Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacyto Face Images
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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 output image can be successfully used for face recognition but not for gender classification. The proposed network uses a new type of learning scheme developed in this work -- referred to as semi-adversarial training -- in order to accomplish the stated purpose. The network has three subnetworks: a convolutional auto-encoder, a pseudo gender classifier, and a pseudo face matcher. A two-stage training scheme is used. 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. Experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images.
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