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

TL;DR

This paper introduces a real-time DeepFake detection method for video calls that uses active probing via corneal reflection analysis, enabling authentication without specialized hardware.

Contribution

The paper presents a novel active forensic approach leveraging corneal reflection patterns for real-time DeepFake detection in video conferencing.

Findings

High reliability in diverse real-world scenarios

No need for specialized imaging hardware

Effective in authenticating live video calls

Abstract

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.