robust color image watermarking using nonsubsampled contourlet transform
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8/7/2019 Robust Color Image Watermarking Using Nonsubsampled Contourlet Transform
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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 9, No. 3, March 2011
Robust 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 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
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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
figure (1).The nonsubsampled pyramid structure results
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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:
(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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A=USVT
Where U and V are orthogonal matrices of order
N x N and N x N such that UTU=I,VTV=I , and the
diagonal matrix S of order N x N has elements λ i (i=1,2,3,..n) , I is an identity matrix of order N x N.
The diagonal entries are called singular values of
matrix A, the columns of U matrix are called the left
singular values of A, and the columns of V are
called as the right singular values of A.The general properties of SVD are [1], [2], [9]
a) Transpose: A and its transpose AT have the
same non-zero singular values.b) Flip: A, row-flipped Arf , and column-flipped Acf have the same non-zero singular values.
c) Rotation: A and Ar (A rotated by an
arbitrary degree) have the same non-zero singular values.
d) Scaling: B is a row-scaled version of A by
repeating every row for L1 times. For each non-zero
singular value λ of A, B has √L1λ . C is a column-
scaled version of A by repeating every column for L2 times. For each nonzero singular value λ of A, C
has √L2λ . If D is row-scaled by L1 times and
column-scaled by L2 times, for each non-zerosingular value λ of A, D has √L1L2λ .
e) Translation: A is expanded by adding rowsand columns of black pixels. The resulting matrixAe has the same Non-zero singular values as A.
The important properties of SVD from the view
point of image processing applications are:
1. The singular values of an image have verygood stability i.e. When a small perturbation is
added to an image, their singular values do not
change significantly.
2. Singular value represents intrinsic algebraic
image properties.
Due to these properties of SVD, in the last fewyears several watermarking algorithms have been
proposed based on this technique. The main idea of this approach is to find the SVD of a original image
and then modify its singular values to embedded the
watermark. Some SVD based algorithms are purely
SVD based in a sense that only SVD domain is used
to embed watermark into original image. Recentlysome hybrid SVD based algorithms have been
proposed where different types of transform domain
including discrete cosine transform (DCT), discrete
wavelet transform (DWT), Contourlet transform(CT) etc have been used to embed watermark into
original image. here the proposed scheme uses
nonsubsampled contourlet transform(NSCT) alongwith SVD for watermarking to obtain better
performance compared to existing hybrid
algorithms.
4. PROPOSED ALGORITHM
In this paper, Nonsubsampled Contourlet
Transform and SVD based hybrid technique isproposed for color image watermarking that uses
true color images for both watermark and host
images. The robustness and visual quality of
watermarked image is tested with three quantifierssuch as PSNR, NCC and Bit Error Rate. It is
investigated whether the NSCT-SVD advantages
over CT-SVD for color image watermarking with
their extra features would provide any significance
in terms of watermark robustness and invisibility.4.1 , 4.2 explain the watermark embedding and
extraction algorithm [10],[11]
4.1 Watermark Embedding Algorithm
The proposed watermark embedding algorithm
is shown in Figure 4. The steps of watermark embedding algorithm are as follows.
Step1: Separate the R G B color spaces of both
host and watermark color images.
Step2: Apply Nonsubsampled Contourlet
Transform to the R color space of both host image
and watermark image to decompose them into subbands.
Step3: Apply SVD to mid frequency sub-band of
CT of R color space of both host and watermark
image.
Step4: Modify the singular values of midfrequency sub-band coefficients of R color space of
host image with the singular values of mid
frequency sub-band coefficients of R color space of watermark image using spread spectrum technique.
i.e. λ I’ = λ I + α λ W.,
Where α is scaling factor [9], λ I is singular valueof R color space of host image, λ W is singular valueof R color space of watermark and λ I’
becomes
singular value of R color space watermarked image.
Step5: Apply inverse SVD on modified singular
values obtained in step4 to get the mid frequency
sub-band coefficients of watermarked image.
