Title: Discrete stationary wavelet transform and SVD-based digital image watermarking for improved security

Authors: Rajakumar Chellappan; S. Satheeskumaran; C. Venkatesan; S. Saravanan

Addresses: Department of Electronics and Communication Engineering, Anurag Group of Institutions, Hyderabad, Telangana, India ' Department of Electronics and Communication Engineering, Anurag Group of Institutions, Hyderabad, Telangana, India ' Department of Electronics and Communication Engineering, HKBK College of Engineering, Bengaluru, Karnataka, India ' Department of Computer Science and Engineering, Agni College of Technology, Chennai, Tamilnadu, India

Abstract: Digital image watermarking plays an important role in digital content protection and security related applications. Embedding watermark is helpful to identify the copyright of an image or ownership of the digital multimedia content. Both the grey images and colour images are used in digital image watermarking. In this work, discrete stationary wavelet transform and singular value decomposition (SVD) are used to embed watermark into an image. One colour image and one watermark image are considered here for watermarking. Three level wavelet decomposition and SVD are applied and watermarked image is tested under various attacks such as noise attacks, filtering attacks and geometric transformations. The proposed work exhibits good robustness against these attacks and obtained simulation results show that proposed approach is better than the existing methods in terms of bit error rate, normalised cross correlation coefficient and peak signal to noise ratio.

Keywords: digital image watermarking; discrete stationary wavelet transforms; wavelet decomposition; singular value decomposition; SVD; peak signal to noise ratio; PSNR.

DOI: 10.1504/IJCSE.2021.117016

International Journal of Computational Science and Engineering, 2021 Vol.24 No.4, pp.354 - 362

Received: 31 Dec 2019
Accepted: 11 May 2020
Published online: 12 Aug 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article