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Hybrid Robust Watermarking Technique

Based on DWT, DCT and SVD

Harish N J, B B S Kumar & Ashok Kusagur

Department of ECE, RRCE,Bangalore

E-mail : harishnj25@gmail.com, bbskumarindia@yahoo.com, ashok.kusagur@gmail.com

Abstract - In this paper, we propose a hybrid

watermarking scheme for digital images based on singular

value decomposition (SVD). The two key aspects of

watermarking schemes are copyright protected and

robustness. In this, we are embedded the principal

components of the watermark in the DCT domain of DWT

subband of host image, for providing copyright protection

as well as reliability. Since Scaling factor is an image

dependent. Experimental results are provided to illustrate

that the proposed scheme is able to withstand a variety of

image processing attacks as well as imperceptibility.

Keywords - Copy Right Protection, Discrete Cosine

transform, Discrete Wavelet Transform, Singular Value

Decomposition, Watermarking.

I. INTRODUCTION

Watermarking has been considered to be a

promising solution that can protect the copyright of

multimedia data through Trans coding, because the

embedded message is always included in the data.

However, today, there is no evidence that watermarking

techniques can achieve the ultimate goal to retrieve the

right owner information from the received data after all

kinds of content-preserving manipulations. Because of

the fidelity constraint, watermarks can only be

embedded in a limited space in the multimedia data.

There is always a biased advantage for the attacker

whose target is only to get rid of the watermarks by

exploiting various manipulations in the finite

watermarking embedding space.

There are many solutions that have been proposed

like Cryptography [1], Steganography and

Watermarking [2]. The watermarking technique

provides one of the best solutions among them. This

technique embeds information so that it is not easily

perceptible to the others. The embedded watermark

should not degrade the quality of the image and should

be perceptually invisible to maintain its protective

secrecy [1].

The important requirements to be satisfied by any

digital watermarking scheme are as follows.

Fidelity: This is about the perceptual similarity between

the original image and the watermarked image. The

watermark should be imperceptible and no visual effect

should be perceived by the end user. The watermark

may degrade the quality of the content, but in some

applications a little degradation may accepted to have

higher robustness or lower cost.

Robustness: Even though an unauthorized person

performs some modifications to the watermarked image

through some common signal processing attacks and

compression attacks etc. But the watermark can still be

extracted. The scheme should resist the various attacks

from hackers.

Non-invertibility: If we are unable to generate the same

watermarked image with the help of different

combinations of host and watermark images then it is

called as Non-invertible watermarking scheme. This

provides copyright protection.

According to the domain in which watermark is

embedded, these are divided into spatial domain and

transform domain schemes. Embedding the watermark

in the spatial domain is the direct method. It has less

computational cost, high capacity, more perceptual

quality but less robust and it mainly suits for

authentication applications. In the frequency domain

schemes, we embed the watermark with the transformed

coefficients of host image. It has more robust, less

control of perceptual quality and mainly suits for

copyright application. The robustness and perceptual

quality of the watermarking schemes are mainly

depends on how much percentage of the watermark is

embedded into host image i.e., Scaling factor.

The main impediments of SVD based watermarking

schemes are as follows:

International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE)

ISSN (Print): 2278-8948, Volume-2, Issue-5, 2013

138

False positive Problem: when a specific watermark is

detected from content in which a different watermark

was embedded, causing an ambiguous situation [6], [7]

and [8].

Diagonal Line Problem: If we modify the singular

values of the cover image directly with the watermark

image then there will be a diagonal line in the

reconstructed watermark from the attacked watermarked

images [9].

So to avoid the false positive problem and diagonal

line problems, here we are embedding the principle

components [7] of the watermark in the host image. The

scaling factor controls the robustness and transparency

of the scheme, which is imadependent.

The discrete cosine transform (DCT) represents an

image as a sum of sinusoids of varying magnitudes and

frequencies. The DCT has special property of Energy

Compaction. The Discrete Wavelet Transform is useful

for multi resolution analysis and subband coding. In this

paper we are embedding the watermark in the DCT

domain of DWT coefficients of host image. Hence we

can achieve better performance when compared to using

DCT alone [13].

