Learning Discrepant Transformations for Face Privacy Protection

Online face recognition systems usually store face features in the server database for authentication, which are vulnerable to face reconstruction attacks. Various face privacy protection approaches have been proposed to address this issue, where transformation-based schemes are shown to be promising. However, the existing transformation-based schemes are all hand-crafted approaches which are difficult to balance the privacy protection and face recognition. In this paper, we propose to learn a set of discrepant convolutional neural networks (DCNNs) to protect the privacy of face features. We randomly split the original face features into different sub-features. Each of the DCNNs transforms an original sub-feature into a protected one. We adopt appropriate strategies to make the DCNNs as diverse as possible to improve the ability of our protected features to resist different face reconstruction attacks, where a face recognition loss and a privacy protection loss are designed for training. The former ensures that the protected feature can be matched directly using the existing face recognizers, while the latter incorporates a shadow face reconstruction model to interrupt the correlation between the protected features and the face images. Experimental results demonstrate the advantage of our method over existing schemes for face privacy protection. Our protected features can be accurately matched using existing face recognizers, which are capable of resisting both black-box and white-box face reconstruction attacks.