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Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM
  • Rushit Dave ,
  • Mounika vanamala
Rushit Dave
Minnesota State University

Corresponding Author:[email protected]

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Mounika vanamala
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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’s 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; support vector machine and convolutional neural network, along with a publicly available dataset named the 140k Real and Fake Faces to accurately detect deepfakes in images with accuracy rates reaching as high as 88.33%.