Decoding Deepfake Detection: Harnessing the Strengths of Traditional Machine Learning for Superior Accuracy

DOI

10.2991/978-94-6463-471-6_39How to use a DOI?

Keywords

Deep Learning; Fake Detection; InceptionResnetV2; VGG19; CNN; and Xception

Abstract

This comprehensive study delves into the dynamic landscape of deep learning applications, focusing on the burgeoning realm of deep fakes. Deep learning has seamlessly integrated into fields like natural language processing, machine learning, and computer vision, giving rise to innovative applications. However, the surge in deep fakes, sophisticatedly manipulated videos/images, has become a pressing concern. The nefarious applications of this technology, such as fake news, celebrity impersonations, financial scams, and revenge porn, pose significant threats in the digital realm. Particularly, public figures like celebrities and politicians are highly susceptible to the Deep fake detection challenge. This research systematically assesses both the production and detection aspects of deep fakes, employing diverse deep learning algorithms, including InceptionResnetV2, VGG19, CNN, and Xception. The evaluation, conducted on a Kaggle deep fake dataset, highlights Xception as the most accurate among the algorithms studied. As malicious uses of deep fakes escalate, the imperative for robust detection mechanisms intensifies to safeguard against potential societal consequences.

Copyright

© 2024 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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TY  - CONF
AU  - P. Yogendra Prasad
AU  - Maddula Lohitha
AU  - Gandikota Sairam Roopak
AU  - Dommaraju Sai Harshini
AU  - Kandipati Sumanth
PY  - 2024
DA  - 2024/07/30
TI  - Decoding Deepfake Detection: Harnessing the Strengths of Traditional Machine Learning for Superior Accuracy
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 392
EP  - 408
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_39
DO  - 10.2991/978-94-6463-471-6_39
ID  - Prasad2024
ER  -