New gray image watermarking algorithm based on LWT and SVD

doi:10.3778/j.issn.1002-8331.2010.11.031

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (11): 104-106.DOI: 10.3778/j.issn.1002-8331.2010.11.031

• 网络、通信、安全 • Previous Articles Next Articles

New gray image watermarking algorithm based on LWT and SVD

JIANG Hua^1，2，ZHANG Min¹

1.College of Computer and Control，Guilin University of Electronic Technology，Guilin，Guangxi 541004，China
2.Guangxi Centre，National Software and Integrated Circuit Public Service Platform，Guilin，Guangxi 541004，China

Received:2008-10-09 Revised:2008-12-29 Online:2010-04-11 Published:2010-04-11
Contact: JIANG Hua

一种新的LWT和SVD的灰度图像水印

蒋华^1，2，张敏¹

1.桂林电子科技大学计算机与控制学院，广西桂林 541004
2.国家软件与电子电路公用平台广西分中心，广西桂林 541004

通讯作者: 蒋华

Abstract

Abstract: The original image is divided into many blocks.Then the middle-high sub-bands of two-level LWT with each block are implemented with LWT，and SVD is applied to the selected sub-band，the new matrix is constructed by singular values，which is divided into many blocks by rule，and decomposing with SVD again.Through twice partition blocks，twice LWT and the use of quartic SVD，the matrix will re-allocation and combination for extraction’s singular value，and finally the watermarking image with Arnold scramble is embedded into the matrix.Experimental results show that the proposed method increases embedded capacity，and has a good transparence and robustness against attacks for common image processing.

Key words: Lifting Wavelet Transform（LWT）, Singular Value Decomposition（SVD）, Arnold scramble, transparence, robustness

摘要： 先对载体图像进行分块，然后对每块二级LWT后的中高频带再次LWT，并对选取的各频带进行SVD，选取其相应的奇异值组成新的变换矩阵，对新的变换矩阵按规则进行分块，并再次SVD。通过两次分块，两次LWT和四重使用SVD构造矩阵的方法，有效地将抽取的奇异值重新分配和组合，最后将Arnold置乱后的灰度水印信息加载到组合后的矩阵中。仿真实验表明，新的图像水印方法提高了嵌入的信息量，具有很好的透明性，而且对常见的图像攻击具有很强的鲁棒性。

关键词: 提升小波变换, 奇异值分解, Arnold置乱, 透明性, 鲁棒性

CLC Number:

TP391

JIANG Hua^1，2，ZHANG Min¹. New gray image watermarking algorithm based on LWT and SVD[J]. Computer Engineering and Applications, 2010, 46(11): 104-106.

蒋华^1，2，张敏¹. 一种新的LWT和SVD的灰度图像水印[J]. 计算机工程与应用, 2010, 46(11): 104-106.

[1]	BAI Zhixu, WANG Hengjun, GUO Kexiang. Summary of Adversarial Examples Techniques Based on Deep Neural Networks [J]. Computer Engineering and Applications, 2021, 57(23): 61-70.
[2]	LI Song, LIU Zhe, TANG Xiaomei, WU Jian, WANG Feixue. Fixed-Point Iterated Huber-Based Robust Cubature Kalman Filter [J]. Computer Engineering and Applications, 2021, 57(16): 90-96.
[3]	YANG Xinmin, DONG Hongbin, TAN Chengyu, ZHOU Wen. Dendritic Cell Model Using Singular Value Decomposition and Information Gain [J]. Computer Engineering and Applications, 2021, 57(15): 156-162.
[4]	HUANG Xiaoqi, WANG Li, LI Gang. Text-Image Generative Adversarial Model for Fusion Capsule Networks [J]. Computer Engineering and Applications, 2021, 57(14): 176-180.
[5]	CHEN Xiaowen, LIU Guangshuai, LIU Wanghua, LI Xurui. Pairwise Rotation-Invariant Co-occurrence Adaptive Complete Local Ternary Pattern [J]. Computer Engineering and Applications, 2021, 57(1): 219-226.
[6]	FAN Chunlong, HE Yufeng, WANG Yixin. Large Weight Suppression Strategy for Training Convolutional Neural Networks [J]. Computer Engineering and Applications, 2020, 56(2): 115-119.
[7]	YANG Yongpeng, YANG Zhenzhen, LI Jianlin, LE Jun. Low Rank and Sparse Decomposition and Its Application in Video and Image Processing [J]. Computer Engineering and Applications, 2020, 56(16): 21-30.
[8]	LI Yun, MA Yinghong. Node Importance Rank by Attribute Reduction Set Evaluation [J]. Computer Engineering and Applications, 2019, 55(5): 149-158.
[9]	CAO Hao, CHEN Lili, SI Jibing, REN Junlan. Singular Value Decomposition and Sparse Automatic Encoder for Bearing Fault Diagnosis [J]. Computer Engineering and Applications, 2019, 55(20): 257-262.
[10]	HE Li, LIU Ying, HAN Keping. Improved TSVM Learning Algorithm Under Noise Labeling [J]. Computer Engineering and Applications, 2019, 55(17): 44-50.
[11]	JIANG Yunbiao, GUO Chen, YU Haomiao. Robust Adaptive Attitude Control Algorithm for Autonomous Underwater Vehicle [J]. Computer Engineering and Applications, 2019, 55(17): 266-270.
[12]	LIANG Xinyu1，2, WU Jiande1，2, HUANG Guoyong1，2, SUN Lei1，2. H∞ robust adaptive CKF algorithm used in GNSS/INS integrated navigation [J]. Computer Engineering and Applications, 2018, 54(9): 251-256.
[13]	YU Jingli, HU Enliang, ZHANG Tao. Study of Fisher linear discriminant analysis based on [L1]-norm [J]. Computer Engineering and Applications, 2018, 54(4): 128-134.
[14]	ZHENG Hao, DONG Mingli, PAN Zhikang. Target tracking algorithm with multiple algorithms in collaboration based on image classification [J]. Computer Engineering and Applications, 2018, 54(4): 185-191.
[15]	WEI Yu, SONG Tian. Lightweight and robust data reduction algorithms for Wireless Sensor Network [J]. Computer Engineering and Applications, 2018, 54(3): 100-108.

New gray image watermarking algorithm based on LWT and SVD

一种新的LWT和SVD的灰度图像水印

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics