Deep Fake Detection: Survey of Facial Manipulation Detection Solutions
International Research Journal of Engineering and Technology Volume 8, Issue 5, May 2021 Deep Learning as a field has been successfully used to solve a plethora of complex problems, the likes of which we could not have imagined a few decades back. But as many benefits as it brings, there are still w...
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Zusammenfassung: | International Research Journal of Engineering and Technology
Volume 8, Issue 5, May 2021 Deep Learning as a field has been successfully used to solve a plethora of
complex problems, the likes of which we could not have imagined a few decades
back. But as many benefits as it brings, there are still ways in which it can
be used to bring harm to our society. Deep fakes have been proven to be one
such problem, and now more than ever, when any individual can create a fake
image or video simply using an application on the smartphone, there need to be
some countermeasures, with which we can detect if the image or video is a fake
or real and dispose of the problem threatening the trustworthiness of online
information. Although the Deep fakes created by neural networks, may seem to be
as real as a real image or video, it still leaves behind spatial and temporal
traces or signatures after moderation, these signatures while being invisible
to a human eye can be detected with the help of a neural network trained to
specialize in Deep fake detection. In this paper, we analyze several such
states of the art neural networks (MesoNet, ResNet-50, VGG-19, and Xception
Net) and compare them against each other, to find an optimal solution for
various scenarios like real-time deep fake detection to be deployed in online
social media platforms where the classification should be made as fast as
possible or for a small news agency where the classification need not be in
real-time but requires utmost accuracy. |
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DOI: | 10.48550/arxiv.2106.12605 |