Step6: Apply inverse Nonsubsampled
Contourlet Transform to the mid frequency sub-
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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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watermarked images. For all the different Host test
images, the watermark is effectively extracted with
around unity NCC. Various intentional and non-
intentional attacks are tested for robustness of the
proposed watermark algorithm includesJPEG,JPEG2000compressions, low pass filtering,
Rotation, Histogram Equalization ,Median Filtering,
Salt &Pepper Noise, Weiner Filtering, GammaCorrection, Gaussian Noise, Rescaling, Sharpening
Blurring ,Contrast Adjustment ,Automatic cropping,
Dilation, Row Colum Copying, Row Colum
removing, color to Gray scale conversion ,shearing
and sharpening. The term robustness describes thewatermark resistance to these attacks and can be
measured by the bit-error rate which, is defined as the
ratio of wrong extracted bits to the total number of embedded bits.
In table 2, extracted watermark and attacked
watermarked image with NCC and BER are shown.
The quality and imperceptibility of watermarkedimage is measured by using PSNR. The PSNR iscalculated separately for R, G, B color space of
watermarked image with respect to the respective
color space of host image using eq.3 [12]. The
final PSNR of watermarked image is taken as meanof PSNR obtained with three color spaces. The
similarity of extracted watermark with original
watermark embedded is measured using NCC. The
NCC is calculated using eq. (4) [13]for the three
color spaces and their mean is taken as the resultant
Normalized Correlation coefficient. The proposedmethod is also tested for binary and grayscale
watermark image of size 128x128 and watermarked
and extracted watermark are shown in table 3.
……….….(3)
Normalized Correlation Coefficient:
………..(4)
Fig 6:Original image-"Tajmahal.jpg”
Fig 7:Watermark image-"Lena.jpg”
Fig 8:Watermarked LenaPSNR= 45.2253
Fig 9:ExtractedWatermark
Ncc=0.9991,Ber=0.1339
TABLE 1: WATERMARKED AND EXTRACTED WATERMARK WITH PSNR, NCC, AND BER FOR DIFFERENT ORIGINALIMAGES.
Original image“lotus.jpg”
Watermark image“LENA.jpg”
Watermarked image withPSNR=46.2785
Extracted imageNCC= 0.9983,Ber=0.1610
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Original image“baboon.jpg”
Watermark image“LENA.jpg”
Watermarked image with
PSNR=44.8322Extracted image
NCC=0.9992, Ber=0.1342
Original image
“barbara.jpg”
Watermark image
“LENA.jpg”
Watermarked image with
PSNR=44.4930
Extracted image
NCC=0.9994,Ber=0.1299
Original image
“way.jpg”
Watermark image
“LENA.jpg”
Watermarked image with
PSNR=44.7550
Extracted image
NCC= 0.9994, Ber=0.1140
Original image
“horse.jpg”
Watermark image
“LENA.jpg”
Watermarked image with
PSNR= 44.7308
Extracted image
NCC= 0.9994, Ber=0.1201
Original image“wheeljpg”
Watermark image“LENA.jpg”
Watermarked image withPSNR= 45.5204
Extracted imageNCC= 0.9985, Ber=0.1614
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TABLE 2: EXTRACTED WATERMARKS WITH NCC AND BER FOR DIFFERENT ATTACKS ALONG WITH ATTACKEDWATERMARKED IMAGE
Jpeg compression Ncc= 0.9985,Ber=0.3306 Jpeg2000Ncc= 0.9995,Ber=0.1056
Salt & pepper noise Ncc= 0.6948, Ber=0.4503 Low Pass filtering Ncc= 0.9729 Ber=0.2995
utomatic cropping Ncc= 0.9538 Ber=0.3449 Histogram Equalization Ncc= 0.9808 Ber=0.3128
Rotation Ncc= 0. 0.9951 Ber=0.2958 Median filtering Ncc= 0.9484 Ber=0.3178
Contrast adjustment Ncc= 0.9985 Ber= 0.1613 Weiner filter Ncc= 0.9982 Ber=0.2051
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Gamma correction Ncc= 0.9989 Ber=0.1387 Gaussian Noise Ncc= 0.8399 Ber=0.3120
Sharpening Ncc= 0.8379 Ber=0.3967 Gaussian Blurring Ncc= 0.9719 Ber=0.3003
Shearing Ncc= 0.9744 Ber=0.2889 Dilatations= 0.9443 Ber=0.3332
Color to grayscale Ncc= 0.8163 Ber=0.3490 Row & column removal Ncc=0.9977 Ber=0.1930
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Row column copying Ncc= 0.9902 Ber=0.9734 Scaling (150%) Ncc = 0.9187
TABLE 3: WATERMARKED AND EXTRACTED WATERMARK IMAGES FOR BINARY AND GRAYSCALE WATERMARK
Original image“tajmahal.jpg
Binary Watermark image“ksp.bmp”.
