8/7/2019 Robust Color Image Watermarking Using Nonsubsampled Contourlet Transform http://slidepdf.com/reader/full/robust-color-image-watermarking-using-nonsubsampled-contourlet-transform 1/12 (IJCSIS)International Journal of Computer Scienceand InformationSecurity,Vol.9,No.3,March2011Robust Color Image Watermarking Using Nonsubsampled Contourlet Transform C.Venkata Narasimhulu K.Satya Prasad Professor, Dept of ECE Professor, Dept of ECE, HIET, Hyderabad, India JNTU Kakinada, India [email protected][email protected], Abstract- In this paper, we propose a novel hybrid spread spectrum watermarking scheme for authentication of color images using nonsubsampled contourlet transform and singular value decomposition. The host color image and color watermark images are decomposed into directional sub- bands using Nonsubsampled contourlet transform and then applied Singular value decomposition to mid frequency sub-band coefficients. The singular values of mid frequency sub-band coefficients of color watermark image are embedded into singular values of mid frequency sub-band coefficients of host color image in Red, Green and Blue color spaces simultaneously based on spread spectrum technique. The experimental results shows that the proposed hybrid watermarking scheme is robust against common image processing operations such as, JPEG, JPEG 2000 compression, cropping, Rotation, histogram equalization, low pass filtering ,median filtering, sharpening, shearing ,salt & Pepper noise, Gaussian noise, grayscale conversion etc. It has also been shown the variation of visual quality of watermarked image for different scaling factors. The comparative analysis reveals that the proposed watermarking scheme out performs the color image watermarking schemes reported recently. Keywords: Color image watermarking, Nonsubsampled Contourlet Transform, Singular value decomposition, Peak signal to noise ratio, normalized Correlation coefficient. 1. INTRODUCTION: In recent years, multimedia products were rapidly distributed over the fast communication systems such as Internet, so there exist strong requirement to protect the ownership and authentication of the multimedia data. Digital watermarking is a method of securing the digital data by embedding additional information called water mark into the digital multimedia content. This embedding information can be later extracted from or detected in the multimedia to make an assertion about the data authenticity. Digital watermarks remain intact under transmission/transformation, allowing us to protect our ownership rights in digital form. Absence of watermark in a previously watermarked image would lead to the conclusion that the data content has been modified. A watermarking algorithm consists of watermark structure, an embedding algorithm and extraction or detection algorithm. In multimedia applications, embedded watermark should be invisible, robust and have a high capacity. Invisibility refers to degree of distortion introduced by the watermark and its affect on the viewers and listeners. Robustness is the resistance of an embedded watermark against intentional attack and normal signal processing operations such as noise, filtering, rotation, scaling, cropping and lossey compression etc. Capacity is the amount of data can be represented by embedded watermark.[1] Watermarking techniques may be classified in different ways. The classification may be based on the type of watermark being used, i.e., the watermark may be a visually recognizable logo or sequence of random numbers. A second classification is based on whether the watermark is applied in the spatial domain or the transform domain. In spatial domain, the simplest method is based on embedding the watermark in the least significant bits (LSB) of image pixels. However, spatial domain techniques are not resistant enough to image compression and other image processing operations. Transform domain watermarking schemes such as those based on the discrete cosine transform (DCT), the discrete wavelet transform (DWT), contourlet transforms along with numerical transformations such as Singular value Decomposition (SVD) and Principle component analysis (PCA) typically provide higher image fidelity and are much robust to image manipulations.[2]Of the so far proposed algorithms, wavelet domain algorithms perform better than other transform domain algorithms since DWT has a number of advantages over other transforms including time frequency localization, multi resolution representation, superior HVS modeling, and linear complexity and adaptively and it has been proved that wavelets are good at representing point wise discontinuities in one dimensional signal. However, in higher dimensions, e.g. image, there exists line or curve-shaped discontinuities. Since, 2D wavelets are produced by tensor products of 1D wavelets; they can only identify horizontal, vertical, diagonal discontinuities (edges) in images, ignoring smoothness along contours and curves. Curvelet transform was defined to represent two 100 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
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8/7/2019 Robust Color Image Watermarking Using Nonsubsampled Contourlet Transform
