TITLE: Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM 

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.
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.
Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM
http://arxiv.org/abs/2302.10280v1