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### 一种非采样轮廓域中的新型彩色图像水印方案 #### 概述本文介绍了一种基于非采样轮廓变换（NSCT）和支持向量回归（SVR）技术的新型彩色图像水印算法。该算法针对几何畸变攻击具有良好的抵抗能力，能够在保持视觉质量的同时，有效抵御常见的图像处理操作如滤波、加噪和JPEG压缩等，并能抵抗几何畸变。 #### 关键词解析 - **图像水印**：是一种嵌入到数字媒体中的不可见或半可见信息，用于版权保护、完整性验证等目的。 - **非采样轮廓变换 (NSCT)**：是一种多尺度多方向的信号分析方法，适用于图像处理任务，尤其是对边缘和纹理特征的分析。 - **彩色图像归一化**：通过对图像颜色空间进行转换，使得不同设备之间能够一致地显示图像。 - **HSV遮罩**：通过在HSV颜色空间中调整水印嵌入强度，实现对水印的优化隐藏。 #### 技术细节 - **构建几何不变空间**：通过彩色图像归一化技术构造几何不变空间，进而获得显著区域。此步骤对于提高水印对几何畸变的抵抗力至关重要。 - **显著区域的选择**：利用不变质心理论从归一化的彩色图像中选择出一个显著区域作为后续处理的对象。 - **NSCT应用于绿色通道**：对显著区域的绿色通道执行NSCT，提取低频系数作为水印嵌入的目标。 - **水印嵌入**：通过对选定的低频NSCT系数进行修改来嵌入数字水印。HSV遮罩被用来控制水印的嵌入强度，确保水印既不可见又不易被检测出来。 - **支持向量回归（SVR）技术**：在水印检测阶段，利用SVR技术根据不同颜色通道之间的高相关性恢复数字水印。即使在受到几何畸变的情况下，也能准确检测出水印。 #### 实验结果与评价 - **实验结果显示**：所提出的彩色图像水印算法不仅具有良好的视觉效果，而且能够抵抗多种常见图像处理操作，包括滤波、噪声添加以及JPEG压缩等。 - **几何畸变抵抗性能**：该算法还能有效地抵抗各种几何畸变，如缩放、旋转和剪切等，这在当前许多水印算法中是一个挑战性的难题。 - **安全性评估**：通过实验验证了该方案的安全性和鲁棒性，证明其在实际应用中具有很高的实用价值。 #### 结论本文提出了一种非采样轮廓域中的新型彩色图像水印方案，该方案利用NSCT和支持向量回归技术，在保证图像视觉质量的同时，有效提高了水印对几何畸变的抵抗力。实验结果表明，该方案不仅能够应对常见的图像处理操作，还能有效抵抗几何畸变，展现出较强的实用性及应用前景。

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A novel color image watermarking scheme in nonsampled contourlet-domain

Pan-Pan Niu

, Xiang-Yang Wang

⇑

, Yi-Ping Yang

, Ming-Yu Lu

School of Information Science & Technology, Dalian Maritime University, Dalian 116026, China

School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China

article info

Keywords:

Image watermarking

Nonsubsampled contourlet transform

(NSCT)

Color image normalization

HVS masking

abstract

Geometric distortion is known as one of the most difﬁcult attacks to resist, for it can desynchronize the

location of the watermark and hence causes incorrect watermark detection. It is a challenging work to

design a robust color image watermarking scheme against geometric distortions. Based on the support

vector regression (SVR) and nonsubsampled contourlet transform (NSCT), we propose a new color image

watermarking algorithm with good visual quality and reasonable resistance toward geometric distortions

in this paper. Firstly, the geometrically invariant space is constructed by using color image normalization,

and a signiﬁcant region is obtained from the normalized color image by utilizing the invariant centroid

theory. Then, the NSCT is performed on the green channel of the signiﬁcant region. Finally, the digital

watermark is embedded into host color image by modifying the low frequency NSCT coefﬁcients, in

which the HVS masking is used to control the watermark embedding strength. In watermark detection,

according to the high correlation among different channels of the color image, the digital watermark can

be recovered by using SVR technique. Experimental results show that the proposed color image water-

marking is not only invisible and robust against common image processing operations such as ﬁltering,

noise adding, and JPEG compression etc., but also robust against the geometrical distortions.

