Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)

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A Frame Work Designing for Deep Fake Motion Detection using Deep Learning in Video Surveillance Systems

Authors
Srikanth Bethu1, *, M. Ratna Sirisha1, C. Kothai Andal2, R. Gayathri3, H. Chandramouli4, R. Aruna5
1Department of CSE, CVR College of Engineering, Hyderabad, 501510, Telangana, India
2Department of EEE, AMC Engineering College, Bengaluru, 560083, Karnataka, India
3Department of ECE, Sree Dattha Institute of Engineering and Science, Hyderabad, 501510, Telangana, India
4Department of CSE, East Point College of Engineering and Technology, Bangalore, 560049, Karnataka, India
5Department of ECE, AMC Engineering College, Bengaluru, 560083, Karnataka, India
*Corresponding author. Email: srikanthbethu@gmail.com
Corresponding Author
Srikanth Bethu
Available Online 21 December 2023.
DOI
10.2991/978-94-6463-314-6_18How to use a DOI?
Keywords
Face Motion; Deep Learning; Deep Fake detection
Abstract

This exploration centers around constant perception of items in a given setting, which prompts a rundown of the ways of behaving or connections of the things. Because of the absence of time for robotized checking and examination, an administrator should watch a lot of video information in time with complete thoughtfulness regarding recognize any inconsistencies or episodes, or solely after the surprising occurrence has happened may the video information be utilized as proof. To overcome these issues, we have developed a Deep Learning model to detect face objects in real-time to identify their motion for fake detection using Video Surveillance systems. We have also compared our model with the existing models, and we can probably secure high accuracy of 95.4%.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
21 December 2023
ISBN
978-94-6463-314-6
ISSN
2589-4900
DOI
10.2991/978-94-6463-314-6_18How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Srikanth Bethu
AU  - M. Ratna Sirisha
AU  - C. Kothai Andal
AU  - R. Gayathri
AU  - H. Chandramouli
AU  - R. Aruna
PY  - 2023
DA  - 2023/12/21
TI  - A Frame Work Designing for Deep Fake Motion Detection using Deep Learning in Video Surveillance Systems
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 179
EP  - 187
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_18
DO  - 10.2991/978-94-6463-314-6_18
ID  - Bethu2023
ER  -