Vinay Kumar Srivastava received the BE degree in Electronics and Telecommunication Engineering from Govt Engineering College Rewa, MP, India in 1989, the M Tech degree in Communication Engineering from IIT BHU, Varanasi, India in 1991 and PhD degree in Electrical Engineering from IIT Kanpur, India in 2001. After spending a brief period in Indian Telephone Industries Limited, Naini, Allahabad as Assistant Executive engineer, he joined Motilal Nehru National Institute of Technology (MNNIT) Allahabad, India, as a Lecturer in 1992, where he became an Assistant Professor in 2001, Associate Professor in 2006 and Professor in 2010. He has about thirty years of teaching and research has supervised a number of B Tech projects, fifty M Tech Theses and eight PhD Theses.
EDUCATION
Undergraduate: B.E. (Electronics & Telecommunication) Govt. Engineering College Rewa, MP, 1989
Postgraduate: M. Tech. (Communication Systems) Indian Institute of Technology, BHU (IIT-BHU), Varanasi, 1991
Doctoral: Ph. D. EE/IIT Kanpur, 2001 Thesis Title: Post-processing of DCT Coded Images
RESEARCH, TEACHING, or OTHER INTERESTS
Signal Processing, Electrical and Electronic Engineering
Deep Learning-Based Data Hiding Techniques for Secure Communication: A Comprehensive Review Divyanshu Awasthi, Anurag Tiwari, Priyank Khare, Vinay Kumar Srivastava Concurrency and Computation Practice and Experience, 2026 The advent of the modern technological era has allowed us to reach a stage where people can share their information via different platforms quite easily. These platforms allow users to express themselves through text, photographs, videos, and audio, among other different representational media. The amount of photographic data is higher in comparison with other forms of data. So, the security of these images is a big concern for the researchers. Deep learning (DL)‐based approaches have gained popularity for a variety of multimedia analysis applications, including segmentation, detection, and classification. This article presents a state‐of‐the‐art summarization of DL‐based multimedia security techniques in which various encryption techniques, covert operations, current challenges, the scope of improvement, and new directions are highlighted. This paper mentions a comprehensive review of different DL watermarking and hiding techniques, along with a comparison of the contributions of the literature. This survey, in our opinion, can open the door to further investigating the essential topic of information concealment in DL environments.
Identifying Rice Transplantation Dates using Sentinel-1 Synthetic Aperture Radar data and Machine Learning Sudheer Kumar Tiwari, Vinay Kumar Srivastava, S. Agrawal, Raghvendra Singh Journal of Scientific and Industrial Research, 2026 This study evaluates the effectiveness of Sentinel-1 Synthetic Aperture Radar (SAR) data, acquired at a 10-m spatial resolution with 12-day intervals, for accurately mapping rice transplantation dates during the Kharif season of 2023 in Kakinada district, Andhra Pradesh. SAR’s ability to penetrate clouds and provide all-weather observations was particularly valuable for tracking rice cultivation under monsoon conditions. The study integrated the Random Forest algorithm to classify rice crop pixels, achieving precise identification of transplantation dates using Google Earth Engine (GEE) platform. A comparison between satellite-based estimates and the Agriculture Department's records demonstrated strong alignment. Sentinel-1 SAR observations from 28th July, 9th August, 21st August, and 2nd September 2023 closely matched Agriculture Department records from 2nd August, 9th August, 23rd August, and 6th September. On 2nd August, satellite data estimated 44,672.42 hectares, compared to the Department’s 43,414 hectares with a 2.9% deviation. By 9th August, satellite estimates were 61,199.22 hectares, while the Department’s estimation was 62,563 hectares, showing a −2.18% deviation. By 23rd August, estimates reached 81,064.94 hectares, with the Department recording 81,889 hectares with a −1.01% difference. Finally, on 6th September, the satellite estimate was 84,049.53 hectares, closely aligning with the Department’s 83,685 hectares, reflecting a minimal 0.44% deviation. These minor variations, likely due to timing or reporting differences, underscore the reliability of SAR data for near-real-time monitoring. Accurately identifying transplantation dates and mapping is crucial, as it significantly aids in the precise estimation of rice crop biomass, which is a key parameter for forecasting rice yields.
Smart Healthcare Security with Moment-Based DICOM Image Protection Anurag Tiwari, Divyanshu Awasthi, Vinay Kumar Srivastava Multimedia and Multimodal Intelligence for Sustainable Development, 2026 The substantial volume of medical data that must be effectively kept, retrieved, and transported, as well as the rising number of security risks that must be carefully addressed, is creating new problems for healthcare information management. In order to protect the security and integrity of DICOM (Digital Imaging and Communications in Medicine) images, the presented work suggests a medical image watermarking (MIW) technology based on Schur Decomposition (SD) and Pseudo Zernike Moment (PZM). By hiding the watermark into the Schur domain, along with the utilization of PZM properties like affine resistance, the proposed method ensures better performance in terms of various essential properties of MIW, like imperceptibility and robustness. The successful extraction of the watermark demonstrates that this approach is suitable for authentication and copyright protection of various multimedia content. Furthermore, the watermarked image is authenticated by comparing its features with those of the original cover image using the FAST (Features from Accelerated Segment Test) key-points matching approach. Moreover, comprehensive experimental findings show that the approach offers improved resilience against a range of affine transformations and attacks. The achieved values of performance parameters demonstrate the effectiveness of the proposed method. This method improves security and resistance against threats by 20% compared to existing methods, ensuring it is a suitable option for the protection of DICOM images in smart healthcare systems.
