Quick Overview of Face Swap Deep Fakes

Author:

Walczyna Tomasz1ORCID,Piotrowski Zbigniew1ORCID

Affiliation:

1. Faculty of Electronics, Military University of Technology, 00-908 Warszawa, Poland

Abstract

Deep Fake technology has developed rapidly in its generation and detection in recent years. Researchers in both fields are outpacing each other in their axes achievements. The works use, among other methods, autoencoders, generative adversarial networks, or other algorithms to create fake content that is resistant to detection by algorithms or the human eye. Among the ever-increasing number of emerging works, a few can be singled out that, in their solutions and robustness of detection, contribute significantly to the field. Despite the advancement of emerging generative algorithms, the fields are still left for further research. This paper will briefly introduce the fundamentals of some the latest Face Swap Deep Fake algorithms.

Funder

the National Centre for Research and Development

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference65 articles.

1. Swathi, P., and Saritha, S.K. (2021, January 2–4). DeepFake Creation and Detection: A Survey. Proceedings of the 3rd International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India.

2. (2023, May 03). Deepfakes Deepfakes_Faceswap. Available online: https://github.com/deepfakes/faceswap.

3. Perov, I., Gao, D., Chervoniy, N., Liu, K., Marangonda, S., Umé, C., Dpfks, M., Facenheim, C.S., RP, L., and Jiang, J. (2021). DeepFaceLab: Integrated, flexible and extensible face-swapping framework. arXiv.

4. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Networks. arXiv.

5. Mahmud, B.U., and Sharmin, A. (2023). Deep Insights of Deepfake Technology: A Review. arXiv.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3