Abstract

This white paper came out of an exploratory workshop held on November 15-16, 2019 at the Institute for Pure and Applied Mathematics at UCLA. Represented at the workshop were members of the mathematics, machine learning, cryptography, philosophy, social science, legal, and policy communities. Discussion at the workshop focused on the impact of deep fakery and how to respond to it. The opinions expressed in this white paper represent those of the individuals involved, and not of their organizations or of the Institute for Pure and Applied Mathematics.
“Deep fake” technology represents a substantial advance on earlier technologies of image, audio, and video manipulation like photoshopping. It emerged from the recent deep learning revolution, especially the development of generative adversarial networks. It enables the efficient, computer-assisted production of highly believable audio and video in which real people appear to be saying things they never said and doing things they never did.

Date

January 1, 1970

Authors

David Chu, Ilke Demir, Kristen Eichensehr, Jacob G Foster, Mark L Green, Kristina Lerman, Filippo Menczer, Cailin O’Connor, Edward Parson, Lars Ruthotto, Amit Sahai10, Jose Sotelo11, Luca Venturi12

Journal

Institute for Pure and Applied Mathematics (IPAM)

Publisher

University of California