Detecting Deepfake Videos Using Euler Video Magnification

TL;DR

This paper explores using Euler video magnification to detect deepfake videos by highlighting subtle features, training models to distinguish genuine from manipulated videos, and comparing effectiveness with existing methods.

Contribution

Introduces a novel approach employing Euler video magnification for deepfake detection and evaluates its performance against current techniques.

Findings

Effective in highlighting subtle facial features

Achieves competitive accuracy in classifying deepfakes

Provides a new tool for deepfake detection research

Abstract

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.