Behavior-Based Deepfake Detection: Leveraging Cognitive Response to Visual Face Perception

Hendrik Graupner, Mohammad Yeghaneh Abkenar, Lisa Schwetlick, Ralf Engbert, Christoph Meinel

Abstract: Face presentation attacks are a propagating issue in an increasingly digitally interconnected world. One of the most recent developments is deepfake impersonation attacks in live video streams. Behavioral biometric analysis is a crucial part of a comprehensive solution to this pressing issue. This paper proposes the application of biological responses to visual self-recognition as a dynamic biometric trait. Self-recognition is a cognitive process that can be leveraged as in-brain identity validation. A sophisticated pre-trained model classifies eye-tracking data to determine the face in the user’s current visual focus. One eminent use case is the protection of online video conferences. This paper provides the architecture of a prototypical implementation based on an open-source video conferencing platform. Our work of interdisciplinary research aims to contribute to a holistic solution to protect our modern communication systems and restore trust in digitization.