Abstract:
We present FakeET– an eye-tracking database to understand
human visual perception of 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
Tobii desktop eye-tracker for 811 videos from the Google
Deepfake dataset, with a minimum of two viewings per video.
Additionally, EEG responses acquired via the Emotiv sensor
are also available. The compiled data confirms (a) distinct eye
movement characteristics for real vs 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 raw EEG signal
to distinguish between real and fake videos
