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

TL;DR

This paper introduces a convolutional autoencoder trained with semi-adversarial techniques to modify face images, preserving recognition ability while obscuring gender information for privacy.

Contribution

It proposes a novel semi-adversarial training scheme with a specialized module to effectively confound gender classification without affecting face recognition.

Findings

Autoencoder successfully confounds gender classification

Maintains face recognition accuracy

Effective privacy extension demonstrated

Abstract

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 a pseudo gender classifier and a pseudo 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.