An optimal image watermarking approach based on a multi-objective genetic algorithm

被引:45
|
作者
Wang, Jun [1 ]
Peng, Hong [2 ]
Shi, Peng [3 ,4 ]
机构
[1] Xihua Univ, Sch Elect & Informat Engn, Chengdu 610039, Sichuan, Peoples R China
[2] Xihua Univ, Sch Math & Comp Engn, Chengdu 610039, Sichuan, Peoples R China
[3] Univ Glamorgan, Dept Comp & Math Sci, Pontypridd CF37 1DL, M Glam, Wales
[4] Victoria Univ, Sch Sci & Engn, Melbourne, Vic 8001, Australia
关键词
Multi-objective genetic algorithm; Variable-length mechanism; Image watermarking; Imperceptibility; Robustness; DIGITAL WATERMARKING; SCHEME; OPTIMIZATION; RECOGNITION;
D O I
10.1016/j.ins.2011.07.040
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In accordance with the multi-objective nature of image watermarking, an optimal image watermarking approach using a multi-objective genetic algorithm is presented in this paper. Both watermarking parameters and embedding positions are often important factors affecting the performance of watermarking systems. The proposed multi-objective watermarking method can automatically optimize system parameters, and a variable-length mechanism is specially designed to search the most suitable positions for embedding watermarks. The method can also remove the difficult issue of determining optimal watermarking parameters from previous watermarking algorithms. The proposed multi-objective watermarking method directly deals with the problem of optimizing watermarking under non-dominated meaning, thus it can effectively avoid the difficulty of determining the optimally weighted factor in existing single-objective watermarking schemes. In addition, a Pareto-optimal set generated by multi-objective optimization can provide flexibility in selecting the most suitable watermarking parameters according to practical requirements. (C) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:5501 / 5514
页数:14
相关论文
共 50 条
  • [1] Introducing a watermarking with a multi-objective genetic algorithm
    Diaz, Diego Sal
    Romay, Manuel Grana
    [J]. GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2, 2005, : 2219 - 2220
  • [2] Multi-objective genetic algorithm for semi-fragile watermarking
    Sal, D
    Graña, M
    [J]. PROCEEDINGS OF THE 8TH JOINT CONFERENCE ON INFORMATION SCIENCES, VOLS 1-3, 2005, : 520 - 523
  • [3] Multi-Objective Genetic Algorithm Optimization for Image Watermarking Based on Singular Value Decomposition and Lifting Wavelet Transform
    Loukhaoukha, Khaled
    Chouinard, Jean-Yves
    Taieb, Mohamed Haj
    [J]. IMAGE AND SIGNAL PROCESSING, PROCEEDINGS, 2010, 6134 : 394 - 403
  • [4] Optimal Configuration of Charging Station Based on Multi-objective Genetic Algorithm
    Qian, Kang
    Yan, Yang
    Xu, Yiyue
    Shan, Tingting
    [J]. PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON NEW ENERGY AND ELECTRICAL TECHNOLOGY, 2023, 1017 : 807 - 815
  • [5] Optimal Test Points Selection Based on Multi-objective Genetic Algorithm
    Zhang, Yong
    Chen, Xixiang
    Liu, Guanjun
    Qiu, Jing
    Yang, Shuming
    [J]. IEEE CIRCUITS AND SYSTEMS INTERNATIONAL CONFERENCE ON TESTING AND DIAGNOSIS, 2009, : 313 - 316
  • [6] Water resources optimal allocation based on multi-objective genetic algorithm
    Liu Meixia
    Wu Xinmiao
    [J]. PROCEEDINGS OF THE 2007 INTERNATIONAL CONFERENCE ON AGRICULTURE ENGINEERING, 2007, : 87 - 91
  • [7] Optimal design of cross shaft based on multi-objective genetic algorithm
    Mao, Yanfeng
    Li, Gongfa
    Jiang, Du
    Tao, Bo
    Cao, Yongcheng
    Li, Shidong
    Sun, Nannan
    Li, Zeshen
    [J]. International Journal of Wireless and Mobile Computing, 2021, 21 (03) : 243 - 254
  • [9] Multi-objective optimal dispatching of microgrid based on improved genetic algorithm
    Chen, H. D.
    An, Y.
    Meng, X. C.
    [J]. 2019 5TH INTERNATIONAL CONFERENCE ON ENERGY MATERIALS AND ENVIRONMENT ENGINEERING, 2019, 295
  • [10] Optimal Web Service Selection based on Multi-Objective Genetic Algorithm
    Wang, Junli
    Hou, Yubing
    [J]. PROCEEDINGS OF THE 2008 INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN, VOL 1, 2008, : 553 - +