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Manoj Devare

Work place: Institute AIIT-Amity University, Mumbai, 410206, India

E-mail: mhdevare@mum.amity.edu

Website: https://orcid.org/0000-0002-9530-3914

Research Interests:

Biography

Manoj Devare, he is academic leader, PhD Supervisor and Administrator of AIIT School at Amity University Mumbai. His contributions includes instrumental role in the Ranking of the AUM as a Nodal Officer of Ranking at QS Asia, WUR, QS Sustainability, THE World Impact Ranking 2022, 2023, 2024, and Atal Ranking (NIRF Innovation 2021 & 2022). He has worked as the Secretary of the IQAC, Accreditation, Quality Assurance, policy development, and Ranking at AUM. Dr. Manoj is strategic planner, Chairman of BOS for IT & Doctoral Research Committee for the AIIT School. He has majorly contributed in the curriculum development, student affairs of PG, and UG Courses of the Computer Science and Information Technology. He is Member of BUTR, Academic Council & BOE at AUM. He is BOS member at Vivekanand Science, Pillai Science College Mumbai, and KBC-NMU Jalgaon.He has been served as Post Doctorate Fellow and Young Researcher (Giovani Ricercatori Indiani) at Centre of Excellence on HPC at University of Calabria, Italy. Dr. Manoj received PhD from Bharati Vidyapeeth University, India and MSC-Computer Science degree from North Maharashtra University, India. He is proud Computer Science fellow. He is currently holding position as Professor and HOI at Amity Institute of Information Technology, Amity University Maharashtra. He is having 20 years of experience. Dr. Manoj has been conducted series of national conferences on Machine Learning and Cyber Forensics. He is continuously engaged review of research papers of Journals and Conferences. He is passionate about quality based teaching learning processes. He is engaged in teaching of in DevOps, Machine Learning, Image Processing, Big Data Processing, Soft Computing, and Programming languages. He has been guided several MCA projects and took lead in project management. Dr. Manoj tested the Nimbus (Chicago) Science Clouds for virtual cluster deployment. He is also working with the theoretical concepts of grid computing and emerging concepts of “Nvidia-Tesla” General Programming with Graphics Processing Units (GP-GPU). He has been published several papers, co-authored edited chapters, and wrote an articles in the newsletter of international society of automation (ISA).

Author Articles
SIFT-BZM: Pixel Based Forgery Image Detection Using Scale-Invariant Feature Transform and Block Based Zernike Moments

By Kshipra Ashok Tatkare Manoj Devare

DOI: https://doi.org/10.5815/ijigsp.2025.01.06, Pub. Date: 8 Feb. 2025

New area of image processing termed "digital image forensics" aims to gather quantifiable proof of a digital image's authenticity and place of origin. Detection of forgery images to look for copied and pasted portions; however, depending on whether the copied portion underwent post-processing before being transferred to another party, the detection method may differ. Zernike Moments and Scale-Invariant Feature Transform (SIFT) combined are unique techniques that aid in the identification of textured and smooth regions. But compared to SIFT separately, this combination is the slowest. So in the proposed work, Block based image division and SIFT based key point detection model is developed to detect forgery images. The gathered images are poor visual quality and various dimension, so it is resized and converter grayscale conversion. In addition, pixel values of images are improved using optimal Gaussian filter and adaptive histogram equalization which remove noise and blurring based on sigma value. Then, using the SIFT key point extraction algorithm to extract the image's key point and compute the feature vector of each key-points. In that using a block based matching technique to split the pre-images into blocks, and each blocks are diagonally subdivide. Length of the feature vector is computed using Zernike moments of each blocks. Both SIFT features and Zernike moments features are matched to identify the manipulated image from the given data. The proposed model provides 100% recall, 98.2% precision, and 99.09% F1_score. Thus provide the proposed model was effectively detects forgery image in the given data. 

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