Watermarked imagePSNR= 47.6710
Extracted imageNcc= 0.9995, Ber=0.0157
Original image“tajmahal.jpg
Binary Watermark image“lena.bmp”.
Watermarked imagePSNR= Inf
Extracted imageNcc= 1,Ber= 0
Original image “tajmahal.jpg Gray scale Watermark image“Lena.jpg”.
Watermarked imagePSNR=45.2629
Extracted imageNcc= 0.9992,Ber= 0.1345
In table 4, the proposed method is compared
with contourlet and SVD based algorithm [11].Itdemonstrates that proposed method is superior to
salt pepper noise, Rotation, Gaussian Noise,
Sharpening, Row and Colum removal and Rowand column copying.
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TABLE 4: COMPARISON OF CT+SVD ANDNSCT + SVD
S.No ATTACK Normalized Correlation
NSCT+SVD CT+SVD
1 Jpegcompression
0.9985 0.9996
2 Jpeg2000 0.9995 0.99963 Salt & pepper
noise
0.6948 0.6823
4 Low passfiltering
0.9729 0.9839
5 Automaticcropping
0.9538 0.9658
6 HistogramEquation
0.9808 0.9733
7 Rotation 0.9958 0.9750
8 Medianfiltering
0.9484 0.9680
9 Contrastadjustment
0.9985 0.9991
10 Weiner filter 0.9982 0.9989
11 Gammacorrection
0.9989 0.9995
12 Gaussian Noise 0.8399 0.7538
13 Sharpening 0.8379 0.8212
14 GaussianBlurring
0.9719 0.9841
14 Shearing 0.9744 0.9857
16 dilatations 0.9443 0.9678
17 Color tograyscale
0.8163 0.8693
18 Row & Columremoval
0.9977 0.9972
19 Row Columcopying
0.9902 0.9820
20 Scaling (150%) 0.9187 0.9417
6. CONCLUSION:
In this paper, a novel robust hybrid watermarking
scheme is proposed for authentication of color images using nonsubsampled contourlet transform
and singular value decomposition. Watermark is
embedded in all color spaces of host image by
modifying singular values of mid frequency sub band
coefficients with respect to watermark mid frequency
sub band coefficient with suitable scaling factor. Therobustness of watermark is improved for common
image procession operations by combining both theconcepts of nonsubsampled contourlet transform andsingular value decomposition. The proposed
algorithm is tested for different host images and
respective watermark images are obtained without
any visual degradation. The proposed algorithm
preserves high perceptual quality of the watermarked
image and shows an excellent robustness to attackslike Salt and Pepper Noise, Gaussian Noise, Row
Column Copying, and Row Column Removal.
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AUTHORS PROFILE:
C.V Narasimhulu
He received his Bachelor degree in Electronics and
Communication Engineering from S.V. University,Tirupati, India in 1995 and Master of Technology in
Instruments and Control Systems from Regional
Engineering College Calicut, India in 2000.He is
currently pursuing the Ph.D degree in the departmentof Electronics and Communication Engineering from
Jawaharlal Nehru Technological University
Kakinada, India. He has more than 15 years
experience of teaching under graduate and post
graduate level. He is interested in the areas of signalprocessing and multimedia security
K.Satya Prasad
Received his Ph.D degree from IIT Madras, India. He
is presently working as professor in ECE department,
JNTU college of Engineering Kakinada and Rector of
JNT University, Kakinada, India. He has more than
30 years of teaching and research experience. He
published 30 research papers in international and 20research papers in National journals. He guided 8
Ph.D thesises and 20 Ph.D thesises are under his
guidance. His area of interests is digital signal andimage processing, communications, adhoc networks
etc..,
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