  The COVID pandemic has led to the wide adoption of online video calls in
recent years. However, the increasing reliance on video calls provides
opportunities for new impersonation attacks by fraudsters using the advanced
real-time DeepFakes. Real-time DeepFakes pose new challenges to detection
methods, which have to run in real-time as a video call is ongoing. In this
paper, we describe a new active forensic method to detect real-time DeepFakes.
Specifically, we authenticate video calls by displaying a distinct pattern on
the screen and using the corneal reflection extracted from the images of the
call participant's face. This pattern can be induced by a call participant
displaying on a shared screen or directly integrated into the video-call
client. In either case, no specialized imaging or lighting hardware is
required. Through large-scale simulations, we evaluate the reliability of this
approach under a range in a variety of real-world imaging scenarios.
