Deepfake threats in digital security : Detection and prevention mechanisms in networked environments
Vaibhav Bangavaibhav.banga@nokia.comNokia CorporationBengaluru, Karnataka, 560045, IndiaView full profile → , Netra Jainjainetra9@gmail.comDepartment of Computer ScienceApaji Institute of Mathematics & Applied Computer Technology (AIM & ACT)Banasthali VidyapithBanasthali, Rajasthan, 304022, IndiaView full profile → , Amit Hirawatamit.hirawat@poornima.edu.inFaculty of Computer Science & EngineeringPoornima UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , *Rekha JainCorresponding authorrekha.jain@jaipur.manipal.eduDepartment of Computer ApplicationsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Arpana Sinhalarpana.sinhal@jaipur.manipal.eduDepartment of Computer ApplicationsManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JDMSC-3090
- Pages:
- 4765–4773
Abstract
Deepfake Technology is developed by both computer vision research and the deep learning that has evolved as a spin-off from the advances made in this area. Deepfakes are classified as artificial digital media (including video, photography, and audio) and are created with a high degree of accuracy to replicate the appearance, voice, and actions of human beings. However, deepfakes also can pose a very real threat to an individual’s or organization’s privacy, data protection, and authenticity of digital content. In addition to having beneficial uses, in the last few years there have been many cases of using deepfakes for malicious reasons including, but not limited to, misrepresentation and misinformation, identity fraud, social engineering, and discrediting individuals and/or companies.
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References
[1] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 27, pp. 2672–2680 (2014).
[2] A. Rössler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Niessner, “FaceForensics++: Learning to detect manipulated facial images,” in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), pp. 1–11 (2019), doi: 10.1109/ICCV.2019.00009.
[3] B. Dolhansky, J. Bitton, B. Pflaum, J. Lu, R. Howes, M. Wang, and C. Canton Ferrer, “The DeepFake Detection Challenge (DFDC) dataset,” arXiv preprint arXiv:2006.07397 (2020).
[4] D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen, “MesoNet: A compact facial video forgery detection network,” in Proc. IEEE Int. Workshop Inf. Forensics Secur. (WIFS), pp. 1–7 (2020), doi: 10.1109/WIFS49906.2020.9360895.
[5] Y. Li and S. Lyu, “Exposing deepfake videos by detecting face warping artifacts,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. Workshops (CVPRW), pp. 46–52 (2020), doi: 10.1109/CVPRW50498.2020.00016.
[6] A. Haliassos, K. Vougioukas, S. Petridis, and M. Pantic, “Lips don’t lie: A generalisable and robust approach to face forgery detection,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 5039–5049 (2022), doi: 10.1109/CVPR52688.2022.00500.
[7] A. H. Soudy, O. Sayed, H. Tag-Elser, M. A. Abdelrahman, and M. A. Moustafa, “Deepfake detection using convolutional vision transformers and convolutional neural networks,” Neural Comput. Appl., vol. 36, pp. 19759–19775 (2024), doi: 10.1007/s00521-024-10181-7.
[8] D. P. Kingma and M. Welling, “Auto-encoding variational Bayes,” in Proc. Int. Conf. Learn. Representations (ICLR), pp. 1–14 (2014).
[9] R. Jain, B. Batra, B. Kanwer, L. Raja, and V. Bhatnagar, “Transforming network security through zero trust architecture: Principles, challenges, and future directions,” J. Discrete Math. Sci. Cryptogr., vol. 29, no. 2-B, pp. 1097–1106 (2026), doi: 10.47974/JDMSC-2649.
[10] S. Sharma, M. Sharma, V. Bhatnagar, and R. Jain, “Utilizing discrete structure and applied algebra to model and analysis network resilience against cyber attack,” J. Discrete Math. Sci. Cryptogr., vol. 29, no. 2-B, pp. 885–893 (2026), doi: 10.47974/JDMSC-2539.
