An Investigation of the Effectiveness of Deepfake Models and Tools

With the development of computer vision and deep learning technologies, rapidly expanding approaches have been introduced that allow anyone to create videos and pictures that are both phony and incredibly lifelike. The term deepfake methodology is used to describe such technologies. Face alteration...

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Bibliographic Details
Published inJournal of sensor and actuator networks Vol. 12; no. 4; p. 61
Main Authors Mukta, Md. Saddam Hossain, Ahmad, Jubaer, Raiaan, Mohaimenul Azam Khan, Islam, Salekul, Azam, Sami, Ali, Mohammed Eunus, Jonkman, Mirjam
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2023
Subjects
Algorithms
autoencoder
CNN
Computer vision
Datasets
Deception
Deep learning
deepfake
Evolution
GANs
Machine vision
Neural networks
Pictures
Political aspects
Privacy
RNN
Taxonomy
Technology
Video
Germany
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Summary:With the development of computer vision and deep learning technologies, rapidly expanding approaches have been introduced that allow anyone to create videos and pictures that are both phony and incredibly lifelike. The term deepfake methodology is used to describe such technologies. Face alteration can be performed both in videos and pictures with extreme realism using deepfake innovation. Deepfake recordings, the majority of them targeting politicians or celebrity personalities, have been widely disseminated online. On the other hand, different strategies have been outlined in the research to combat the issues brought up by deepfake. In this paper, we carry out a review by analyzing and comparing (1) the notable research contributions in the field of deepfake models and (2) widely used deepfake tools. We have also built two separate taxonomies for deepfake models and tools. These models and tools are also compared in terms of underlying algorithms, datasets they have used and their accuracy. A number of challenges and open issues have also been identified.
ISSN:2224-2708
2224-2708
DOI:10.3390/jsan12040061