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使用α阶海维赛德函数和分数高斯场的不变域水印

Invariant domain watermarking using heaviside function of order alpha and fractional Gaussian field.

作者信息

Abbasi Almas, Woo Chaw Seng, Ibrahim Rabha Waell, Islam Saeed

机构信息

Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.

Institute of Mathematical Science, University of Malaya, Kuala Lumpur, Malaysia.

出版信息

PLoS One. 2015 Apr 17;10(4):e0123427. doi: 10.1371/journal.pone.0123427. eCollection 2015.

Abstract

Digital image watermarking is an important technique for the authentication of multimedia content and copyright protection. Conventional digital image watermarking techniques are often vulnerable to geometric distortions such as Rotation, Scaling, and Translation (RST). These distortions desynchronize the watermark information embedded in an image and thus disable watermark detection. To solve this problem, we propose an RST invariant domain watermarking technique based on fractional calculus. We have constructed a domain using Heaviside function of order alpha (HFOA). The HFOA models the signal as a polynomial for watermark embedding. The watermark is embedded in all the coefficients of the image. We have also constructed a fractional variance formula using fractional Gaussian field. A cross correlation method based on the fractional Gaussian field is used for watermark detection. Furthermore the proposed method enables blind watermark detection where the original image is not required during the watermark detection thereby making it more practical than non-blind watermarking techniques. Experimental results confirmed that the proposed technique has a high level of robustness.

摘要

数字图像水印是多媒体内容认证和版权保护的一项重要技术。传统的数字图像水印技术往往容易受到诸如旋转、缩放和平移(RST)等几何失真的影响。这些失真会使嵌入图像中的水印信息失去同步,从而无法进行水印检测。为了解决这个问题,我们提出了一种基于分数阶微积分的RST不变域水印技术。我们使用α阶海维赛德函数(HFOA)构建了一个域。HFOA将信号建模为用于水印嵌入的多项式。水印被嵌入到图像的所有系数中。我们还使用分数高斯场构建了一个分数方差公式。基于分数高斯场的互相关方法用于水印检测。此外,所提出的方法能够实现盲水印检测,即在水印检测过程中不需要原始图像,从而使其比非盲水印技术更具实用性。实验结果证实,所提出的技术具有很高的鲁棒性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c247/4401616/46b03c06090d/pone.0123427.g001.jpg

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