II. EXISTING SYSTEM

There are many solutions that have been proposed

like Cryptography, Steganography and Watermarking.

The watermarking technique provides one of the best

solutions among them. This technique embeds

information so that it is not easily perceptible to the

others. The embedded watermark should not degrade the

quality of the image and should be perceptually invisible

to maintain its protective secrecy. The robustness and

perceptual quality of the watermarking schemes are

mainly depends on how much percentage of the

watermark is embedded into host image i.e., Scaling

factor.

Disadvantages:

False positive problem occurs

Diagonal line problem occurs

PSNR is less

Correlation coefficient is less

III. PROPOSED SYSTEM

The proposed Watermarking scheme is

implemented as in two phases first watermarking and

then extraction. First on image we will apply haar

wavelet transform and will get four sub band images.

On sub band we are applying first DCT and on that DCT

matrix we are going to apply SVD in all values. Then

the watermarking step is performed by scaling down the

pixel values of watermark and then embedding those

values into the cover image. After this the watermarked

image is obtained on which various attacks are applied

in order to achieve the robustness in watermarking.

Then we follow the extraction phase where we apply

again the wavelet transform, DCT and SVD and extract

the watermark under attacks. Finally the correlation is

determined between the watermark extracted and

original watermark.

Advantages:

False positive problem is resolved

Diagonal line problem is resolved

PSNR is good

Correlation coefficient is high

IV. PROBLEM DESCRIPTION

The main motive to implement this project is to

solve the problems arising like False Positive and

Diagonal Line problem. Also, the demerits of low PSNR

and less correlation coefficient after extraction phase is

to be resolved here. Considering these disadvantages of

previous existing watermarking techniques, the project

implemented to extract the image having a good quality

of data and also if any type of attack is inserted in the

channel and the effect of this attack should be nullified.

V. THE CHOICE OF MATLAB

MATLAB [2] brings to Digital Image Processing is

an extensive set of functions for processing

multidimensional arrays of which images(two-

dimensional numerical arrays) are a special case. The

Image Processing Toolbox(IPT) with Wavelet

Toolbox(WT)is a collection of function that extend the

capability of the MATLAB numeric computing

environment. These functions, and the expressiveness of

the MATLAB language, make many image-processing

operations easy to write in a compact, clear manner, thus

providing an ideal software prototype environment for

the solution of image processing problems.

There are a several low-level programming

languages that can be used to demonstrate the

application of DSP & DIP, such as C++, and Java; but

these require a high proficiency in programming.

There are several high-level software packages that

can be used to teach signal processing, such as

Mathematica, Octave, and MATLAB. A good overview

of these and other packages in terms of engineering

education can be found. Mathematica is meant more for

symbolic mathematics than creating applications.

International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE)

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139

Octave, a free open-source mathematics software

application, is quite compatible with MATLAB code,

but it lacks much of the rich library of functions

available in MATLAB. In addition there is no easy way

to create graphical user interfaces (GUIs).

VI. DIGITAL WATERMARKING

Digital watermarking is the process of embedding

information into a digital signal which may be used to

verify its authenticity or the identity of its owners, in the

same manner as paper bearing a watermark for visible

identification. In digital watermarking, the signal may

be audio, pictures, or video. If the signal is copied, then

the information also is carried in the copy. A signal may

carry several different watermarks at the same time.

In visible digital watermarking, the information is

visible in the picture or video. Typically, the

information is text or a logo, which identifies the owner

of the media. The image on the right has a visible

watermark. When a television broadcaster adds its logo

to the corner of transmitted video, this also is a visible

watermark.

In invisible digital watermarking, information is

added as digital data to audio, picture, or video, but it

cannot be perceived as such (although it may be possible

to detect that some amount of information is hidden in

the signal). The watermark may be intended for

widespread use and thus, is made easy to retrieve or, it

may be a form of steganography, where a party

communicates a secret message embedded in the digital

signal. In either case, as in visible watermarking, the

objective is to attach ownership or other descriptive

information to the signal in a way that is difficult to

remove. It also is possible to use hidden embedded

information as a means of covert communication

between individuals.

One application of watermarking is in copyright

protection systems, which are intended to prevent or

deter unauthorized copying of digital media. In this use,

a copy device retrieves the watermark from the signal

before making a copy; the device makes a decision

whether to copy or not, depending on the contents of the

watermark.

