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CLC number: TP391.41

On-line Access: 2016-11-07

Received: 2015-05-20

Revision Accepted: 2016-02-16

Crosschecked: 2016-10-17

Cited: 0

Clicked: 6244

Citations:  Bibtex RefMan EndNote GB/T7714


M. F. Kazemi


A. H. Mazinan


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.11 P.1199-1217


Level-direction decomposition analysis with a focus on image watermarking framework

Author(s):  M. F. Kazemi, M. A. Pourmina, A. H. Mazinan

Affiliation(s):  Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran; more

Corresponding email(s):   mfkazemi@srbiau.ac.ir, pourmina@srbiau.ac.ir, mazinan@azad.ac.ir

Key Words:  Level-direction decomposition analysis, Watermarking framework, Contourlet embedding representation, Scrambling module, Simulated attacks

M. F. Kazemi, M. A. Pourmina, A. H. Mazinan. Level-direction decomposition analysis with a focus on image watermarking framework[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(11): 1199-1217.

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%T Level-direction decomposition analysis with a focus on image watermarking framework
%A M. F. Kazemi
%A M. A. Pourmina
%A A. H. Mazinan
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T1 - Level-direction decomposition analysis with a focus on image watermarking framework
A1 - M. F. Kazemi
A1 - M. A. Pourmina
A1 - A. H. Mazinan
J0 - Frontiers of Information Technology & Electronic Engineering
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DOI - 10.1631/FITEE.1500165

This research addresses the new level-direction decomposition in the area of image watermarking as the further development of investigations. The main process of realizing a watermarking framework is to generate a watermarked image with a focus on contourlet embedding representation. The approach performance is evaluated through several indices including the peak signal-to-noise ratio and structural similarity, whereby a set of attacks are carried out using a module of simulated attacks. The obtained information is analyzed through a set of images, using different color models, to enable the calculation of normal correlation. The module of the inverse of contourlet embedding representation is correspondingly employed to obtain the present watermarked image, as long as a number of original images are applied to a scrambling module, to represent the information in disorder. This allows us to evaluate the performance of the proposed approach by analyzing a complicated system, where a decision making system is designed to find the best level and the corresponding direction regarding contourlet embedding representation. The results are illustrated in appropriate level-direction decomposition. The key contribution lies in using a new integration of a set of subsystems, employed based upon the novel mechanism in contourlet embedding representation, in association with the decision making system. The presented approach is efficient compared with state-of-the-art approaches, under a number of serious attacks. A number of benchmarks are obtained and considered along with the proposed framework outcomes. The results support our ideas.

The article presented watermarking in contourlet transform.


概要:本文研究新型层级-方向分解在图像水印中的应用。实现水印框架的主要步骤是生成带有水印的图像,重点用到轮廓小波嵌入表达。通过一组模拟攻击,基于峰值信噪比(peak signal-to-noise ratio)、结构相似度(structural similarity)等指标评估其性能。利用一组图像并使用不同颜色模型分析所获信息,以判断正态相关性。相应地,每当扰码模块作用于一组原始图像,用以表示无序信息,就使用逆轮廓小波嵌入表达以获取带有水印的图像。从而,我们可以通过分析一个复杂系统--其中设计了一个决策系统,使用轮廓小波嵌入表达来发现最优层级和相应方向--评估所提方法的性能。对于所得到的结果,利用恰当的层级-方向分解阐释。本文主要贡献在于,在轮廓小波嵌入表达新机制的基础上,集成一套子系统,与决策系统相配合。与现有方法相比,本文所述方法在大量重度攻击下仍然有效。利用多个基准数据集对所提方案进行了测试,结果证实方法有效。


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