aspg-2-1724-1791417480 20261007235800 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2022 9 2 Preeti Singh Sheetla college of education, Rohtak, Haryana, India Khyati Chaudhary Faculty of Engineering and Technology agra College Agra Gopal Chaudhary VIPS-TC, School of engineering and technology, Delhi, India Manju Khari School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India Bharat Rawal Cybersecurity Department, Benedict College, Columbia, USA Deepfake videos are a growing concern today as they can be used to spread misinformation and manipulate public opinion. In this paper, we investigate the use of different feature extraction techniques for detecting deepfake videos using machine learning algorithms. We explore three feature extraction techniques, including facial landmarks detection, optical flow, and frequency analysis, and evaluate their effectiveness in detecting deepfake videos. We compare the performance of different machine learning algorithms and analyze their ability to detect deepfakes using the extracted features. Our experimental results show that the combination of facial landmarks detection and frequency analysis provides the best performance in detecting deepfake videos, with an accuracy of over 95%. Our findings suggest that machine learning algorithms can be a powerful tool in detecting deepfake videos, and feature extraction techniques play a crucial role in achieving high accuracy. 2022 42 50 1724 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.090204 https://www.americaspg.com/journal/2/article/1724 https://www.americaspg.com/storage/11740684821.pdf