# Detecting Deepfake Videos Using Euler Video Magnification

arXiv: https://arxiv.org/abs/2101.11563 · HTML: https://mlsanity.com/paper/2101.11563/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Face2Face: Real-time Face Capture and Reenactment of RGB Videos](https://mlsanity.com/paper/2007.14808/) (2020)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [The Deepfake Detection Challenge (DFDC) Preview Dataset](https://mlsanity.com/paper/1910.08854/) (2019)
- [Handheld Multi-Frame Super-Resolution](https://mlsanity.com/paper/1905.03277/) (2019)
- [Detecting GAN-generated Imagery using Color Cues](https://mlsanity.com/paper/1812.08247/) (2018)
- [In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking](https://mlsanity.com/paper/1806.02877/) (2018)
- [Identification of Deep Network Generated Images Using Disparities in Color Components](https://mlsanity.com/paper/1808.07276/) (2018)
- [Detecting Deepfakes with Metric Learning](https://mlsanity.com/paper/2003.08645/) (2020)
- [Using GANs to Synthesise Minimum Training Data for Deepfake Generation](https://mlsanity.com/paper/2011.05421/) (2020)
- [An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data](https://mlsanity.com/paper/1812.00668/) (2018)
- [Differential Imaging Forensics](https://mlsanity.com/paper/1906.05268/) (2019)

## Built on by (most cited first, 4 of 4)

- [Generalizable Deepfake Detection with Phase-Based Motion Analysis](https://mlsanity.com/paper/2211.09363/) (2022)
- [How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection](https://mlsanity.com/paper/2212.14033/) (2022)
- [DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning](https://mlsanity.com/paper/2109.02874/) (2021)
- [Attribution-Guided Multimodal Deepfake Detection via Cross-Modal Forensic Fingerprints](https://mlsanity.com/paper/2604.26453/) (2026)

## Abstract (arXiv)

Recent advances in artificial intelligence make it progressively hard to distinguish between genuine and counterfeit media, especially images and videos. One recent development is the rise of deepfake videos, based on manipulating videos using advanced machine learning techniques. This involves replacing the face of an individual from a source video with the face of a second person, in the destination video. This idea is becoming progressively refined as deepfakes are getting progressively seamless and simpler to compute. Combined with the outreach and speed of social media, deepfakes could easily fool individuals when depicting someone saying things that never happened and thus could persuade people in believing fictional scenarios, creating distress, and spreading fake news. In this paper, we examine a technique for possible identification of deepfake videos. We use Euler video magnification which applies spatial decomposition and temporal filtering on video data to highlight and magnify hidden features like skin pulsation and subtle motions. Our approach uses features extracted from the Euler technique to train three models to classify counterfeit and unaltered videos and compare the results with existing techniques.