Related papers: Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM

Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM

URL: http://arxiv.org/abs/2302.10280v1
Date: Fri, 27 Jan 2023 01:00:39 GMT
Title: Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM
Authors: Jacob mallet, Laura Pryor, Rushit Dave, Mounika Vanamala
Abstract summary: We propose a new deepfake detection schema using two popular machine learning algorithms. 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.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
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.

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