DOWNTOWN BROOKLYN -- A TEAM OF NYU TANDON COMPUTER SCIENCE FACULTY IS TACKLING DEEPFAKES. The team, with the support of Google's Cyber NYC Institutional Research Program (IRP), is developing an interactive system that issues intelligent challenges to differentiate real from deepfake audio and videos during live calls. The system uses what is called a "challenge-response" approach, which aims to "arm people with tools to avoid scams and other duplicitous acts."

Researchers have already demonstrated an approach that can successfully address reliability problems, using conventional forensic analysis in complex distribution channels. Chinmay Hegde, an associate professor in NYU Tandon Computer Science and Engineering and Electrical and Computer Engineering departments, and several of his colleagues, have published two papers that introduce and validate new techniques for real-time detection of deepfake audio and video. In the first paper, Hedge and his team point out hat AI-enabled Real-Time Deepfakes (RTDFs) "have now made it feasible to replace an imposter's face with their victim in live video interactions," and that "such advancement in deepfakes also coaxes detection to rise to the same standard."