# In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking

arXiv: https://arxiv.org/abs/1806.02877 · HTML: https://mlsanity.com/paper/1806.02877/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 30 of 54)

- [FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset](https://mlsanity.com/paper/2108.05080/) (2021)
- [TALL: Thumbnail Layout for Deepfake Video Detection](https://mlsanity.com/paper/2307.07494/) (2023)
- [Protecting Celebrities from DeepFake with Identity Consistency Transformer](https://mlsanity.com/paper/2203.01318/) (2022)
- [Face Forensics in the Wild](https://mlsanity.com/paper/2103.16076/) (2021)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Deepfake Video Detection Using Convolutional Vision Transformer](https://mlsanity.com/paper/2102.11126/) (2021)
- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features](https://mlsanity.com/paper/2104.04480/) (2021)
- [Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors](https://mlsanity.com/paper/2109.02993/) (2021)
- [Towards Solving the DeepFake Problem : An Analysis on Improving DeepFake Detection using Dynamic Face Augmentation](https://mlsanity.com/paper/2102.09603/) (2021)
- [Deepfake Videos in the Wild: Analysis and Detection](https://mlsanity.com/paper/2103.04263/) (2021)
- [Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection](https://mlsanity.com/paper/2403.10261/) (2024)
- [Celeb-DF++: A Large-scale Challenging Video DeepFake Benchmark for Generalizable Forensics](https://mlsanity.com/paper/2507.18015/) (2025)
- [FaceGuard: Proactive Deepfake Detection](https://mlsanity.com/paper/2109.05673/) (2021)
- [Detection and Localization of Facial Expression Manipulations](https://mlsanity.com/paper/2103.08134/) (2021)
- [Towards Benchmarking and Evaluating Deepfake Detection](https://mlsanity.com/paper/2203.02115/) (2022)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [Where Do Deep Fakes Look? Synthetic Face Detection via Gaze Tracking](https://mlsanity.com/paper/2101.01165/) (2021)
- [DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation](https://mlsanity.com/paper/2312.10680/) (2023)
- [Fair Deepfake Detectors Can Generalize](https://mlsanity.com/paper/2507.02645/) (2025)
- [Understanding the Security of Deepfake Detection](https://mlsanity.com/paper/2107.02045/) (2021)
- [Learning Real Facial Concepts for Independent Deepfake Detection](https://mlsanity.com/paper/2505.04460/) (2025)
- [Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction](https://mlsanity.com/paper/2102.00798/) (2021)
- [Detecting Deepfake Videos Using Euler Video Magnification](https://mlsanity.com/paper/2101.11563/) (2021)
- [Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection](https://mlsanity.com/paper/2412.01101/) (2024)
- [Generalized Facial Manipulation Detection with Edge Region Feature Extraction](https://mlsanity.com/paper/2102.01381/) (2021)
- [What's wrong with this video? Comparing Explainers for Deepfake Detection](https://mlsanity.com/paper/2105.05902/) (2021)
- [Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes](https://mlsanity.com/paper/2409.10889/) (2024)
- [ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces](https://mlsanity.com/paper/2308.00964/) (2023)
- [A Spatial-Frequency Aware Multi-Scale Fusion Network for Real-Time Deepfake Detection](https://mlsanity.com/paper/2508.20449/) (2025)

## Abstract (arXiv)

The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos. In this work, we describe a new method to expose fake face videos generated with neural networks. Our method is based on detection of eye blinking in the videos, which is a physiological signal that is not well presented in the synthesized fake videos. Our method is tested over benchmarks of eye-blinking detection datasets and also show promising performance on detecting videos generated with DeepFake.