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

Detection of Real-time DeepFakes in Video Conferencing with Active Probing and Corneal Reflection