A New Reliable Optimized Image Watermarking Scheme Based on the Integer Wavelet Transform and Singular Value Decomposition for Copyright Protection

Makbol, N.M. and Khoo, Bee Ee and Rassem, Taha H. and Loukhaoukha, Khaled (2017) A New Reliable Optimized Image Watermarking Scheme Based on the Integer Wavelet Transform and Singular Value Decomposition for Copyright Protection. Information Sciences, 417. pp. 381-400. ISSN 0020-0255. (Published)

PDF 1-s2.0-S0020025516322186-main.pdf - Published Version Restricted to Repository staff only Download (6MB) | Request a copy

Abstract

Although image watermarking schemes based on singular value decomposition (SVD) demonstrate high robustness and imperceptibility, they are exposed to the false positive problem (FPP). This drawback mostly occurs when embedding steps depend on singular values while singular vectors are used as secret keys. In this study, a new reliable SVD-based image watermarking scheme that uses integer wavelet transform (IWT) is proposed to overcome FPP and fulfil all watermarking requirements. Unlike in other schemes, the S and V matrices of the watermark are used as secret keys, whereas the S singular vector of the watermark is embedded into the singular values of the host image. The additional secret key is obtained from the watermarked image during the embedding process to increase security and avoid FPP completely. To improve the robustness, as well as achieve balance between robustness and imperceptibility, multi-objective ant colony optimization (MOACO) is utilized to find the optimal scaling factors, namely, multiple zooming factors. Results of the robustness, imperceptibility, and reliability tests demonstrate that the proposed IWT-SVD-MOACO scheme outperforms several previous schemes and avoids FPP completely.

Item Type:	Article
Uncontrolled Keywords:	Image watermarking; SVD; Integer wavelet transform; Multi-objective ant colony optimization (MOACO); False positive problem (FPP)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division:	Faculty of Computer System And Software Engineering
Depositing User:	Ms Norfamieza Mohamed Fayli
Date Deposited:	23 Aug 2017 06:53
Last Modified:	29 Mar 2018 07:50
URI:	http://umpir.ump.edu.my/id/eprint/18413
Download Statistic:	View Download Statistics

Actions (login required)

View Item