DeepFake Detection for Human Face Images and Videos: A Survey

Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish b...

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Vydáno v:IEEE Access Ročník 10; s. 18757 - 18775
Hlavní autoři: Malik, Asad, Kuribayashi, Minoru, Abdullahi, Sani M., Khan, Ahmad Neyaz
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2022
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial neural networks
CNNs
Computer & video games
Crime
Datasets
Deception
Deep learning
DeepFake
Electrical engineering. Electronics. Nuclear engineering
Faces
Forensics
GANs
Image manipulation
Information integrity
Kernel
Machine learning
Media
Motion pictures
Multimedia
TK1-9971
Videos
Visual effects
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí:Techniques for creating and manipulating multimedia information have progressed to the point where they can now ensure a high degree of realism. DeepFake is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish between real and fake features. This technology has greatly advanced and promotes a wide range of applications in TV channels, video game industries, and cinema, such as improving visual effects in movies, as well as a variety of criminal activities, such as misinformation generation by mimicking famous people. To identify and classify DeepFakes, research in DeepFake detection using deep neural networks (DNNs) has attracted increased interest. Basically, DeepFake is the regenerated media that is obtained by injecting or replacing some information within the DNN model. In this survey, we will summarize the DeepFake detection methods in face images and videos on the basis of their results, performance, methodology used and detection type. We will review the existing types of DeepFake creation techniques and sort them into five major categories. Generally, DeepFake models are trained on DeepFake datasets and tested with experiments. Moreover, we will summarize the available DeepFake dataset trends, focusing on their improvements. Additionally, the issue of how DeepFake detection aims to generate a generalized DeepFake detection model will be analyzed. Finally, the challenges related to DeepFake creation and detection will be discussed. We hope that the knowledge encompassed in this survey will accelerate the use of deep learning in face image and video DeepFake detection methods.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3151186