  Realistic fake videos are a potential tool for spreading harmful
misinformation given our increasing online presence and information intake.
This paper presents a multimodal learning-based method for detection of real
and fake videos. The method combines information from three modalities - audio,
video, and physiology. We investigate two strategies for combining the video
and physiology modalities, either by augmenting the video with information from
the physiology or by novelly learning the fusion of those two modalities with a
proposed Graph Convolutional Network architecture. Both strategies for
combining the two modalities rely on a novel method for generation of visual
representations of physiological signals. The detection of real and fake videos
is then based on the dissimilarity between the audio and modified video
modalities. The proposed method is evaluated on two benchmark datasets and the
results show significant increase in detection performance compared to previous
methods.
