APSIPA Transactions on Signal and Information Processing > Vol 11 > Issue 2

DeepFake and its Enabling Techniques: A Review

Rachael Brooks, Computer Science and Engineering, Santa Clara University, USA, Yefeng Yuan, Computer Science and Engineering, Santa Clara University, USA, Yuhong Liu, Computer Science and Engineering, Santa Clara University, USA, yhliu@scu.edu , Haiquan Chen, California State University, USA
 
Suggested Citation
Rachael Brooks, Yefeng Yuan, Yuhong Liu and Haiquan Chen (2022), "DeepFake and its Enabling Techniques: A Review", APSIPA Transactions on Signal and Information Processing: Vol. 11: No. 2, e40. http://dx.doi.org/10.1561/116.00000024

Publication Date: 28 Dec 2022
© 2022 R. Brooks, Y. Yuan, Y. Liu and H. Chen
 
Subjects
 
Keywords
Deep LearningImage AnimationHuman Pose TransferHuman Motion TransferDeepfake
 

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This is published under the terms of CC BY-NC.

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In this article:
Introduction 
Related Works 
Key Building Blocks of Existing Studies 
Supporting Techniques 
Human Pose Transfer 
Human Motion Transfer and Generation 
Deepfake Detection 
Future of Field 
Discussion and Conclusion 
References 

Abstract

Deepfake technology has been undoubtedly growing at a rapid pace since 2017. Particularly since using GAN architecture was popularized, research in this area has grown and seems to only be gaining momentum. One interesting area is animating images of full body humans using deep learning. This paper looks at the research done in this area and research that can influence it by looking at papers regarding human pose transfer, human motion transfer, and human motion generation. All of these types of papers have similar requirements, where a target pose must be abstracted to a skeleton and combined with appearance data from a source image to generate a result. The primary difference in the three types of research is whether or not there is motion in the result and whether that motion is given as an input or generated by the model. Overall, the research in this area is still new, and with the potential applications of this technology, both good and bad, there are many avenues of potential future research in this area in both creation and detection.

DOI:10.1561/116.00000024

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