Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/1724 2019 2019 Sheetla college of education, Rohtak, Haryana, India Preeti Singh Faculty of Engineering and Technology agra College Agra Khyati Chaudhary VIPS-TC, School of engineering and technology, Delhi, India Gopal Chaudhary School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India Manju Khari Cybersecurity Department, Benedict College, Columbia, USA Bharat Rawal 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 2022 42 50 10.54216/JCIM.090204 https://www.americaspg.com/articleinfo/2/show/1724