VII. DIGITAL WATERMARKING LIFE-CYCLE

PHASES

Fig.1: General digital watermark life-cycle phases with

embedding, attacking, and detection and retrieval functions

The information to be embedded in a signal is

called a digital watermark, although in some contexts

the phrase digital watermark means the difference

between the watermarked signal and the cover signal.

The signal where the watermark is to be embedded is

called the host signal. A watermarking system is usually

divided into three distinct steps, embedding, attack, and

detection. In embedding, an algorithm accepts the host

and the data to be embedded, and produces a

watermarked signal.

Then the watermarked digital signal is transmitted

or stored, usually transmitted to another person. If this

person makes a modification, this is called an attack.

While the modification may not be malicious, the term

attack arises from copyright protection application,

where pirates attempt to remove the digital watermark

through modification. There are many possible

modifications, for example, lossy compression of the

data (in which resolution is diminished), cropping an

image or video or intentionally adding noise.

Detection (often called extraction) is an algorithm

which is applied to the attacked signal to attempt to

extract the watermark from it. If the signal was

unmodified during transmission, then the watermark still

is present and it may be extracted. In robust digital

watermarking applications, the extraction algorithm

should be able to produce the watermark correctly, even

if the modifications were strong. In fragile digital

watermarking, the extraction algorithm should fail if any

change is made to the signal.

VIII. DISCRETE COSINE TRANSFORM

The discrete cosine transform (DCT) is a function

that has the ability to convert a signal into elementary

frequency components. It represents an image as a sum

of sinusoids of varying magnitudes and frequencies. The

popular block-based DCT transform segments an image

non-overlapping block and applies DCT to each block.

This result in giving three frequency sub-bands: low

frequency sub band, mid-frequency sub-band and high

frequency sub-band. DCT-based watermarking is based

on two facts [10]. The first fact is that most of the signal

energy lies at low-frequencies sub band which contains

the most important visual parts of the image. The second

fact is that high frequency components of the image are

usually removed through compression and noise attacks.

[3] The watermark is therefore embedded by modifying

the coefficients of the middle frequency sub band so that

the visibility of the image will not be affected and the

watermark will not be removed by compression.

International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE)

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IX. DISCRETE WAVELET TRANSFORM

In mathematics, a wavelet series is the best

representation of a square integrable (real or complex-

valued) function by certain orthonormal series generated

by a wavelet. This article provides a formal,

mathematical definition of an orthonormal wavelet and

of the integral wavelet transform. The wavelet transform

can provide us with the frequency of the signals and the

time associated to those frequencies, making it very

convenient for its application in numerous fields. For

instance, signal processing of accelerations for gait

analysis.

In numerical analysis and functional analysis, a

discrete wavelet transform (DWT) is any wavelet

transform for which the wavelets are discretely sampled.

As with other wavelet transforms, a key advantage it has

over Fourier transforms is temporal resolution: it

captures both frequency and location information

(location in time). The discrete wavelet transform has a

huge number of applications in science, engineering,

and mathematics and computer science. Most notably, it

is used for signal coding, to represent a discrete signal in

a more redundant form, often as a preconditioning

for data compression.

Fig. 2: Block diagram of filter banks of DWT first level

The Wavelet Transform of Digital Image

The basic thought of wavelet transform is using the

same function by expanding and shifting to approach the

original signal. The wavelet coefficients carry the time-

frequency information in certain areas. It has good local

characteristics both in time domain and frequency

domain. It can maintain the fine structure of the original

images in various resolutions and it is convenient to

combine with human vision characteristics. Compared

with the orthogonal wavelet, bi-orthogonal wavelet has

more obvious superiority in image processing because it

balances the orthogonality and symmetry. In addition,

the reconstructing signal of biorthogonal wavelet

transform is suitable to embed watermark for its

balance. Daubechies9/7 wavelet is recommended in

JPEG2000. In this algorithm Daubechies9/7

biorthogonal wavelet is selected because it is one of the

most suitable biorthogonal wavelets implicated in digital

watermark.

