Multi-Objective Genetic Algorithm Optimization for Image Watermarking Based on Singular Value Decomposition and Lifting Wavelet Transform

被引：0
|
作者：
Loukhaoukha, Khaled [1 ]
Chouinard, Jean-Yves [1 ]
Taieb, Mohamed Haj [1 ]
机构：
[1] Univ Laval, Quebec City, PQ G1K 7P4, Canada
来源：
IMAGE AND SIGNAL PROCESSING, PROCEEDINGS | 2010年 / 6134卷
关键词：
Digital watermarking; multi-objective optimization; genetic algorithm; singular value decomposition; lifting wavelet transform; SCHEME;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new optimal watermarking scheme based on singular value decomposition (SVD) and lifting wavelet transform (LWT) using multi-objective genetic algorithm optimization (MOGAO) is presented. The singular values of the watermark is embedded in a detail subband of host image. To achieve the highest possible robustness without losing watermark transparency, multiple scaling factors (MSF) are used instead of single scaling factor (SSF). Determining the optimal values of the MSFs is a difficult problem. However, to find this values a multi-objective genetic algorithm optimization is used. Experimental results show a much improved performance in term of transparency and robustness of the proposed method compared to others methods.
引用
收藏
页码：394 / 403
页数：10
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