1. Introduction

With the rapid growth and widespread use of network distribu-

tions of digital media content, there is an urgent need for protect-

ing the copyright of digital content against piracy and malicious

manipulation. Digital watermarking has been proposed as a possi-

ble and efﬁcient answer to these concerns. While the most prom-

inent application of watermarking is copyright protection, others

including ﬁngerprinting, broadcast monitoring, digital media

authentication, and copy protection are important research areas

(Lian, Kanellopoulos, & Ruffo, 2009). For different purposes, digital

watermarking has been branched into two classiﬁcations: robust

watermarking technique and fragile watermarking technique. Ro-

bust digital watermarking is used to protect ownership of the dig-

ital media. In contrast, the purpose of fragile watermarking

technique is digital media authentication, that is, to ensure the

integrity of the digital media.

In recent years, there is an unprecedented development in the

robust image watermarking ﬁeld. On the other hand, attacks

against image watermarking systems have become more sophisti-

cated (Zheng, Liu, Zhao, & El Saddik, 2007). In general, these attacks

on watermarking systems can be categorized into common image

processing operations and geometrical distortions. Watermark

robustness to common image processing operations has been ad-

dressed extensively, such as lossy compression, noise addition

and median ﬁltering etc. However, many of the existing schemes

are fragile to geometrical distortions, such as rotation, scaling

and translation (RST). Even a slight geometric distortion may sig-

niﬁcantly inﬂuence the extraction of watermark because the syn-

chronization of the watermark is destroyed. Nowadays, several

approaches that counterattack geometric distortions have been

developed. These schemes can be roughly divided into invariant

transform, template insertion and feature-based algorithms (Dong,

Jovan, Yongyi, Yang, & Franck, 2005; Zheng et al., 2007; Zheng,

Wang, & Zhao, 2009).

Invariant transform: The most obvious way to achieve resil-

ience against geometric distortions is to use an invariant trans-

form. In Zheng et al. (2007), Guo, Liu, and Liu (2007), Xin, Liao,

and Pawlak (2007), Zhang, Qian, Xiao, and Ji (2007) and Xiang,

doi:10.1016/j.eswa.2010.07.147

This work was supported by the National Natural Science Foundation of China

under Grant Nos. 60773031 & 60873222, the Open Foundation of State Key

Laboratory of Networking and Switching Technology of China under Grant No.

SKLNST-2008-1-01, the Open Foundation of Network and Data Security Key

Laboratory of Sichuan Province, the Open Foundation of State Key Laboratory for

Novel Software Technology of China under Grant No. A200702, the Open Founda-

tion of Key Laboratory of Modern Acoustics Nanjing University under Grant No. 08-

02, and Liaoning Research Project for Institutions of Higher Education of China

under Grant No. L2010230.

⇑

Corresponding author at: School of Computer and Information Technology,

Liaoning Normal University, Dalian 116029, China. Tel.: +86 0411 85992415; fax:

+86 0411 85992323.

E-mail address: wxy37@126.com (X.-Y. Wang).

Expert Systems with Applications 38 (2011) 2081–2098

Contents lists available at ScienceDirect

Expert Systems with Applications

journal homepage: www.elsevier.com/locate/eswa

Kim, and Huang (2008), the watermark is embedded in an afﬁne

invariant domain by using Fourier–Mellin transform, generalized

Radon transform, pseudo-Zernike moments, geometric moments,

and histogram shape respectively. In Zhang, Cheng, Qiu, and

Cheng (2008), an algorithm of leveled watermarking is proposed.

This algorithm is based on the curvelet transform and does not

involve the original image at the detector end or exhaustive direc-

tion search. Watermark bits with different robustness are embed-

ded in each scale and one bit is carried by one wedge. The

coefﬁcients selection for watermarking is based on the conception

that the coefﬁcient energy is proportional to its directional sensi-

tivity. Tai, Yeh, and Chang (2009) presented a reversible data hid-

ing scheme based on histogram modiﬁcation. They exploit a

binary tree structure to solve the problem of communicating pairs

of peak points. Distribution of pixel differences is used to achieve

large hiding capacity while keeping the distortion low. They also

adopt a histogram shifting technique to prevent overﬂow and

underﬂow. Despite that they are robust against global afﬁne

transformations, those techniques involving invariant domain

suffer from implementation issues and are vulnerable to mixed

attacks.

