Work place: Department of Computer Science, Central University of Tamil nadu, India
E-mail: chandramouli@cutn.ac.in
Website: https://orcid.org/0000-0001-7909-9733
Research Interests:
Biography
Chandra Mouli Pvssr is an Associate Professor in the Department of Computer Science, Central University of Tamil Nadu since December 2019. His research interests are Digital Image Processing, Machine Learning, Deep Learning, Digital Watermarking, Hyper-spectral Imaging. He earned his PhD from National Institute of Technology (N.I.T.) Trichy in 2010. Prior to joining CUTN, he was associated with VIT University, Vellore from 2009 to 2018 in the capacities of Associate Professor and Professor. Later he moved to National Institute of Technology, Jamshedpur as Assistant Professor during June 2018 to December 2019. Three research scholars have completed Ph.D. under his guidance. He has guided several B.Tech., M.Tech., and M.Sc. students. He has executed two research projects sponsored by DRDO, New Delhi one as a principal investigator and the other as co-investigator. He is a reviewer for many reputed SCI/SCIE indexed journals that include Multimedia Tools and Applications, IET Image Processing, etc.
By Viswanathasarma Ch. Danish Ali Khan Chandramouli Pvssr
DOI: https://doi.org/10.5815/ijigsp.2025.06.09, Pub. Date: 8 Dec. 2025
Because of the nature of the Internet and the growing number of people using digital media, copyright protection is becoming more important. One of the most common ways to protect this is by implementing digital image watermarking. This protection method safeguards the image from unauthorized access. The Gorilla Troop Optimization Algorithm (GTO), a new evolutionary algorithm, is what we propose to be a powerful watermarking technique. Initially, we applied Discrete Wavelet Transform (DWT) to the cover image, followed by Singular Value Decomposition (SVD) for enhanced security, and finally, we applied SVD to the Watermark image for its embedding into the cover image. In this process, we aim to optimize the multiple scaling factors (MSFs) by applying the GTO algorithm and testing the proposed algorithm in the MATLAB environment using some standard images. We then evaluated the experiment using performance metrics such as Normalized Cross-Correlation (NCC), the Structural Similarity Index (SSIM), and the Peak Signal-to-Noise Ratio (PSNR). These metrics proved the imperceptibility of different attacks and the proposed algorithm’s performance.
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