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 the 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 92.48% accuracy
on FaceForensics++, 80.0% on Deep Fakes (in the wild), 88.35% on
CelebDF, and 99.27% on DeeperForensics datasets. Our approach
outperforms most deep and biological fake detectors with complex
network architectures without the proposed gaze signatures. We
conduct ablation studies involving different features, architectures,
sequence durations, and post-processing artifacts.