计算机应用 ›› 2015, Vol. 35 ›› Issue (9): 2527-2530.DOI: 10.11772/j.issn.1001-9081.2015.09.2527

• 信息安全 • 上一篇    下一篇

基于果蝇优化算法的小波域数字水印算法

肖振久1,2, 孙健1, 王永滨2, 姜正涛2   

  1. 1. 辽宁工程技术大学 软件学院, 辽宁 葫芦岛 125105;
    2. 中国传媒大学 计算机学院, 北京 100024
  • 收稿日期:2015-04-07 修回日期:2015-05-17 出版日期:2015-09-10 发布日期:2015-09-17
  • 通讯作者: 孙健(1990-),男,黑龙江齐齐哈尔人,硕士研究生,主要研究方向:网络信息安全、数字水印,sunjian1990sj@163.com
  • 作者简介:肖振久(1968-),男,内蒙古赤峰人,副教授,博士研究生,主要研究方向:网络与信息安全、数字版权管理;王永滨(1963-),男,北京人,教授,博士,主要研究方向:形式化建模与仿真、分布式计算;姜正涛(1976-),男,北京人,副教授,博士,主要研究方向:密码学与信息安全。
  • 基金资助:
    国家自然科学基金资助项目(61103199);国家863计划项目(2011AA01A107-0),教育部-中国移动科研基金资助项目(MCM20130411)。

Wavelet domain digital watermarking method based on fruit fly optimization algorithm

XIAO Zhenjiu1,2, SUN Jian1, WANG Yongbin2, JIANG Zhengtao2   

  1. 1. College of software, Liaoning Technical University, Liaoning Huludao 125105, China;
    2. School of Computer, Communication University of China, Beijing 100024, China
  • Received:2015-04-07 Revised:2015-05-17 Online:2015-09-10 Published:2015-09-17

摘要: 为了平衡水印的透明性和鲁棒性,提出了基于果蝇优化算法(FOA)的小波域数字水印算法。该算法利用果蝇优化算法将离散小波变换(DWT)应用到水印技术中,通过群体智能算法解决水印的透明性和鲁棒性之间的矛盾。为了保护数字图像的版权信息,将所选择的原始图像通过二维离散小波变换分解,然后将经过Arnold变换后的水印图像较优地嵌入到小波的垂直子带系数中,这样可以保证图像的质量。在优化过程中,缩放因子是通过FOA不断地被训练和更新的。此外,还提出一个新的算法框架,通过DWT域预测可行性来评估参数。实验结果表明,所提算法具有较高的透明性和鲁棒性,水印相似度在0.95以上,与现有的一些基于群智能算法的水印方法相比,在对抗旋转和剪切等几何攻击提高了10%。

关键词: 果蝇优化算法, 离散小波变换, 缩放因子, Arnold变换, 透明性, 鲁棒性

Abstract: For balancing transparency and robustness of watermark, this paper proposed wavelet-domain digital watermarking method based on Fruit Fly Optimization Algorithm (FOA). The algorithm used Discrete Wavelet Transform (DWT) by FOA to watermarking technology and solved the contradiction between transparency and robustness in the watermark by swarm intelligence algorithm. In order to protect the copyright information of digital image, the selected original image was decomposed through a two-dimensional discrete wavelet transform, and watermark image through Arnold transformation was better embedded into wavelet coefficients of vertical sub-band, which guaranteed image quality. In the optimization process, the scaling factor was continuously being trained and updated by FOA. In addition, a new algorithm framework was proposed, which evaluated the scaling factor by prediction feasibility of DWT domain. The experimental results show that, the proposed algorithm has higher transparency and robustness against attacks, with watermarking similarity above 0.95, and 10% higher under geometric attacks such as rotation and shearing compared to some existing watermarking methods based on swarm intelligence.

Key words: Fruit Fly Optimization Algorithm (FOA), Discrete Wavelet Transform (DWT), scaling factor, Arnold transformation, transparency, robustness

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