[1] Wang Zhenfei, Song Shengli,. Digital watermarking algorithmbased on neural network in multiwavelet domain [J]. Journal of Southeast University (English Edition), 2007, 23 (2): 211-215. [doi:10.3969/j.issn.1003-7985.2007.02.012]

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Digital watermarking algorithmbased on neural network in multiwavelet domain()

基于神经网络和多小波变换的数字水印算法

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

23

Issue:

2007 2

Page:

211-215

Research Field:

Computer Science and Engineering

Publishing date:

2007-06-30

Info

Title:

Digital watermarking algorithmbased on neural network in multiwavelet domain

基于神经网络和多小波变换的数字水印算法

Author(s):

Wang Zhenfei¹;  2;  Song Shengli³

¹College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
² College of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
³Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

王振飞¹;  2;  宋胜利³

¹ 华中科技大学计算机科学与技术学院, 武汉 430074; ² 郑州大学信息工程学院, 郑州 450001; ³ 华中科技大学控制科学与工程系, 武汉 430074

Keywords:

digital watermarking;  neural networks;  multiwavelet transform

数字水印;  神经网络;  多小波变换

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2007.02.012

Abstract:

A novel blind digital watermarking algorithm based on neural networks and multiwavelet transform is presented.The host image is decomposed through multiwavelet transform.There are four subblocks in the LL-level of the multiwavelet domain and these subblocks have many similarities.Watermark bits are added to low-frequency coefficients.Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image.Experimental results demonstrate that the new algorithm is robust against a variety of attacks, especially, the watermark extraction does not require the original image.

基于图像多小波域低频系数子块的相似性, 利用神经网络的学习特性, 提出了新的盲数字水印算法.将宿主图像变化为多小波域, 把水印加入到宿主图像多小波变化后的低频系数中.通过后向传播算法的神经网络训练出宿主图像与嵌入的水印信号之间的关系特征, 利用神经网络具有学习和自适应的特性, 训练后的神经网络能够完全恢复嵌入到宿主图像中的水印信息.仿真实验表明, 该算法针对各种攻击具有很好的鲁棒性, 特别是在水印检测时不需要原始图像.

References:

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[5] Wang Shih-Hao, Lin Yuan-Pei.Wavelet tree quantization for copyright protection watermarking[J].IEEE Transactions on Image Process, 2004, 13(2):154-165.
[6] Reddy A A, Chatterji B N.A new wavelet based logo-watermarking scheme[J].Pattern Recognition Letters, 2005, 26(7):1019-1027.
[7] Ghouti L, Bouridane A, Ibrahim M K, et al.Digital image watermarking using balanced multiwavelets[J].IEEE Transactions on Signal Processing, 2006, 54(4):1519-1536.
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Memo

Memo:

Biography: Wang Zhenfei(1973—), male, doctor, associate professor, wzfei@zzu.edu.cn.

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