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Using GANs to synthesise minimum training data for deepfake generation

Singh, Simranjeet, Sharma, Rajneesh and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2020) Using GANs to synthesise minimum training data for deepfake generation. In: AICS 2020 Irish Conference on Artificial Intelligence and Cognitive Science, 7-8 Dec 2020, Dublin, Ireland (Online).

Abstract
There are many applications of Generative Adversarial Networks (GANs) in fields like computer vision, natural language processing, speech synthesis, and more. Undoubtedly the most notable results have been in the area of image synthesis and in particular in the generation of deepfake videos. While deepfakes have received much negative media coverage, they can be a useful technology in applications like entertainment, customer relations, or even assistive care. One problem with generating deepfakes is the requirement for a lot of image training data of the subject which is not an issue if the subject is a celebrity for whom many images already exist. If there are only a small number of training images then the quality of the deepfake will be poor. Some media reports have indicated that a good deepfake can be produced with as few as 500 images but in practice, quality deepfakes require many thousands of images, one of the reasons why deepfakes of celebrities and politicians have become so popular. In this study, we exploit the property of a GAN to produce images of an individual with variable facial expressions which we then use to generate a deepfake. We observe that with such variability in facial expressions of synthetic GAN-generated training images and a reduced quantity of them, we can produce a near-realistic deepfake videos.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Deepfake generation; Generative Adversarial Networks; GANs; Variable face images
Subjects:Computer Science > Machine learning
Computer Science > Multimedia systems
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of AICS2020. CEUR Workshop Proceedings 2771. CUER-WS.
Publisher:CUER-WS
Official URL:http://ceur-ws.org/Vol-2771/AICS2020_paper_20.pdf
Copyright Information:© 2020 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland under grant number SFI/12/RC/2289 P2, European Regional Development Fund.
ID Code:25164
Deposited On:14 Dec 2020 17:26 by Alan Smeaton . Last Modified 10 Feb 2021 13:20
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