Authors : Anagha Thorat; Bhagyashree Kadam; Pratiksha Rampure; Shreya Patil; Vijay Sonawane

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/ypsh5rtt

DOI : https://doi.org/10.38124/ijisrt/25may1847

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Deep learning has revolutionized various complex tasks, including image interpretation, autonomous system control, and large-scale data analysis. However, its advancements have also facilitated the development of sophisticated tools capable of generating highly realistic yet fraudulent media content, known as deepfakes. These AI-generated images and videos can convincingly mimic real individuals, raising significant concerns regarding national security, democratic integrity, and personal privacy. Consequently, there is an urgent need for intelligent detection systems that can effectively identify and verify the authenticity of digital media. Such systems are crucial for distinguishing between genuine and manipulated content, ensuring the reliability of information, and preventing the dissemination of misleading visuals. This paper delves into the methodologies employed in creating prominent deepfakes and reviews the current literature on detection strategies. Furthermore, it discusses the inherent challenges posed by deepfake technologies and outlines prospective avenues for future research aimed at developing more robust and trustworthy detection mechanisms.

Keywords : Deep Learning, CNN, Pre - Processing, Feature Extraction, Face Detection and Face Recognition.

References :

1. Rana, Md Shohel, Mohammad Nur Nobi, Beddhu Murali, and Andrew H. Sung. “Deepfake detection: A systematic literature review.” IEEE access 10 (2022): 25494-25513.
2. Lewis, John K., Imad Eddine Toubal, Helen Chen, Vishal Sandesera, Michael Lomnitz, Zigfried Hampel- Arias, Calyam Prasad, and Kannappan Palaniappan. “Deepfake video detection based on spatial, spectral, and temporal inconsistencies using multimodal deep learning.” In 2020 4. IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1- 9. IEEE, 2020.
3. Trinh, Loc, Michael Tsang, Sirisha Rambhatla, and Yan Liu. “Interpretable and trustworthy deepfake detection via dynamic prototypes.” In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 1973-1983. 2021.
4. S. P and S. Sk, "DeepFake Creation and Detection:A Survey," 2021 Third International Conference on Inventive  Research  in Computing Applications.
5. S. R. B. R, P. Kumar Pareek, B. S and G. G, "Deepfake Video Detection System Using Deep Neural
6. Networks," 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2023, pp. 1-6, doi: 10.1109/ICICACS57338.2023.10099618.
7. M. S. Rana, B. Murali and A. H. Sung, "Deepfake Detection Using Machine Learning Algorithms," 2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI), Niigata, Japan, 2021, pp. 458- 463, doi: 10.1109/IIAI-AAI53430.2021.00079.
8. Swathi, P., and Saritha Sk. "Deepfake creation and detection: A survey." In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 584-588. IEEE, 2021.
9. Mahmud, Bahar Uddin, and Afsana Sharmin. "Deep insights of deepfake technology: A review." arXiv preprint
10. Busacca, Angela, and Melchiorre Alberto Monaca. "Deepfake: Creation, purpose, risks." In Innovations and Economic and Social Changes due to Artificial Intelligence: The State of the Art, pp. 55-68. Cham: Springer Nature Switzerland, 2023.
11. Chadha, Anupama, Vaibhav Kumar, Sonu Kashyap, and Mayank Gupta. "Deepfake: an overview."
12. In Proceedings of second international conference on computing, communications, and cyber-security: IC4S 2020, pp. 557-566. Springer Singapore, 2021.
13. Seow, Jia Wen, Mei Kuan Lim, Raphaël CW Phan, and Joseph K. Liu. "A comprehensive overview of Deepfake: Generation, detection, datasets, and opportunities." Neurocomputing 513 (2022): 351-371.
14. Westerlund, Mika. "The emergence of deepfake technology: A review." Technology innovation management review 9, no. 11 (2019).
15. Feeney, Matthew. "Deepfake laws risk creating more problems than they solve." Regulatory Transparency Project (2021).
16. Abu-Ein, Ashraf A., Obaida M. Al-Hazaimeh, Alaa
17. M. Dawood, and Andraws I. Swidan. "Analysis of the current state of deepfake techniques- creation and detection methods." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (2022): 1659-1667.
18. 16.      Yadav, Digvijay, and Sakina Salmani. "Deepfake: A    survey on facial forgery technique using generative adversarial network." In 2019   International conference on intelligent computing and control systems (ICCS), pp. 852-857. IEEE, 2019.
19. Shivale, N.M., Mahajan, R.A., Bhandari, G.M., Sonawane, V.D., Kulkarni, M.M., Patil, S.S., “Optimizing Blockchain Protocols with Algorithmic Game Theory”, Advances in Nonlinear Variational Inequalities, 2024, 27(4), pp. 231–246.
20. Sonawane, V.D., Mahajan, R.A., Patil, S.S., Bhandari, G.M., Shivale, N.M., Kulkarni, M.M., “Predicting Software Vulnerabilities with Advanced Computational Models”, Advances in Nonlinear Variational Inequalities, 2024, 27(4), pp. 196–212.
21. Kulkarni, M.M., Mahajan, R.A., Shivale, N.M., Patil, S.S., Bhandari, G.M., Sonawane, V.D., “Enhancing Social Network Analysis using Graph Neural Networks”, Advances in Nonlinear Variational Inequalities, 2024, 27(4), pp. 213–230.