Proceedings of the 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)

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Deepfake Detection using Hybrid Model for Trust of Citizens

Authors
Jayshree Ghorpade-Aher1, *, Raina Basu1, Siddharth Patil1, Keshav Jha1
1Department of Computer Engineering and Technology, School of CSE, Dr. Vishwanath Karad MIT World Peace University, Pune, Maharashtra, India
*Corresponding author. Email: jayshree.aher@mitwpu.edu.in
Corresponding Author
Jayshree Ghorpade-Aher
Available Online 25 June 2025.
DOI
10.2991/978-94-6463-740-3_13How to use a DOI?
Keywords
Machine Learning; Deep Learning; Images processing; Cybersecurity
Abstract

The accelerated advancement of deepfake technology presents considerable challenges to the security of digital media, leading to serious concerns due to the potential for misleading information, manipulation, and malicious use. As deepfakes become increasingly advanced and realistic, the need for effective detection mechanisms has grown substantially. This paper explores the emerging field of deepfake detection, focusing on a comprehensive model that integrates various techniques, including EfficientNet and Long Short-Term Memory (LSTM) networks. By utilizing multi-modal approaches that incorporate video data, proposed research work aims to identify subtle inconsistencies in deepfake content. This paper reviews state-of-the-art methods in deepfake detection, discussing their strengths and limitations while highlighting the necessity for hybrid approaches that combine multiple models to enhance detection accuracy. The paper concludes by identifying open challenges and proposing potential research directions to further improve the reliability of deepfake detection systems.

Copyright
© 2025 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 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)
Series
Advances in Intelligent Systems Research
Publication Date
25 June 2025
ISBN
978-94-6463-740-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-740-3_13How to use a DOI?
Copyright
© 2025 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  - Jayshree Ghorpade-Aher
AU  - Raina Basu
AU  - Siddharth Patil
AU  - Keshav Jha
PY  - 2025
DA  - 2025/06/25
TI  - Deepfake Detection using Hybrid Model for Trust of Citizens
BT  - Proceedings of the 6th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR 2024)
PB  - Atlantis Press
SP  - 138
EP  - 148
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-740-3_13
DO  - 10.2991/978-94-6463-740-3_13
ID  - Ghorpade-Aher2025
ER  -