  Since the advent of Deepfakes in digital media, the development of robust and
reliable detection mechanism is urgently called for. In this study, we explore
a novel approach to Deepfake detection by utilizing electroencephalography
(EEG) measured from the neural processing of a human participant who viewed and
categorized Deepfake stimuli from the FaceForensics++ datset. These
measurements serve as input features to a binary support vector classifier,
trained to discriminate between real and manipulated facial images. We examine
whether EEG data can inform Deepfake detection and also if it can provide a
generalized representation capable of identifying Deepfakes beyond the training
domain. Our preliminary results indicate that human neural processing signals
can be successfully integrated into Deepfake detection frameworks and hint at
the potential for a generalized neural representation of artifacts in computer
generated faces. Moreover, our study provides next steps towards the
understanding of how digital realism is embedded in the human cognitive system,
possibly enabling the development of more realistic digital avatars in the
future.
