  Face images are rich data items that are useful and can easily be collected
in many applications, such as in 1-to-1 face verification tasks in the domain
of security and surveillance systems. Multiple methods have been proposed to
protect an individual's privacy by perturbing the images to remove traces of
identifiable information, such as gender or race. However, significantly less
attention has been given to the problem of protecting images while maintaining
optimal task utility. In this paper, we study the novel problem of creating
privacy-preserving image representations with respect to a given utility task
by proposing a principled framework called the Adversarial Image Anonymizer
(AIA). AIA first creates an image representation using a generative model, then
enhances the learned image representations using adversarial learning to
preserve privacy and utility for a given task. Experiments were conducted on a
publicly available data set to demonstrate the effectiveness of AIA as a
privacy-preserving mechanism for face images.
