Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images

In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. In order to train this autoencoder, we propose a novel training scheme, referred to as semi-adversarial training in this work. The training is facilitated by attaching a semi-adversarial module consisting of an auxiliary gender classifier and an auxiliary face matcher to the autoencoder. The objective function utilized for training this network has three terms: one to ensure that the perturbed image is a realistic face image; another to ensure that the gender attributes of the face are confounded; and a third to ensure that biometric recognition performance due to the perturbed image is not impacted. Extensive experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images.

1. Introduction

Biometric face recognition refers to the use of face images for recognizing an individual in an automated manner [10]. A typical face recognition system employs a face matcher that compares two face images and determines the degree of similarity or dissimilarity between them. This comparison operation can be used to (a) verify the claimed identity of an input face image or (b) determine the identity of an unknown face image by comparing it against a set of known face images.