Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers

TL;DR

This paper introduces an ensemble of Semi Adversarial Networks that generate diverse face image perturbations to effectively confound arbitrary gender classifiers while preserving face matching capabilities.

Contribution

The paper proposes a novel ensemble SAN approach with diverse data augmentation to improve generalization against unseen gender classifiers.

Findings

Ensemble SANs successfully confound various unseen gender classifiers.

Diverse perturbations maintain face matching accuracy.

The method enhances gender privacy in face images.

Abstract

Recent research has proposed the use of Semi Adversarial Networks (SAN) for imparting privacy to face images. SANs are convolutional autoencoders that perturb face images such that the perturbed images cannot be reliably used by an attribute classifier (e.g., a gender classifier) but can still be used by a face matcher for matching purposes. However, the generalizability of SANs across multiple arbitrary gender classifiers has not been demonstrated in the literature. In this work, we tackle the generalization issue by designing an ensemble SAN model that generates a diverse set of perturbed outputs for a given input face image. This is accomplished by enforcing diversity among the individual models in the ensemble through the use of different data augmentation techniques. The goal is to ensure that at least one of the perturbed output faces will confound an arbitrary, previously unseen gender classifier. Extensive experiments using different unseen gender classifiers and face matchers are performed to demonstrate the efficacy of the proposed paradigm in imparting gender privacy to face images.