cascade approach of dwt-svd digital image watermarking

7/29/2019 Cascade Approach of DWT-SVD Digital Image Watermarking

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Cascade approach of DWT-SVD digital image watermarking

Ajit k. bhovi, Prashant R. Sangulagi

Department of Electronics and Communication Engineering, BEC, Bagalkot

[email protected],[email protected]

Abstract

Digital image watermarking is a method of embedding

information into the host image in order to make the

image secure against any possible attacks. Information

can be an image or a data whose size is less than the host

image, such that embedding should not alter the original

characteristics of host image to a large extent. The

embedded information is called watermark, it can be

extracted whenever required and the originality of the

image can be tested by using a set of performance

parameters. Watermarking method has wide range of

applications in the field of secured data transfer. In this

work, we have implemented digital watermarking

techniques, namely, DWT-SVD cascade technique where

we combined both SVD and DWT techniques.

Keyword: Host image, watermark, embedding, extracting

and performance parameters.

I. Introduction

A recent proliferation and success of the Internet, togetherwith availability of relatively inexpensive digital

recording and storage devices has created an environment

in which it became very easy to obtain, replicate and

distribute digital content without any loss in quality. This

has become a great concern to the multimedia content

(music, video, and image) publishing industries, because

technologies or techniques that could be used to protect

intellectual property rights for digital media, and prevent

unauthorized copying did not exist.

While encryption technologies can be used to prevent

unauthorized access to digital content, it is clear that

encryption has its limitations in protecting intellectual

property rights: once a content is decrypted, theresnothing to prevent an authorized user from illegally

replicating digital content. Some other technology was

obviously needed to help establish and prove ownership

rights, track content usage, ensure authorized access,

facilitate content authentication and prevent illegal

replication.

This need attracted attention from the research

community and industry leading to a creation of a new

information hiding form, called Digital Watermarking.

Many research efforts over the past decade have enabled

digital watermarking to establish itself as a potential

solution for the protection of ownership rights and

policing information piracy of multimedia elements like

images, audio and video. A digital watermark is an

invisible signature embedded inside multimedia content to

show authenticity and ownership. It contains useful

certifiable information for the owner of the host media,such as producer's name, company logo, etc; the

watermark can be detected or extracted later to make an

assertion about the host media.

An image watermarking scheme should at least meet the

following requirements: transparency and robustness.

Transparency means that the embedded watermark should

be perceptual invisible and robustness means that the

embedded watermark shouldnt be erased by any attack

that maintains the host image quality acceptable. Trade

off between transparency and robustness is one of the

most important issues in image watermarking.

This article summarizes the both DWT and SVDtechniques and their cascade approach. The remainder of

the paper is organized as follows. Section II introduces

brief about both techniques DWT and SVD. Section III

presents an algorithm of cascade approach of DWT-SVD.

Section IV presents experimental results. Sections V give

conclusion and future work.

II. Techniques of digital image watermarking

The techniques [1] [6] which are present in digital image

watermarking are

Singular Value Decomposition (SVD) Discrete wavelet transform (DWT) Discrete cosine transform (DCT)

In these techniques we are interested in DWT and SVD

where we are combining both. In next sub section we give

brief introduction to both techniques.
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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2D DWT Applied to an Image

An image can be represented in the form of a matrix,

when a DWT of that matrix is taken it transforms that

image into the bands of the frequencies called very low

frequency(A), high frequency(D) and middle

frequency(H,V) for the first level. DWT is applied on the

higher levels on the band (A) to generate frequency

bands.

Figure 1 explains the 3level DWT applied on image

Fig 1 level DWT applied to an image

Figure 2 illustrates the example of the DWT applied on

the image Ganapati.

Fig. 2 DWT2 applied to an image GANAPATI

Singular Value Decomposition Based Watermarking

Method

SVD is based on a theorem from linear algebra which

says that a rectangular matrix A can be broken down into

the product of three matrices - an orthogonal matrix U, a

diagonal matrix S, and the transpose of an orthogonal

matrix V . The theorem is usually presented something

like this:

A = USV

Where A is m*n matrix, U is m*m matrix, S is m*n

matrix & V is n*n matrix

Where UU = I; VV = I; the columns of U are

orthonormal eigenvectors of AA , the columns of V are

orthonormal eigenvectors of AA, and S is a diagonal

matrix containing the square roots of eigen values from U

or V in descending order.

III. DWT-SVD Based Watermarking Method

An alternative to the DWT-DCT and SVD method is

implemented called DWT-SVD which gives satisfactory

results for the attacks and the payload capacity as

discussed in [2] [5]

3.1 Watermark Embedding Algorithm1. 4 level DWT is applied on the host image which

generates 12 bands of frequencies.

2. SVD based embedding is applied for the V andH bands of the 4

th

level of the image as explainedin the section 4.3 using suitable gain factor.

3. SVD embedded V and H bands are used toreconstruct back the watermarked image by

IDWT.

Flow Chart

3.2 Watermark Extraction Algorithm

1. 4 level DWT is applied on the host image whichgenerates 12 bands of frequencies.

2. SVD based extraction is applied for the V and Hbands of the 4th level of the image as explained

in the section 4.4 using suitable gain factor.

