A multi-level trusted security framework for high-fidelity GAN video deepfake detection in real-time systems
*Sanjay KumarCorresponding authorsnjay1991@gmail.comDepartment of Basic Sciences and HumanitiesPranveer Singh Institute of TechnologyKanpur, Uttar Pradesh, 209305, IndiaView full profile → , Deepak Awasthideepak.awasthi@psit.ac.inDepartment of Mechanical EngineeringPranveer Singh Institute of TechnologyKanpur, Uttar Pradesh, 209305, IndiaView full profile → , Ravi Krishan Pandeyprofravikp@gmail.comDepartment of Computer Science & EngineeringFaculty of Engineering and TechnologyUniversity of LucknowLucknow, Uttar Pradesh, 226007, IndiaView full profile → , Arun Kumar Uttamarun.uttam@gmail.comDepartment of Computer ApplicationsPSIT College of Higher EducationKanpur, Uttar Pradesh, India, 209305View full profile → , Shashi Kant Guptadr.gshashikant@gmail.comDepartment of Computer Science and ApplicationsITM UniversityGwalior, Madhya Pradesh, 475001, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2026
- Published Online:
- 31 Aug 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2357
- Pages:
- 3063–3072
Abstract
High-fidelity GANs enable realistic deepfakes, increasing risks of misinformation and fraud. This paper proposes a multi-level trusted framework combining spatio-temporal forensics, hybrid classification, and cryptographic validation for real-time detection. Results show improved accuracy, robustness, and low latency, offering a secure, scalable solution for applications like digital forensics, authentication, surveillance, and social media verification.
Keywords
Subject Classifications
References
[1] K. N. Ramadhani, R. Munir, and N. P. Utama, “Improving video vision transformer for deepfake video detection using facial landmark, depthwise separable convolution and self-attention,” IEEE Access, vol. 12, pp. 8932–8939 (2024), doi: 10.1109/ACCESS.2024.3352890.
[2] U. Sharma and J. Singh, “A comprehensive overview of fake news detection on social networks,” Soc. Netw. Anal. Min., vol. 14, no. 1 (2024), doi: 10.1007/s13278-024-01280-3.
[3] C. Hu, X. Xie, and L. Wu, “Face reenactment via generative landmark guidance,” Image Vis. Comput., vol. 130, Art. no. 104611 (2023), doi: 10.1016/j.imavis.2022.104611.
[4] Z. Xue, X. Jiang, Q. Liu, and Z. Wei, “Global–local facial fusion based GAN generated fake face detection,” Sensors, vol. 23, no. 2, Art. no. 616 (2023), doi: 10.3390/s23020616.
[5] S. Waseem, “DeepFake on face and expression swap: A review,” IEEE Access, vol. 11, pp. 117865–117906 (2023), doi: 10.1109/ACCESS.2023.3324403.
[6] M. K. Aulakh, N. Kanwal, and M. Bansal, “A systematic literature review of video forgery detection techniques,” Multim. Tools Appl., vol. 84, no. 33, pp. 41277–41327 (2025), doi: 10.1007/s11042-025-20769-3.
[7] B. N. Al-din Abed, J. Karimpour, and F. Mahan, “Deep fake detection system based on improvement graph convolutional network,” J. Inf. Optim. Sci., vol. 47, no. 1, pp. 287–298 (2026), doi: 10.47974/JIOS-2038.
[8] S. Kalra, Y. Bansal, Y. Sharma, and G. S. Chauhan, “FakeSpotter: A blockchain-based trustworthy idea for fake news detection in social media,” J. Inf. Optim. Sci., vol. 44, no. 3, pp. 515–527 (2023), doi: 10.47974/JIOS-1411.
[9] T. Gopalakrishnan, H. M. A. Ghanimi, T. Alam, K. Sreekanth, K. E. Purushothaman, S. Sengan, and P. Dadheech, “Leveraging blockchain technology to combat deception, deepfake, and counterfeit system,” J. Discrete Math. Sci. Cryptogr., vol. 27, no. 7, pp. 2143–2154 (2024), doi: 10.47974/JDMSC-2087.
[10] Z. A. Jawad and A. J. Obaid, “A systemic literature overview of Fake News Challenge (FNC-1) dataset and its use in fake news detection schemes,” J. Discrete Math. Sci. Cryptogr., vol. 26, no. 4, pp. 1197–1206 (2023), doi: 10.47974/JDMSC-1567.
[11] P. Vats, S. K. Vats, and P. Peddi, “Unveiling crypto analysis secrets: A comprehensive analysis of smart contract security within blockchain network environments,” J. Discrete Math. Sci. Cryptogr., vol. 27, no. 4, pp. 1121–1128 (2024), doi: 10.47974/JDMSC-1967.
[12] G. M. Upadhyay, S. Shanker, V. Kumar, Vishal, P. Vats, and L. Raja, “Enhancing key distribution via discrete mathematical structures in cryptosystems,” J. Discrete Math. Sci. Cryptogr., vol. 28, no. 6, pp. 2515–2526 (2025), doi: 10.47974/JDMSC-2336.
