Danish Ali Khan

Work place: Department of CSE, NIT Jamshedpur, Adityapur, Jamshedpur, India

E-mail: dakhan.cse@nitjsr.ac.in

Website:

Research Interests:

Biography

Danish Ali Khan received his PhD from the National Institute of Technology Jamshedpur in the specialization of Some JELS Inventory Models for one Vendor-Multi Customer Situation in 2004. He currently serves as Head of the Department (HOD) in the Department of Computer Science and Engineering, National Institute of Technology, Jamshedpur, India. His area of research includes information security, operationn research, software engineering, Machine Learning and IT application in Supply Chain Management. He has Published 45+ research papers, including 14 in SCI/Scopus journals and Book Chapters in reputed national and international journals and supervised 9 PhD scholars and 8 MTech. Thesis and 100+ MCA Projects. Currently, he is supervising 3 full-time and 6 part-time PhD scholars.

Author Articles
A Robust Digital Image Watermarking Using Gorilla Troop Optimization Algorithm in Hybrid Frequency Domain

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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