Protecting World Leaders Against Deep Fakes

Shruti Agarwal, Hany Farid, Yuming Gu, Mingming He, Koki Nagano, Hao Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 38-45


The creation of sophisticated fake videos has been largely relegated to Hollywood studios or state actors. Recent advances in deep learning, however, have made it significantly easier to create sophisticated and compelling fake videos. With relatively modest amounts of data and computing power, the average person can, for example, create a video of a world leader confessing to illegal activity leading to a constitutional crisis, a military leader saying something racially insensitive leading to civil unrest in an area of military activity, or a corporate titan claiming that their profits are weak leading to global stock manipulation. These so called deep fakes pose a significant threat to our democracy, national security, and society. To contend with this growing threat, we describe a forensic technique that models facial expressions and movements that typify an individual's speaking pattern. Although not visually apparent, these correlations are often violated by the nature of how deep-fake videos are created and can, therefore, be used for authentication.

Related Material

author = {Agarwal, Shruti and Farid, Hany and Gu, Yuming and He, Mingming and Nagano, Koki and Li, Hao},
title = {Protecting World Leaders Against Deep Fakes},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}