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 semiadversarial training in this work. The training is facilitated by attaching a semi-adversarial module consisting of an auxiliary gender classifier and an auxiliary 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. In this work, we develop a convolutional autoencoder (CAE) that generates a perturbed face image that can be successfully used by a face matcher but not by a gender classifier. The proposed CAE is referred to as a semiadversarial network since its output is adversarial to the gender classifier but not to the face matcher. The proposed network can be easily appropriated for use with other attributes (such as age or race). In principle, the design of the semi-adversarial network can be utilized in other problem domains where there is a need to confound some classifiers while retaining the utility of other classifiers. 82 2018 International Conference on Biometrics 0-7695-6403-8/18/31.00
In order to derive perturbations that are transferable to unseen gender classifiers, Mirjalili et al. REF designed a convolutional autoencoder that modifies input face images such that an auxiliary face matcher still retains good matching performance on the modified output image while confounding an auxiliary gender classifier.
Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
2018 International Conference on Biometrics (ICB)
2018 International Conference on Biometrics (ICB)
Computer Science