Template insertion: Another solution to cope with geometric

distortions is to identify the transformation by retrieving artiﬁ-

cially embedded references. Liu, Zheng, and Zhao (2007) presents

an image rectiﬁcation scheme that can be used by any image

watermarking algorithm to provide robustness against rotation,

scaling and translation (RST). In the watermarking, a small block

is cut from the log-polar mapping (LPM) domain as a matching

template, and a new ﬁltering method is proposed to compute

the cross-correlation between this template and the magnitude

of the LPM of the image having undergone RST transformations

to detect the rotation and scaling parameters. Zhang, Li, and

Wang (2008) designs look-up table (LUT) according to the distor-

tion of LUT embedding. A new practical reduced-distortion LUT

design method is developed for robust data hiding. A Gaussian

mixture model and a related expectation-maximization algorithm

based method are employed to model the statistical distribution

of the host image. The statistical model is used to select signiﬁ-

cant wavelet coefﬁcients of the host image for data hiding. Boato

and Conotter (2009) present an innovative and ﬂexible tool suit-

able to assess the robustness of digital watermarking techniques,

by introducing a novel metric based on the perceptual quality

evaluation for unmarked images. Genetic algorithms (GA) per-

form the search of optimal parameters to be assigned to each im-

age processing operator, as well as the order they need to be

applied in, to remove the watermark from the content while

keeping the perceptual quality of the resulting image as high as

possible. However, this kind of approach can be tampered with

by the malicious attack. As for random bending attacks, the tem-

plate-based methods will be incompetent to estimate the attack

parameters.

Feature-based: The last category is based on media features. Its

basic idea is that, by binding the watermark with the geometri-

cally invariant image features (Local Feature Region, LFR), the

watermark detection can be done without synchronization error.

Lee, Lee, and Lee (2007) propose a geometrically invariant water-

marking method that uses circular Hough transform for water-

mark synchronization. Through circular Hough transform, the

circular features are extracted that are invariant to geometric dis-

tortions. Based on Harris–Laplace detector and scale-space the-

ory, Wang, Wu, and Niu (2007) propose a feature-based digital

image watermarking scheme in DFT domain. In Li and Guo

(2009), present a novel robust image watermarking scheme for

resisting geometric distortions. Watermark synchronization is

ﬁrst achieved by local invariant regions which can be generated

using scale normalization and image feature points. The water-

mark is embedded into all the local regions repeatedly in spatial

domain. Deng, Gao, Li, and Tao (2009) give a content-based

watermarking scheme that combines the invariant feature extrac-

tion with watermark embedding by using Tchebichef moments.

Pham, Miyaki, Yamaski, and Aizama (2008) present a robust ob-

ject-based watermarking algorithm using the local image feature

in conjunction with a data embedding method based on DCT, and

the digital watermark is embedded in the DCT domain of ran-

domly generated blocks in the selected object region. Gao, Deng,

and Li (2010) propose a new image watermarking scheme by

incorporating the advantages of the afﬁne invariant point detec-

tor and the orientation alignment seamlessly. The afﬁne invariant

point detector is adopted to extract Afﬁne Covariant Regions

(ACRs). The graph theoretical clustering algorithm is then em-

ployed to select a set of nonoverlapped ACRs for watermark

embedding. Singhal, Lee, and Kim (2009) propose a robust image

watermarking algorithm using local Zernike moments, which are

computed over circular patches around feature points. The pro-

posed algorithm locally computes Zernike moments and modiﬁes

them to embed watermarks, achieving robustness against crop-

ping and local geometric attacks. Moreover, to deal with scaling

attacks, the proposed algorithm extracts salient region parame-

ters, which consist of an invariant centroid and a salient scale,

and transmits them to the decoder. The parameters are used at

the decoder to normalize a suspect image and detect watermarks.

In Li, Guo, and Pan (2010), a new robust image watermarking

scheme is presented by combining scale-space feature point

based watermark synchronization and NSCT based watermark

embedding. Watermark synchronization is achieved based on

the local circular regions, which can be generated using the

scale-invariant feature transform (SIFT). In the encoder, the

watermark is embedded into the NSCT coefﬁcients in a con-

tent-based and rotation-invariant manner by odd–even quantiza-

tion. In the decoder, the watermark can be extracted directly

from the local regions using the proposed coefﬁcient property

detector (CPD). It is not difﬁcult to see that the feature-based ap-

proaches are better than others in terms of robustness. However,

some drawbacks indwelled in current feature-based schemes re-

strict the performance of watermarking system. First, the feature

point extraction is sensitive to image modiﬁcation. Second, the

computational complexity in calculating the features of an image

before watermark detection is added. Third, the volume of water-

mark data is lesser.

