Face Swapping for Solving Collateral Privacy Issues in Multimedia Analytics

Abstract

A wide range of components of multimedia analytics systems relies on visual content that is used for supervised (e.g., classification) and unsupervised (e.g., clustering) machine learning methods. This content may contain privacy sensitive information, e.g., show faces of persons. In many cases it is just an inevitable side-effect that persons appear in the content, and the application may not require identification – a situation which we call “collateral privacy issues”. We propose de-identification of faces in images by using a generative adversarial network to generate new face images, and use them to replace faces in the original images. We demonstrate that face swapping does not impact the performance of visual descriptor matching and extraction.