Unnoticeable synthetic face replacement for image privacy protection

A rapidly growing amount of personal sensitive information is being released to the public due to unprotected sharing of face images and videos on social networks. Although some pioneering face de-identification techniques, such as face blurring, have been proposed, there is still a long way towards providing full protection of one's facial privacy. In this paper, we propose a novel end-to-end privacy protection approach to seamlessly replace a face in an image with a synthesized face that looks as natural as normal photos yet pertaining very different look from the original face. The synthesized face images will prevent potential attackers from de-identifying the users. Specifically, our approach relies on generative adversarial network and considers both the foreground and background constrains with respect to the input face image to achieve the following two goals: Firstly, to make synthesized images perceptually unaltered, we design a new generative model to effectively fuse a synthesized face with the original background. Secondly, to ensure a synthesized face to be much different from the original face, we define multiple losses to distinguish the synthesized face from the original face. The experimental results on public datasets have validated the effectiveness of our approach compared with the-state-of-the-art.