A Shared Look: Detecting Deepfakes with Inter-Subject Neural Synchrony

The rapid evolution of generative AI presents a significant challenge for Deepfake detection. While most research focuses on face-swapping, the emerging threat of "image-to-video" (I2V) forgeries is harder to detect and poses a greater risk. Traditional computer vision detectors rely on transient digital artifacts, which often lack interpretability and robustness against the new generation techniques. This study introduces a neuro-cognitive method, using dyadic electroencephalogram (EEG) to decode the human perception of authenticity. We recorded inter-brain synchrony via EEG hyperscanning from 15 participant pairs as they viewed a balanced set of authentic and AI-generated videos. Results showed that these shared neural response can classify video authenticity with an accuracy of up to 89.23% using our proposed Hyper-FusionNet. In addition, the biomarkers exhibited distinct patterns for different emotional valences, highlighting their versatility. These findings highlight the potential of inter-brain synchrony for detecting emerging deepfakes, offering a new perspective for enhancing user trust and digital literacy.