Indian Journal of Science and Technology
Year: 2021, Volume: 14, Issue: 4, Pages: 351-360
B Madhu1, Ganga Holi2*
1Assistant Professor, Department of Computer science and Engineering, Dr. Ambedkar Institute of Technology, Outer ring road, Bengaluru, India. Tel.: +919945187060
2Professor and Head , Department of Information Science and Engineering, AMC Engineering College, Bengaluru, 560083
Email: [email protected]
Received Date:29 October 2020, Accepted Date:13 January 2021, Published Date:02 February 2021
Objectives: To propose a Non-blind watermarking based on a Convolutional Neural Network (CNN). Methodology: An iterative learning model is proposed to ensure robustness and imperceptibility of watermarking process. In the first step, Stationary Wavelet Transformation (SWT) and Singular Value Decomposition (SVD) are used for the initial transformation and for embedding. The neural network is used to determine the relationship between host and watermarked image to extract the watermark. Findings: We have implemented our algorithm using Magnetic Resonant Imaging (MRI) and Computerized Tomography (CT), Mammogram and Retinal Images with different attacks and proved to have good robustness with Normalized Correlation coefficient (NC) value of 0.99 and invisibility feature with Peak Signal to Noise Ratio (PSNR) of 43.77 DB. We have compared our method with that of others and it proves to be good in terms of PSNR and NC values. Novelty/Application: This study provides a novel method to train CNN with both watermarked , attacked images and to classify them.
Keywords: Medical image authentication; stationary wavelet transform; convolutional neural networks; singular value decomposition
© 2021 Madhu & Holi.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)
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