A Review on Deepfake Detection Techniques using Machine Learning and Deep Learning

Verma, Anmol; Sharma, Aadit; Tawar, Ayush; Dubey, Ansh; Electric, Abbas

doi:https://doi.org/10.55041/ijcope.v2i5.540

Published on: May 2026

A REVIEW ON DEEPFAKE DETECTION TECHNIQUES USING MACHINE LEARNING AND DEEP LEARNING

Anmol Verma Aadit Sharma Ayush Tawar Ansh Dubey Abbas Electric

Computer Science and Engineering Indore Institute of Science and Technology Rau Pithampur Road Indore 453331 Madhya Pradesh India

DOI:https://doi.org/10.55041/ijcope.v2i5.540

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Abstract

Deepfake technology has rapidly evolved with the advancement of artificial intelligence, enabling the creation of highly realistic synthetic audio and video content. While such technology has beneficial applications in entertainment and virtual reality, it also poses serious threats in terms of misinformation, identity fraud, and privacy violations. Consequently, deepfake detection has emerged as an important research area within cyber security and artificial intelligence. This paper presents a comprehensive review of deepfake detection techniques based on machine learning and deep learning approaches. Existing methods are analyzed with respect to detection strategies, datasets, and performance characteristics. A comparative analysis of prominent techniques is provided to highlight their strengths and limitations. Furthermore, major challenges such as generalization, dataset bias, and computational complexity are discussed along with potential future research directions. This review aims to provide a concise understanding of current advancements in deepfake detections and to serve as a reference for researchers and practitioners in this domain.

Keywords: Deepfake Detection, Machine Learning, Deep Learning, Cyber Security, Artificial Intelligence

How to Cite this Paper

Verma, A., Sharma, A., Tawar, A., Dubey, A. & Electric, A. (2026). A Review on Deepfake Detection Techniques using Machine Learning and Deep Learning. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.540

Verma, Anmol, et al.. "A Review on Deepfake Detection Techniques using Machine Learning and Deep Learning." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.540.

Verma, Anmol,Aadit Sharma,Ayush Tawar,Ansh Dubey, and Abbas Electric. "A Review on Deepfake Detection Techniques using Machine Learning and Deep Learning." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.540.

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References

[1] M. S. Rana, M. N. Nobi, B. Murali, and A. H. Sung, “Deepfake Detection: A Systematic Literature Review,” IEEE Access, vol. 10, pp. 25494–25513, 2022.

[2] A. Rössler et al., “FaceForensics++: Learning to Detect Manipulated Facial Images,” in Proc. IEEE Int. Conf. on Computer Vision (ICCV), 2019.

[3] B. Dolhansky et al., “The Deepfake Detection Challenge (DFDC) Preview Dataset,” arXiv:1910.08854, 2019.

[4] D. Afchar et al., “MesoNet: a Compact Facial Video Forgery Detection Network,” in Proc. IEEE Int. Workshop on Information Forensics and Security (WIFS), 2018.

[5] Y. Li and S. Lyu, “Exposing DeepFake Videos By Detecting Face Warping Artifacts,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019.

[6] U. A. Ciftci, I. Demir, and L. Yin, “FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals,” arXiv:1901.02212, 2019

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•Published on: May 19 2026

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