A digital image is decomposed into three high

frequency sub bands and a low frequency sub band by

one level wavelet transform. The low frequency sub

band can be decomposed continuously. With the more

levels the image is decomposed by wavelet transform,

the energy of the image is diffused better and the

stronger image intensity can be embedded. So the

wavelet decomposing levels adopted in the algorithms

should be chosen as far as possible.

The symmetry extension is adopted in wavelet

decomposing process, while repeat symmetrical

extension is adopted in wavelet reconstruction process.

The standard test sequence Lena is decomposed by two-

level wavelet decomposition into low frequency sub

band LL2, horizontal high frequency sub bands LH2

LH1, vertical high frequency sub band HL2 HL1 and

diagonal direction high frequency sub band HH2 HH1,

as shown in figure 3. (In order to display, the high

frequency sub bands coefficients are amplified.)

Fig.3: (a) Lena image after one level wavelet decomposition

and (b) Two level wavelet decomposition

X. SINGULAR VALUE DECOMPOSITION

In linear algebra, the singular value decomposition

(SVD) is a factorization of a real or complex matrix,

with many useful applications in signal processing and

statistics. Formally, the singular value decomposition of

an m×n real or complex matrix M is a factorization of

the form

*VUM

where U is an m×m real or complex unitary matrix,

Σ is an m×n rectangular diagonal matrix with

nonnegative real numbers on the diagonal, and V* (the

conjugate transpose of V) is an n×n real or complex

unitary matrix. The diagonal entries Σi,i of Σ are known

as the singular values of M. The m columns of U and the

n columns of V are called the left-singular vectors and

International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE)

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141

right-singular vectors of M, respectively. US are called

the principal components of M.

XI. ALGORITHAMS FOR PROPOSED METHOD

The proposed Watermarking scheme is

characterized as follows:

A. Watermark Embedding:

Step 1: Apply one-level Haar DWT to decompose the

host image A, into four sub bands i.e.

ALL , AHL , ALH , and AHH .

Step 2: Consider AHL and perform 2D DCT and using

zig-zag sequence, map the DCT Co-efficients of AHL

into four quadrants: B1, B2, B3 and B4.

Step 3: Apply SVD to all four quadrants, Bk =UkSkVkT ,

where k=1, 2, 3 and 4.

Step 4: Apply the SVD on the watermark image and

calculate the principal components of the watermark.

W = UW SW VWT , P = UW ∗ SW

Step 5: Divide the principal components P, into four

quadrants: P1, P2, P3 and P4.

Step 6: Modify the singular values of the DCT

coefficients of the cover image with the principal

components of watermark image i.e.,

SWk = Sk + Φ. Pk , k = 1, 2, 3 and 4.

Step 7: Perform, BWk =Uk Swk VkT , where k = 1, 2,

3and4.

Step 8: Map the coefficients of BWk back to their

original positions and apply DCT to produce the

modified HL band, AHLW .

Step 9: Perform the inverse DWT by using modified and

non-modified coefficients to get the watermarked image,

AW .

B. Watermark Extraction:

Step 1: Apply one-level Haar DWT to decompose the

watermarked (possibly attacked) image Aw into four

subbands: ALL∗ , AHL

∗ , ALH∗ , and AHH

∗ .

Step 2: Apply DCT on AHL∗ and using zig-zag scan

arrange the DCT coefficients of AHL∗ into four

quadrants B1∗, B2

∗, B3∗ and B4

∗ .

Step 3: Subtract each quadrant with the original

transformed quadrants: Ck= Bk∗ - Bk , where k=1, 2, 3,

and 4.

Step 4: Compute the distorted principal component parts

EPCk = (Uk ∗ Ck ∗ VkT )/ Φ

Step 5: Construct the distorted principal component

from their parts i.e.

Epc = Epc

1 Epc2

Epc3 Epc

4

Step 6: Obtain the extracted watermark.

EW = Epc ∗ VWT

XII. EXPERIMENTAL RESULTS

In this paper the simulation process is implemented

in MATLAB using different types of host and

watermark images. For testing purpose gray scaled

(JPEG format) image is used.