In this paper, we propose a novel hybrid spreadspectrum watermarking scheme for authentication of color images using nonsubsampled contourlet transformand singular value decomposition. The host color imageand color watermark images are decomposed intodirectional sub- bands using Nonsubsampled contourlettransform and then applied Singular value decomposition
to mid frequency sub-band coefficients. The singularvalues of mid frequency sub-band coefficients of colorwatermark image are embedded into singular values of mid frequency sub-band coefficients of host color image inRed, Green and Blue color spaces simultaneously based onspread spectrum technique. The experimental resultsshows that the proposed hybrid watermarking scheme isrobust against common image processing operations suchas, JPEG, JPEG 2000 compression, cropping, Rotation,histogram equalization, low pass filtering ,medianfiltering, sharpening, shearing ,salt & Pepper noise,Gaussian noise, grayscale conversion etc. It has also beenshown the variation of visual quality of watermarkedimage for different scaling factors. The comparativeanalysis reveals that the proposed watermarking scheme
out performs the color image watermarking schemesreported recently.
Keywords: Color image watermarking, Nonsubsampled Contourlet Transform, Singular value decomposition, Peaksignal to noise ratio, normalized Correlation coefficient.
1. INTRODUCTION:
In recent years, multimedia products were rapidlydistributed over the fast communication systems suchas Internet, so there exist strong requirement to protectthe ownership and authentication of the multimediadata. Digital watermarking is a method of securing thedigital data by embedding additional information calledwater mark into the digital multimedia content. This
embedding information can be later extracted from or detected in the multimedia to make an assertion aboutthe data authenticity. Digital watermarks remain intactunder transmission/transformation, allowing us toprotect our ownership rights in digital form. Absence of watermark in a previously watermarked image wouldlead to the conclusion that the data content has beenmodified. A watermarking algorithm consists of watermark structure, an embedding algorithm andextraction or detection algorithm. In multimedia
applications, embedded watermark should be invisible,robust and have a high capacity. Invisibility refers todegree of distortion introduced by the watermark and itsaffect on the viewers and listeners. Robustness is theresistance of an embedded watermark againstintentional attack and normal signal processingoperations such as noise, filtering, rotation, scaling,cropping and lossey compression etc. Capacity is theamount of data can be represented by embeddedwatermark.[1]
Watermarking techniques may be classified indifferent ways. The classification may be based on thetype of watermark being used, i.e., the watermark maybe a visually recognizable logo or sequence of randomnumbers. A second classification is based on whether the watermark is applied in the spatial domain or thetransform domain. In spatial domain, the simplestmethod is based on embedding the watermark in theleast significant bits (LSB) of image pixels. However,spatial domain techniques are not resistant enough toimage compression and other image processing
operations.
Transform domain watermarking schemes such asthose based on the discrete cosine transform (DCT), thediscrete wavelet transform (DWT), contourlettransforms along with numerical transformations suchas Singular value Decomposition (SVD) and Principlecomponent analysis (PCA) typically provide higher image fidelity and are much robust to imagemanipulations.[2]Of the so far proposed algorithms,wavelet domain algorithms perform better than other transform domain algorithms since DWT has a number of advantages over other transforms including timefrequency localization, multi resolution representation,
superior HVS modeling, and linear complexity andadaptively and it has been proved that wavelets aregood at representing point wise discontinuities in onedimensional signal. However, in higher dimensions,e.g. image, there exists line or curve-shapeddiscontinuities. Since, 2D wavelets are produced bytensor products of 1D wavelets; they can only identifyhorizontal, vertical, diagonal discontinuities (edges) inimages, ignoring smoothness along contours andcurves. Curvelet transform was defined to represent two
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011
dimensional discontinuities more efficiently, with leastsquare error in a fixed term approximation. Curvelettransform was proposed in continuous domain and itsdiscretisation was a challenge when critical sampling isdesired. Contourlet transform was then proposed by DOand Vetterli as an improvement of Curvelet transform.The Contourlet transform is a directional multi
resolution expansion which can represents imagescontains contours efficiently. The CT employsLaplacian pyramids to achieve multi resolutiondecomposition and directional filter banks to achievedirectional decomposition [3] Due to down samplingand up sampling, the Contourlet transform is Shiftvariant. However shift invariance is desirable in imageanalysis applications such as edge detection, Contour characterization, image enhancement [4] and imagewatermarking. Here, we present a NonSubsampledContourlet transform (NSCT) [5] which is shiftinvariant version of the contourlet transform. TheNSCT is built upon iterated nonsubsampled filter banksto obtain a shift invariant image representation.
