  We present \textbf{FakeET}-- an eye-tracking database to understand human
visual perception of \emph{deepfake} videos. Given that the principal purpose
of deepfakes is to deceive human observers, FakeET is designed to understand
and evaluate the ease with which viewers can detect synthetic video artifacts.
FakeET contains viewing patterns compiled from 40 users via the \emph{Tobii}
desktop eye-tracker for 811 videos from the \textit{Google Deepfake} dataset,
with a minimum of two viewings per video. Additionally, EEG responses acquired
via the \emph{Emotiv} sensor are also available. The compiled data confirms (a)
distinct eye movement characteristics for \emph{real} vs \emph{fake} videos;
(b) utility of the eye-track saliency maps for spatial forgery localization and
detection, and (c) Error Related Negativity (ERN) triggers in the EEG
responses, and the ability of the \emph{raw} EEG signal to distinguish between
\emph{real} and \emph{fake} videos.