Generally speaking, the above-mentioned research efforts con-

centrated on developing the watermarking schemes for gray

images, and the watermarking schemes for color images are very

few. While, in ubiquitous multimedia applications, color images

are basic components of multimedia systems, such as video sys-

tems based on current video compression standards, MPEG-1,

MPEG-2, MPEG-4, and further MPEGs. Hence, it is crucial to ﬁrst

develop effective watermarking techniques for color images. In

Peng and Liu (2008), a new semi-fragile watermarking scheme

for color image authentication is proposed based on spatiotempo-

ral chaos and singular value decomposition (SVD). The watermark

is embedded into the singular values of the blocks within wavelet

subband. In order to enhance the security, spatiotemporal chaos is

employed to select the embedding positions for each watermark

bit as well as for watermark encryption. Tsui, Zhang, and Androut-

sos (2008) encodes the L

components of color images and

watermarks are embedded as vectors by modifying the Spatiochro-

matic Discrete Fourier Transform (SCDFT) coefﬁcients and using

the Quaternion Fourier Transform (QFT). However, it was found

that even very small geometric distortions, such as RST attacks,

could reduce the detector’s ability of detecting watermarks. Arash

and Shohreh (2008) proposed a novel method to utilize the Princi-

pal Component Analysis (PCA) in the neighborhoods of an image in

2082 P.-P. Niu et al. / Expert Systems with Applications 38 (2011) 2081–2098

order to extract the corresponding eigenimages. These eigenimag-

es exhibit high levels of energy compaction and thus are appropri-

ate for such operations as compression and watermarking.

However, the algorithm is not robust to cropping. In Lee and Kwon

(2008), a color image watermarking algorithm based on Direct Se-

quence-Code Division Multiple Access (DS-CDMA) and Hadamard

kernel has been proposed. In Liu (2010), a wavelet-based water-

marking scheme for color images is proposed. The watermarking

scheme is based on the design of a color visual model that is the

modiﬁcation of a perceptual model used in the image coding of

gray scale images. The model is to estimate the noise detection

threshold of each wavelet coefﬁcient in luminance and chromi-

nance components of color images in order to satisfy transparency

and robustness required by the color image watermarking tech-

nique. Unfortunately these approaches are non-invariant to image

rotation and translation attacks. Zheng and Feng (2009) proposed a

new watermarking scheme based on the multi-channel image

watermarking framework, which generates a watermarking tem-

plate from one of image channels data, and then embeds this

watermarking template into another image channel. In Lin, Shie,

and Guo (2010), an improved DCT-based image watermarking

technique has been introduced. The watermark is embedded into

a digital image, based on the concept of mathematical remainder,

by modifying the low-frequency coefﬁcients in DCT frequency do-

main. Fu and Shen (2008) presented a novel oblivious color image

watermarking scheme based on linear discriminant analysis (LDA).

The watermark accompanied with a reference is embedded into

the RGB channels of color images. By applying the embedded ref-

erence watermark, a linear discriminant matrix is obtained. The

watermark can be correctly extracted under several different at-

tacks. Behnia, Teshnehlab, and Ayubi (2010) proposed a new

watermarking scheme for color image based on a family of the

pair-coupled maps. Pair-coupled maps are employed to improve

the security of watermarked image, and to encrypt the embedding

position of the host image. Another map is also used to determine

the pixel bit of host image for the watermark embedding. The

purpose of this algorithm is to improve the shortcoming of water-

marking such as small key space and low security. These algo-

rithms are robust to image rotation and cropping, but they are

not robust to translation and scaling distortion in general. Xing

and Tan (2007) gives a color watermarking algorithm not only

robust to waveform attacks due to the use of block-SVD, but also

robust to cropping because of the watermark Arnold transforma-

tion preprocessing. But the scheme only survives very small angle

rotation. In Xing and Tan (2010), the authors add a step before

watermark extract, that is, calculating phase correlation in image

log-polar mapping (LPM) domain to estimate the geometrical

distortion parameters to resynchronize the watermark position

destroyed by geometrical distortion. Therefore, the scheme is

robust to all most geometric distortions, such as rotation, scaling

and cropping, but it is very little under translation attacks.

