15;2022:5919522. doi: 10.1155/2022/5919522. eCollection 2022.
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 directly affects the operation effect of the PPSA algorithm. In our scheme, the total privacy budget is divided into two parts: _1 and _2. The former is evenly allocated to each seed, according to the estimated number of faces 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 -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 1-score is improved by at least 15.2%.
人脸图像隐私保护旨在防止攻击者通过人脸识别准确识别目标人员。受目标驱动推理（反向推理）的启发，本文在人脸识别算法、区域生长和差分隐私的交互框架下，设计了一种多人脸图像（人脸区域）中敏感区域的目标驱动局部隐私保护算法。所设计的算法称为敏感区域隐私保护（PPSA），通过以下方式实现：首先，采用多任务级联卷积网络（MTCNN）识别每张人脸的区域和地标。如果地标与从原始图像划分的子图重叠，则将该子图作为人脸区域的区域生长的种子，遵循融合相似性度量机制（FSMM）的生长准则。与单人脸隐私保护不同，多人脸隐私保护需要处理未知数量的人脸。因此，隐私预算的分配直接影响 PPSA 算法的运行效果。在我们的方案中，总隐私预算 分为两部分：_1 和 _2。前者根据图像中包含的估计人脸数，均匀分配给每个种子，后者通过二分法分配给可能消耗隐私预算的其他区域。与拉普拉斯（LAP）算法不同，PPSA 算法的噪声误差不会随图像尺寸而变化，因为隐私保护仅限于人脸区域。结果表明，PPSA 算法满足差分隐私要求，并通过在不同的人脸数据库中使用不同的图像隐私保护算法来实现图像分类。验证结果表明，PPSA 算法的准确率至少提高了 16.1%，召回率至少提高了 2.3%，1 分至少提高了 15.2%。
