Abstract:
Deepfake videos, which use artifcial intelligence techniques to create realistic but fabricated footage, have raised concerns regarding
their potential to deceive and manipulate viewers. This study is one
of the frst of its kind that aimed to investigate the cross-cultural
perception of deepfakes and uncover potential neural markers associated with their detection. Electroencephalography (EEG) data
were recorded from 10 healthy participants while they viewed three
categories of videos: Asian people speaking Chinese (C-C), Asian
people speaking English (C-E), and Middle Eastern people speaking
English (A-E). Participants were asked to determine whether each
video wasreal orfake. Behavioral analysisrevealed that participants
performed better in diferentiating real and deepfake videos when
the provided visual stimulus was in a language they were familiar
with (English) and when the actor belonged to an ethnically similar
background. EEG analysis demonstrated signifcant diferences in
brain signals between the three categories, suggesting the potential
use of EEG as a biomarker for deepfake classifcation. Machine
learning models achieved accuracies of up to 84.52% in categorizing
the EEG data while observing real vs. fake videos, with Support
Vector Machines. These fndings contribute to our understanding
of deepfake perception, have implications for the development of
deepfake detection methods, and highlight the importance of media
literacy in the face of digital deception.