In order to effectively resolve the problem of resisting geomet-

ric distortions, the support vector machine (SVM) theory is intro-

duced to the image watermarking domain. In scheme (Wu & Xie,

2006), in order to obtain the rotation, scaling and translation

(RST) parameters, the SVM are utilized to learn image geometric

pattern represented by six combined low order image moments.

The watermark extraction is carried out after watermarked image

has been synchronized without original image. Wang, Xu, and Yang

(2009) propose a robust image watermarking detection algorithm

against geometric distortions, in which the steady pseudo-Zernike

moments and Krawtchouk moments are utilized. Tsai and Sun

(2007) propose a novel watermarking technique called SVM-based

color image watermarking (SCIW) for the authentication of color

images. The SCIW method utilizes the set of training patterns to

train the SVM and then applies the trained SVM to classify a set

of testing patterns. Following the results produced by the classiﬁer,

the SCIW method retrieves the hidden watermark without the ori-

ginal image during watermark extraction. Li, Ling, and Lu (2007)

introduce a novel semi-fragile watermarking scheme based on

SVM. This scheme ﬁrst gives a deﬁnition of wavelet coefﬁcient

direction tree, then the relation model between the root node

and its offspring nodes is established using SVM, and further

watermark is embedded and extracted based on this relation mod-

el. Based on a large number of theory analyses and experimental

results, we can easily come to the conclusion that it is possible to

resist geometric distortions by utilizing the advanced SVM, but

the current SVM-based image watermarking have shortcomings

as follows: (i) They are not very robust against some geometric dis-

tortions, such as cropping, mixed attacks etc; (ii) In watermark

detection procedure, the original watermark signal is needed, so

it is unfavorable to practical application.

In this paper, a new color image watermarking algorithm with

good visual quality and reasonable resistance toward geometric

distortions is proposed, in which the SVR and NSCT are utilized.

Firstly, the geometrically invariant space is constructed by using

color image normalization, and a signiﬁcant region is obtained

from the normalized color image by utilizing the invariant cen-

troid theory. Then, the NSCT is performed on the green channel

of the signiﬁcant region. Finally, the digital watermark is embed-

ded into host color image by modifying the low-frequency NSCT

coefﬁcients, in which the HVS masking is used to control the

watermark embedding strength. In watermark detection, accord-

ing to the high correlation among different channels of the color

image, the digital watermark can be recovered by using SVR

technique.

The rest of this paper is organized as follows. Section 2 presents

the basic theory about NSCT. In Section 3, the SVR technique is de-

scribed. Section 4 contains the description of our watermark

embedding procedure. Section 5 covers the details of the water-

mark detection procedure. Simulation results in Section 6 will

show the performance of our scheme. Finally, Section 7 concludes

this presentation.

2. An introduction to nonsubsampled contourlet transform

(NSCT)

Fig. 1(a) displays an overview of the nonsubsampled contourlet

transform (NSCT). The structure consists in a bank of ﬁlters that

splits the 2-D frequency plane into the subbands illustrated in

Fig. 1(b). The NSCT can be divided into two shift-invariant parts:

(1) a nonsubsampled pyramid structure that ensures the multi-

scale property and (2) a nonsubsampled directional ﬁlter bank

(DFB) structure that gives directionality.

2.1. Nonsubsampled pyramid (NSP)

The multiscale property of the NSCT is obtained from a shift-

invariant ﬁltering structure that achieves a subband decomposi-

tion similar to that of the Laplacian pyramid. This is achieved by

using two-channel nonsubsampled 2-D ﬁlter banks. Fig. 2 illus-

trates the nonsubsampled pyramid (NSP) decomposition with

J = 3 stages. Such expansion is conceptually similar to the one-

dimensional (1-D) Nonsubsampled Wavelet Transform (NSWT)

computed with the àtrous algorithm (Cunha, Zhou, & Do, 2006)

and has J + 1 redundancy, where J denotes the number of decompo-

sition stages. The ideal passband support of the lowpass ﬁlter at

the jth stage is the region [(

),(

)]

. Accordingly, the ideal

support of the equivalent highpass ﬁlter is the complement of

the lowpass, i.e., the region [(

j1

),(

j1

)]

n[(

),(

)]

. The ﬁlters for subsequent stages are obtained by upsampling

P.-P. Niu et al. / Expert Systems with Applications 38 (2011) 2081–2098

2083

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