The opted host (original) and watermark images are as

follows:

Lena Home Girl

Fig.4: Host or Cover (original) images of size 256 x 256 pixels

Text Text Girl

Fig.5: Watermark (original) images of size 128x128 pixels

For robustness inspection of the scheme the

watermarked image was tested against several types of

attacks namely Histogram equalization, Speckle noise,

Gaussian noise, Rotate-45, Best contrast, Salt & Pepper

noise, Poisson noise, JPEG compression, Low pass filter

and High pass filter.

The quality of the watermarked image can be

estimated using peak-signal-to-noise ratio (PSNR) and is

calculated as follows

MSEPSNR

2

10

255log10

Where MSE represents the mean square error and is given

by

International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE)

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142

Where A and Aw are original and watermarked

images. The similarities between the original, W and

extracted watermarks, eW can be determined by using the

normalized correlation coefficient (NC) and it is defined

as follows:

21

1 1 1

2

1

1 1

)),((*)),((

)),()()),((

w

n

j

m

i

n

j

w

m

i

m

I

n

j

w

jiEwjiw

wjiwjiw

NC

where w and Ew are mean values of W and Ew

respectively.

The following figures represent the extracted

watermark images with several attacks of the proposed

scheme.

The different values of PSNR & MSE for different

images are listed in Table 1. Also for different attacks

the values of Normalized Correlation Coefficient and

Correlation Coefficient also changes. The different

values of Normalized Correlation Coefficient and

Correlation Coefficient for different attacks have been

listed in Table 2.

Fig.6: Extracted watermark images with several attacks

Table 1: Different values of PSNR and MSE for

different images

Image PSNR (in dB) MSE

Lena 46.0068 1.6308

House 45.7364 1.6416

Girl 45.9573 1.6495

Boat 45.9804 1.6407

Table 2: Different values of NCC and CC for different

attacks on Lena Image

Noise Type

Normalized

Correlation

coefficient

(NCC)

Correlation

Coefficient

(NC)

Histogram equalize 0.949 0.918

Speckle noise 0.982 0.989

Gaussian noise 0.961 0.969

Rotate by +45

degree 0.992 0.998

Best contrast 0.94 0.918

Salt n pepper noise 0.946 0.894

Poisson noise 0.944 0.939

JPEG Compression 0.987 0.993

Low Pass Filter 0.982 0.991

High Pass Filter 0.982 0.991

XIII. CONCLUSIONS

In this proposed paper taken for analysis of

different cover images like Lena and House for

comparing the difference between PSNR and MSE

values. In all the cases the value of PSNR is well above

20dB which shows the good quality of embedding

algorithm in comparison of other techniques like DWT

watermarking or DCT watermarking of DWT and SVD

watermarking, the evaluated PSNR value limited up to

20-30dB along with diagonal line problems or false

positive problems.

By embedding the principle components of the

watermark into the DCT of horizontal sub band of DWT

decomposition of host image, it provides better

imperceptibility as well as reliability in the quality and

recovery of image. By implementing, can avoid the false

positive problem and hence provides the copyright

protection. From the extracted watermarks have proved

the there is no diagonal line problem. And also achieved

the suitable scaling factor for any watermark which is

image dependent. Hence the robustness and

transparency of the scheme for any watermarking can be

processed.

The performance of the proposed method is tested

by applying 1-level DWT compression technique and

image processing attacks like Histogram equalization,

Speckle noise, Gaussian noise, Rotate-45, Best contrast,

Salt & Pepper noise, etc. and has got the values of NCC

and CC are all above 0.85 which are acceptable. Thus

proposed method is robust as well as reliable

watermarking technique. The scheme has been tested

International Journal of Advanced Electrical and Electronics Engineering, (IJAEEE)

ISSN (Print): 2278-8948, Volume-2, Issue-5, 2013

143

with success on various test images on a MATLAB

simulation platform.

XIV. FUTURE SCOPE

This project further can be extended with multilevel

DWT or two levels DCT or two levels SVD to enhance

PSNR and Normalized Correlation Coefficient values.

Also the method can be tried to analyze watermarking

for RGB or color images with embedding in all three

channels and extraction can also be done from all the

three channels. Also, instead of hiding watermark image

inside the cover image the project can be extended for

hiding a text message inside the cover image. This

technique can also be analyzed for audio watermarking

or video watermarking.

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