计算机工程与应用 ›› 2014, Vol. 50 ›› Issue (1): 78-82.

• 网络、通信、安全 • 上一篇    下一篇

基于整数小波变换和SVD的视频水印算法

熊祥光1,蒋天发2,蒋  巍3   

  1. 1.贵州师范大学 数学与计算机科学学院,贵阳 550001
    2.中南民族大学 计算机科学学院,武汉 430073
    3.中国软件评测中心,北京 100048
  • 出版日期:2014-01-01 发布日期:2013-12-30

Video watermarking algorithm based on integer wavelet transform and singular value decomposition

XIONG Xiangguang1, JIANG Tianfa2, JIANG Wei3   

  1. 1.School of Mathematics and Computer Science, Guizhou Normal University, Guiyang 550001, China
    2.College of Computer Science, South-Central University for Nationalities, Wuhan 430073, China
    3.Enterprise Dept I, China Software Testing Center, Beijing 100048, China
  • Online:2014-01-01 Published:2013-12-30

摘要: 提出了一种以二值图像为水印的混合整数小波变换和奇异值分解的视频水印盲提取算法。对水印图像进行混沌加密和Arnold置乱处理,选择计算复杂度低的直方图算法将视频分割为若干场景;借助密钥随机选取某些场景的亮度分量进行l级整数小波变换,再对低频子带进行分块的奇异值分解;采用量化的方法,将预处理后的水印图像嵌入奇异值分解后的最大奇异值中。在嵌入了水印的视频场景中提取所有的水印版本之后,利用对提取的所有水印信号版本进行统计求和的方法得到最终提取的水印图像。实验表明,提出的算法具有较好的透明性,对常见的处理具有较好的鲁棒性。

关键词: 视频水印, 整数小波变换, 奇异值分解, 鲁棒性

Abstract: A novel blind video watermarking algorithm is proposed by using a visible binary image as watermarking based on Integer Wavelet Transform(IWT) and Singular Value Decomposition(SVD). The watermarking is preprocessed by using chaotic encryption and Arnold scrambling and the algorithm partitions the video sequence into different scenes by using histogram method with low computation, some of which are selected with a key to perform l-level IWT, and then apply block-based SVD on the low-frequency sub-band. The preprocessed watermarking is embedded into the biggest singular values of the low-frequency sub-band based on quantization method. After all versions of the watermarking are extracted from the watermarked video scenes, the final extracted watermarking image is obtained by applying statistical summation method. The experimental results show that the proposed algorithm has good transparency and can robust against on common signal processing.

Key words: video watermarking, Integer Wavelet Transform(IWT), Singular Value Decomposition(SVD), robustness