Real and Deepfake Face Recognition: An EEG Study on Cognitive and Emotive Implications

Abstract

The human brain’s role in face processing (FP) and decision making for social interactions depends on recognizing faces accurately. However, the prevalence of deepfakes, AI-generated images, poses challenges in discerning real from synthetic identities. This study investigated healthy individuals’ cognitive and emotional engagement in a visual discrimination task involving real and deepfake human faces expressing positive, negative, or neutral emotions. Electroencephalographic (EEG) data were collected from 23 healthy participants using a 21-channel dry-EEG headset; power spectrum and event-related potential (ERP) analyses were performed. Results revealed statistically significant activations in specific brain areas depending on the authenticity and emotional content of the stimuli. Power spectrum analysis highlighted a right-hemisphere predominance in theta, alpha, high-beta, and gamma bands for real faces, while deepfakes mainly affected the frontal and occipital areas in the delta band. ERP analysis hinted at the possibility of discriminating between real and synthetic faces, as N250 (200–300 ms after stimulus onset) peak latency decreased when observing real faces in the right frontal (LF) and left temporo-occipital (LTO) areas, but also within emotions, as P100 (90–140 ms) peak amplitude was found higher in the right temporo-occipital (RTO) area for happy faces with respect to neutral and sad ones.