  Following the recent initiatives for the democratization of AI, deep fake
generators have become increasingly popular and accessible, causing dystopian
scenarios towards social erosion of trust. A particular domain, such as
biological signals, attracted attention towards detection methods that are
capable of exploiting authenticity signatures in real videos that are not yet
faked by generative approaches. In this paper, we first propose several
prominent eye and gaze features that deep fakes exhibit differently. Second, we
compile those features into signatures and analyze and compare those of real
and fake videos, formulating geometric, visual, metric, temporal, and spectral
variations. Third, we generalize this formulation to deep fake detection
problem by a deep neural network, to classify any video in the wild as fake or
real. We evaluate our approach on several deep fake datasets, achieving 89.79\%
accuracy on FaceForensics++, 80.0\% on Deep Fakes (in the wild), and 88.35\% on
CelebDF datasets. We conduct ablation studies involving different features,
architectures, sequence durations, and post-processing artifacts. Our analysis
concludes with 6.29\% improved accuracy over complex network architectures
without the proposed gaze signatures.
