Outsmarting Deep Fakes: AI-Driven Imaging System Protects Authenticity Lab Manager

To thwart sophisticated methods of altering photos and video, researchers at the NYU Tandon School of Engineering have demonstrated an experimental technique to authenticate images throughout the entire pipeline, from acquisition to delivery, using artificial intelligence (AI). In tests, this prototype imaging pipeline increased the chances of detecting manipulation from approximately 45 percent to more than 90 percent without sacrificing image quality. Determining whether a photo or video is authentic is becoming increasingly problematic. Sophisticated techniques for altering photos and videos have become so accessible that so-called "deep fakes"--manipulated photos or videos that are remarkably convincing and often include celebrities or political figures--have become commonplace. Pawel Korus, a research assistant professor in the Department of Computer Science and Engineering at NYU Tandon, pioneered this approach.