SFIWMark: Secure Fundus Image Watermarking Scheme for Smart Healthcare Applications Anurag Tiwari, Vijay Birchha, Vinay Kumar Srivastava International Conference on Innovative Practices in Technology and Management Iciptm 2026, 2026 The increasing volume of medical data shows significant challenges for different healthcare information management (HIM), particularly in the form of image security, its accessibility, authentication, reliability and its protections against different security threats. In the proposed work, a Fundus medical image watermarking (FMIW) scheme based on Redundant Discrete Wavelet Transform (RDWT) and Multiple Matrix Decomposition (MMD) is suggested in which additionally chaotic encryption of watermark (Wmark) image is done to further enhance the security. RDWT enhances the robustness against various types of attacks, while multiple matrix decomposition (MMD) improves the invisibility of Wmark along with the robustness against affine attacks. In this approach, a FUNDUS image of patient used as cover image, and the patient ID i.e. 'Aadhar' image is used as Wmark image. The 'Aadhar' image is divided into two halves, each of which is separately encrypted using a Henon map chaotic encryption scheme and used as a Wmark, thereby enhancing the overall security of the system. The proposed method achieves a Peak Signal-to-Noise Ratio (PSNR) exceeding 40 dB, indicating high imperceptibility of the Wmark. The Wmark is later extracted and reconstructed by merging the two halves. Experimental results demonstrate that the proposed technique outperforms existing state-of-the-art methods, offering approximately 20% improvement in robustness against various attacks.
EEGmark: LWT and Dual Decomposition-Based Secure EEG Watermarking with Optimization Ranjana Dwivedi, Divyanshu Awasthi, Vinay Kumar Srivastava Multimedia and Multimodal Intelligence for Sustainable Development, 2026 Transmission of biomedical signals across a network that is both secure and effective while carrying sensitive and vital patient health data is difficult. In this work, an optimized, secure, and robust watermarking technique for Electroencephalogram (EEG) data is proposed. EEG recordings are applied with 3-level lifting wavelet transform (LWT) to divide into sub-bands (SBs). Two-level LWT is utilized to pre-process the grayscale watermark (WM) before embedding. A suitable strength factor is obtained by using the Manta Ray Foraging Optimization (MRFO) technique to make a balance between resilience and visual similarity. A chaotic map is used to encrypt the WM before embedding to provide security to the proposed scheme. Hessenberg decomposition (HD) and singular value decomposition (SVD) are applied on the SB of the host EEG. The performance of the proposed work is assessed in terms of robustness and imperceptibility (IPY). Parameters such as peak signal-to-noise ratio (PSNR), structural similarity measure index (SSIM), Kullback–Leibler Divergence (KL DIV), Jensen–Shannon Divergence (JS DIV), Percentage Residual Difference (PRD), and normalized correlation coefficient (NC) are used to analyze the performance. To analyze the robustness of the suggested scheme, various types of attacks are applied on the watermarked EEG. These attacks include geometric attacks, image processing attacks, filtering attacks, and noise attacks. Simulation results demonstrate that the presented work attains a PSNR value above 58 dB with an SSIM value higher than 0.9998. Comparisons with existing schemes demonstrate that the suggested scheme outperforms existing watermarking techniques.
A Hybrid Digital Filter Based Framework for Enhanced Exon Prediction Amit kumar Singh, Anurag Tiwari, Pratosh Kumar Pal, Vinay Kumar Srivastava 2025 IEEE 12th Uttar Pradesh Section International Conference on Electrical Electronics and Computer Engineering Upcon 2025, 2025
WideBand Wearable Antenna for ISM Band Pooja Sharma, Vinay Kumar, V. S. Tripathi IEEE Antennas and Propagation Society AP S International Symposium Digest, 2024
Link energy minimization in magnetic induction based non-conventional wsns A Laxmi Prasanna, Vinay Kumar, Sadanand Yadav, Sanjay B Dhok, Pankaj Dhule, L. Wuttisittikulkij, Vitawat Sittakul Proceedings of the 16th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2019, 2019
MistGIS: optimizing geospatial data analysis using mist computing Rabindra K. Barik, Ankita Tripathi, Harishchandra Dubey, Rakesh K. Lenka, Tanjappa Pratik, Suraj Sharma, Kunal Mankodiya, Vinay Kumar, Himansu Das Advances in Intelligent Systems and Computing, 2018
GPS and GSM based rail signaling and tracking system Muddana Tarun, Vinay Kumar, Sudhir Kumar, Mukunda Ujwal Jajoo, Saif Ur Rahman, Joydeep Sengupta 2017 4th International Conference on Control Decision and Information Technologies Codit 2017, 2017
Mobile phone user's speed estimation using WiFi signal-to-noise ratio Pavan Kumar Pedapolu, Pradeep Kumar, Vaidya Harish, Satvik Venturi, Sushil K. Bharti, Vinay Kumar, Sudhir Kumar Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing Mobihoc, 2017
Design of 2D-multiple notch filter and its application in reducing blocking artifact from DCT coded image Annual International Conference of the IEEE Engineering in Medicine and Biology Proceedings, 2000