Creating a data set and a challenge for deepfakes

Data sets and benchmarks have been some of the most effective tools to speed progress in AI. Our current renaissance in deep learning has been fueled in part by the ImageNet benchmark. Recent advances in natural language processing have been hastened by the GLUE and SuperGLUE benchmarks. "Deepfake" techniques, which present realistic AI-generated videos of real people doing and saying fictional things, have significant implications for determining the legitimacy of information presented online. Yet the industry doesn't have a great data set or benchmark for detecting them.