Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers
This work addresses privacy concerns for individuals by enhancing the robustness of face image protection against gender classification, though it is incremental as it builds on existing SAN methods.
The paper tackled the generalization issue of Semi Adversarial Networks (SANs) for gender privacy in face images by designing an ensemble model that generates diverse perturbed outputs to confound arbitrary, unseen gender classifiers, demonstrating efficacy through extensive experiments.
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.