In all above transform domain watermarking techniques
including NSCT the watermarking bits would be
directly embedded in the locations of sub band
coefficients. Though here the visual of perception of original image is preserved, the watermarked image
when subjected to some intentional attacks like
compression the watermark bits will get damaged.
Coming to the spatial domain watermarking using
numerical transformation like SVD (Gorodetski [6], liuet al [7]) they provide good security against tampering
and common manipulations for protecting rightful
ownership. But these schemes are non adaptive, thusunable to offer consistent perceptual transparency of
watermarking of different images. To provide adaptive
transparency, robustness to the compressions and
insensitivity to malicious manipulations, we propose a
novel image hybrid watermarking scheme using NSCTand SVD.
In this paper, proposed method is compared withanother which is based on Contourlet Transform and
singular value decomposition (CT-SVD). The peak
signal to noise ratio (PSNR) between the original image
and watermarked image and the normalized correlation
coefficients (NCC) and bit error rate (BER) between
the original watermark and extracted were calculated
with and without attacks. The results show highimprovement detection reliability using proposed
method. The rest of this paper is organized as follows.Section 2 describes the Nonsubsampled contourlet
transform, section 3 describes singular value
decomposition, section 4 illustrates the details of
proposed method, in section 5 experimental results are
discussed without and with attacks, conclusion andfuture scope are given in section 6.
2. NONSUBSAMPLED CONTOURLETTRANSFORM
The Nonsubsampled contourlet transform is a newimage decomposition scheme introduced by Arthur
L.Cunha, Jianping Zhou and Minh N.Do [8]. NSCT is
more effective in representing smooth contours in
different directions of in an image than contourlettransform and discrete wavelet transform. The NSCT is
fully shift invariant, Multi scale and multi direction
expansion that has a fast implementation. The NSCT
exhibits a similar sub band decomposition as that of contourlets, but without down samplers and up samplers
in it. Because of its redundancy the filter design problem
of nonsubsampled contourlet is much less constrained
than that of contourlet. The NSCT is constructed by
combining nonsubsampled pyramids andnonsubsampled directional filter bank as shown in
the multi scale property and nonsubsampled directional
filter bank results the directional property.
(a) (b)
Figure 1 The nonsubsampled contourlet transform (a)nonsubsampled filter bank structure that implements the NSCT.(b) Idealized frequency partitioning obtained with NSCT
2.1 Nonsubsampled Pyramids
The nonsubsampled pyramid is a two channelnonsubsampled filter bank as shown in figure2(a).The H0(z) is the low pass filter and one then setsH1(z) =1-H0(z). the corresponding synthesis filters
G0(z) =G1(z)=1.
the perfect reconstruction condition is given byBezout identity
H0(z)G0(z)+H1(Z) G1 (Z) =1………………(1)
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8/7/2019 Robust Color Image Watermarking Using Nonsubsampled Contourlet Transform
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011
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:
(a) (b)
Figure (2): Nonsubsampled pyramidal filter (a). Ideal frequency response of nonsubsampled pyramidal filter (b).The cascading analysis of three stages nonsubsampled pyramid by iteration of two channels
Nonsubsampled filter banks.
