# The eyes know it: FakeET -- An Eye-tracking Database to Understand Deepfake Perception

arXiv: https://arxiv.org/abs/2006.06961 · HTML: https://mlsanity.com/paper/2006.06961/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios](https://mlsanity.com/paper/2101.03321/) (2021)
- [Utilizing Cognitive Signals Generated during Human Reading to Enhance Keyphrase Extraction from Microblogs](https://mlsanity.com/paper/2606.26485/) (2026)
- [Towards mitigating uncann(eye)ness in face swaps via gaze-centric loss terms](https://mlsanity.com/paper/2402.03188/) (2024)
- [Analysis of Human Perception in Distinguishing Real and AI-Generated Faces: An Eye-Tracking Based Study](https://mlsanity.com/paper/2409.15498/) (2024)
- [Human-AI Ensembles Improve Deepfake Detection in Low-to-Medium Quality Videos](https://mlsanity.com/paper/2603.14658/) (2026)
- [Towards Gaze-Informed AI Disclosure Interfaces: Eye-Tracking Attentional and Cognitive Load While Reading AI-Assisted News](https://mlsanity.com/paper/2605.14999/) (2026)
- [Deepfake Detection using Biological Features: A Survey](https://mlsanity.com/paper/2301.05819/) (2023)

## Abstract (arXiv)

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 textitGoogle 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.