Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM

TL;DR

This paper proposes a deepfake detection method using a hybrid dataset and combines CNN and SVM algorithms to identify doctored images and videos effectively.

Contribution

It introduces a novel deepfake detection approach utilizing a hybrid dataset and integrates CNN with SVM for improved accuracy.

Findings

Effective detection of deepfakes using the proposed hybrid dataset

Enhanced accuracy through combined CNN and SVM models

Potential to prevent misinformation spread online

Abstract

Social media is currently being used by many individuals online as a major source of information. However, not all information shared online is true, even photos and videos can be doctored. Deepfakes have recently risen with the rise of technological advancement and have allowed nefarious online users to replace one face with a computer generated face of anyone they would like, including important political and cultural figures. Deepfakes are now a tool to be able to spread mass misinformation. There is now an immense need to create models that are able to detect deepfakes and keep them from being spread as seemingly real images or videos. In this paper, we propose a new deepfake detection schema using two popular machine learning algorithms.