Work place: Department of CSE, NIT Jamshedpur, Adityapur, Jamshedpur, India
E-mail: viswanathasarma.ch@gmrit.edu.in
Website: https://orcid.org/0000-0002-2047-1745
Research Interests:
Biography
Viswanathasarma Ch. received his M.Tech degree in Computer Science and Technology in the specialization of Artificial Intelligence and Robotics from Andhra University College of Engineering, Visakhapatnam, India in 2009. He is currently pursuing PhD at the Department of Computer Science and Engineering from National Institute of Technology, Jamshedpur, India. His areas of interest are Artificial Intelligence, Machine Learning, Deep Learning and Digital Watermarking. He is currently working as Sr. Asst Prof in the department of CSE-AIML in GMR Institute of Technology, Razam, Andhrapradesh, India.
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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