Multi scale decomposition is achieved fromnonsubsampled pyramids by iterating the
nonsubsampled filter banks by up sampling all filters
by 2 in both direction the next level decomposition is
achieved. The complexity of filtering is constantwhether the filtering is with H(z) or an up sampled
filter H(z m ) computed a Trous algorithm The
cascading of three stage analysis part is shown infigure 2( b)
2.2 Nonsubsampled directional Filter Banks:
The directional filter bank (DFB) is constructed fromthe combination of critically-sampled two-channel
fan filter banks and resampling operations. The
outcome of this DFB is a tree-structured filter bank
splitting the 2-D frequency plane into wedges. The
nonsubsampled directional filter bank which is shift
invariant is constructed by eliminating the down andup samplers in the DFB.The ideal frequency response
of nonsubsampled filter banks is shown in figure3 (a)
To obtain multi directional decomposition, thenonsubsampled DFBs are iterated. To obtain the
next level decomposition, all filters are up
sampled by a quincunx matrix given by
Q =
……………..(2)
The analysis part of iterated nonsubsampled filter
bank is shown in figure 3 (b)
(a) (b)
Figure (3) Nonsubsampled directional filter bank (a) idealized frequency response of nonsubsampled directional filter bank.(b) Theanalysis part of an iterated nonsubsampled directional bank.
3. SINGULAR VALUE DECOMPOSITION
Singular value decomposition (SVD) is apopular technique in linear algebra and it hasapplications in matrix inversion, obtaining lowdimensional representation for high dimensional
data, for data compression and data denoising. If A is any N x N matrix, it is possible to find adecomposition of the form
1 1
1 ‐1
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band coefficients obtained in step 5 to get the R
color space of watermarked image.
Step7: Apply the same Steps from Step2 to
Step6 for the G and B color subspaces.
Step 8: Combine the R,G and B color spaces of watermarked image to obtain the color watermarked
image.
Figure 4 Watermark Embeddign Algorithm
4.2 Watermark Extraction Algorithm
The watermark extraction algorithm is shown in
Figure 5. The Steps of watermark extractionalgorithm are as follows.
Step1: Separate the R,G,B color spaces of
watermarked image.
Step2: Apply Nonsubsampled Contourlet
Transform to the R color space obtained in step1.
Step3: Apply SVD to mid frequency sub-band of
R color space of transformed watermarked image.
Step4: Extract the singular values from mid
frequency sub-band of R color space of
watermarked and host image
i, e λ W = ( λ I’ - λ I )/ α
Where λ I is singular value of watermarked image.
Step5: Apply inverse SVD to obtain mid
frequency coefficients of R color space of
transformed watermark image using Step 3.
Step6: Apply inverse NSCT using the
coefficients of the mid frequency sub-band to obtainthe R color space of Watermark image.
Step7: Repeat the Steps 2 to 6 for G and B color
spaces.
Step8: Combine the R,G and B color spaces to
get the color watermark.
Figure 5 Watermark Extracting Algorithm
5. EXPERIMENTAL RESULTS
In the experiments, we use the true color “tajmahal.jpg” of size 256X256 as host image asshown in the Figure 6 and true color “lena.jpg” of size 128 X 128 as watermark as shown in Figure 7.The experiment is performed by taking scalingfactor alpha as 0.5.The results show that there are noperceptibly visual degradations on the watermarkedimage shown in Figure 8 with a PSNR of 45.2253dB. Extracted watermark without attack isshown in Figure 9 with NCC around unity and BER of 0.1339. MATLAB 7.6 version is used for testingthe robustness of the proposed method.
The proposed algorithm is tested for different host
images such as “lotus.jpg”, ”Baboon.jpg”,
”Barbara.jpg”, ”Way.jpg” ,”Horse.jpg” and“Wheel.jpg” as shown in Table 1 and it is observed
that there are no visual degradations on the respected
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