Dr. Shobhit Tyagi - ASSISTANT PROFESSOR (SENIOR GRADE) | JIIT Noida

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Dr. Shobhit Tyagi

Biography

Dr. Shobhit Tyagi is currently working as an Assistant Professor (Sr. Grade) in the Department of Computer Science & Engineering and Information Technology at Jaypee Institute of Information Technology, Noida. He earned his Ph.D. in Computer Science & Engineering from the National Institute of Technology, Hamirpur in 2023 with a CGPA of 8.6, following his M.Tech in CSE (2019) from NIT Hamirpur and B.Tech in CSE (2017) from FGIET, Raebareli. Dr. Tyagi has over three years of teaching and research experience, having previously served as an Assistant Professor at Sharda University, Greater Noida. His research expertise lies in deep learning, computer vision, multimedia forensics, and image/video forgery detection. During his Ph.D., he worked extensively on designing efficient convolutional neural network architectures for detecting fake and manipulated

visual media in real-time.

Educational Qualifications

Ph.D., M.Tech, B.Tech

Research Highlights

Dr. Tyagi’s research primarily focuses on multimedia forensics, particularly in developing robust and efficient methods for detecting fake and manipulated content in images and videos. His doctoral research led to the creation of ForensicNet and MiniNet, two convolutional neural network-based architectures optimized for forgery detection with reduced computational complexity, enabling near real-time deployment. He has published in reputed journals such as The Visual Computer, Journal of Forensic Sciences, and Evolving Systems, and has authored a comprehensive review on generative adversarial networks in Archives of Computational Methods in Engineering. Dr. Tyagi has cleared competitive examinations like GATE and UGC-NET and has received academic recognition including 2nd rank in Ph.D. coursework and 3rd rank in M.Tech coursework at NIT Hamirpur.

His work on image synthesis and forgery detection has contributed to understanding the capabilities and risks associated with generative adversarial networks (GANs). By systematically reviewing literature and proposing new lightweight architectures, his research addresses challenges in detecting deepfake content under real-world constraints such as compression, noise, and partial occlusions.

Areas of Interest

Multimedia Forensics
Deepfake Detection
Computer Vision
Deep Learning Architectures

Publications

M. Kalra, S. Tyagi, V. Kumar, M. Kaur, W. K. Mashwani, H. Shah, and K. Shah, “A
comprehensive review on scatter search: Techniques, applications, and challenges,”
Mathematical Problems in Engineering, vol. 2021, pp. 1–21, 2021.
S. Tyagi and D. Yadav, “A comprehensive review on image synthesis with adversarial
networks: Theory, literature, and applications,” Archives of Computational Methods in
Engineering, 2021.
S. Tyagi and D. Yadav, “A detailed analysis of image and video forgery detection
techniques,” The Visual Computer, 2022.
S. Tyagi and D. Yadav, “ForensicNet: Modern convolutional neural network-based image
forgery detection network,” Journal of Forensic Sciences, vol. 68, no. 2, pp. 461–469, 2023.
S. Tyagi and D. Yadav, “MiniNet: A concise CNN for image forgery detection,” Evolving
Systems, vol. 14, no. 3, pp. 545–556, 2023.