Trade-off independent image watermarking using enhanced structured matrix decomposition

Author:

Khan Ahmed,Wong KokSheikORCID,Baskaran Vishnu Monn

Abstract

AbstractImage watermarking plays a vital role in providing protection from copyright violation. However, conventional watermarking techniques typically exhibit trade-offs in terms of image quality, robustness and capacity constrains. More often than not, these techniques optimize on one constrain while settling with the two other constraints. Therefore, in this paper, an enhanced saliency detection based watermarking method is proposed to simultaneously improve quality, capacity, and robustness. First, the enhanced structured matrix decomposition (E-SMD) is proposed to extract salient regions in the host image for producing a saliency mask. This mask is then applied to partition the foreground and background of the host and watermark images. Subsequently, the watermark (with the same dimension of host image) is shuffled using multiple Arnold and Logistic chaotic maps, and the resulting shuffled-watermark is embedded into the wavelet domain of the host image. Furthermore, a filtering operation is put forward to estimate the original host image so that the proposed watermarking method can also operate in blind mode. In the best case scenario, we could embed a 24-bit image as the watermark into another 24-bit image while maintaining an average SSIM of 0.9999 and achieving high robustness against commonly applied watermark attacks. Furthermore, as per our best knowledge, with high payload embedding, the significant improvement in these features (in terms of saliency, PSNR, SSIM, and NC) has not been achieved by the state-of-the-art methods. Thus, the outcomes of this research realizes a trade-off independent image watermarking method, which is a first of its kind in this domain.

Funder

Monash University Malaysia

Publisher

Springer Science and Business Media LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3