  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.