3. SVD embedded V and H bands are used toreconstruct back the watermarked image by

IDWT.


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IV Experimental Results

The host image we used is lena which is shown in fig. 3,

because it has all the characteristics of the image. The

watermark image is as shown in fig. 4.

Fig.3 Lena image size 512x512

Fig.4 Watermark wm

Cascade DWT-SVD Based Method

In a cascade DWT-SVD method the watermark can be

extracted. Fig 5 shows the watermarked image with the

gain factor 0.85, and fig 6 shows the extracted watermark

for gain = 0.4.

Fig 5 watermarked

Fig 6

Table 1 shows the correlation coefficient and the PSNR

values with their respective gains

Gain PSNR

(dB)

Correlation Coefficient

0 353.4 1

0.1 105.88 1

0.4 88.31 0.9984

1.0 76.46 0.985

2.0 68.78 0.9271

3 64.616 0.8499

4 61.76 0.7741

5 59.595 0.708

Table 1 Values of PSNR and Correlation coefficient for

different values of gain

The graph below shows the PSNR versus gain and

correlation coefficient versus gain in fig 7 and fig 8

respectively for the watermarked lena with respect to the

original image.

Fig 7 gain v/s PSNR

Fig 8 gain v/s correlation


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In DWT-SVD based approach watermark is embedded

only in the 4th level V and H sub bands. Correlation

coefficient of 0.8 is for the value of gain factor 3.7.This

infers that the watermarking approach has good payload

capacity. Image perceptibility is also satisfactory as on

watermarking the original image doesnt loose its

originality for high value of gain. The pixel are readily

varied the gain value exceeds 1.5 above 2. The original

image looses its perceptibility

Attacks on Watermarked Images

The Lena watermarked image with the gain factor of 1 is

used to experiment the extraction of watermark for

different types of the attacks

1. Gaussian Noise

The different values of mean and variance are used and

the watermark is extracted .fig 9 shows the watermarked

image attacked by the Gaussian noise and its respective

extracted watermark is shown in fig 10.

Fig 9 mean=0.2, variance=0.3

Fig 10 Extracted watermark

The watermark is extracted for the value of mean and

variance below 0.3 and variance below 0.5. Hence the

watermarking approach is robust to addition of Gaussiannoise.

2. Blurring Noise

Blurring noise is applied on the watermarked image for

the variable values of the LEN and THETA the following

fig 11 shows the Blurring noise added image and the fig

12 shows extracted image

For many values of THETA when length is kept constant

to one the extracted watermark has a constant correlation

of 0.998 and PSNR of 91.26dB and on increasing the

values of LEN and THETA above 10 and 15 respectively

.The watermarked image looses its original perceptibility

and the extracted watermark has only the correlation of

0.57 Hence the watermarking method is robust to the

wide range of blurring parameters.

Fig. 11 blurring noise added LEN=10 THETA=12

Fig. 12 extracted watermark

3. Rotational Noise

The watermark is extracted for the various values of

rotational angels. Figure 13 shows some of the

watermarked images and the respective extracted

watermarks with the correlation coefficient and PSNRwith respect to the original image in fig 14.

Fig. 13 rotated to 10 degrees



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As shown in the above figures the watermark can be

extracted for the degrees of rotation and the correlation

with the original watermark is satisfactory. The extracted

watermark gives has highest correlation for 0,180,360

degrees of rotation and almost reduces for 270, 120, 90

degrees with the correlation coefficient around 0.445.

4. Resize Noise

Resized watermarked image is resized to the original

image size so that watermark can be extracted. The

watermarked image was resized to the various

percentages as illustrated in the below figures.

Fig. 15 resized to 25%


DWT-SVD based watermarking is robust to the resizing

for various values of resizing the extracted watermark can

be easily correlated with the correlation coefficient notbelow 0.98 one example is shown in fig 15 and its

extracted watermark in fig 16 because the noise hardly

affects the V band where watermark is added.

V CONCLUSION AND FUTURE WORK

Conclusion

SVD method requires both the watermark and the host

image or the original image to extract the watermark. This

may not be possible in practical applications. DWT-DCT

method requires only host image to detect the watermark

from the watermarked image and watermark to separate

the original image from watermarked image, this provides

authentication, Digital signature, Security which may be

applicable in various fields where the latter factors are

necessary. The SVD and DWT-DCT watermarking

methods did assure satisfactory results for robustness to

the wide ranges of attacks and also the payload capacity

so the effort is made to improve the robustness by

employing DWT-SVD cascade of spatial and transform

domain, which assured confidential results comparative of

other discussed watermarking methods. The above

discussed watermarking method can be employed in the

fields such as Fingerprinting, data hiding, copyright

protection, Copyright, Data Authentication, Medical

Safety etc.

Future Work

1. The image watermarking of colored images canbe developed by separating red green and blue

primary colors independently and reconstructingback to get the watermarked color image.

2. The watermarking was proposed for the grayscale images which can be further implemented

for different multimedia data such as video and

audio. This is in the immediate demand in the

market for security and copyright applications.

3. Advanced and faster watermarking methods forlive audio and video streams can be implemented

where the speed of processing the immediate

data is an issue.

REFERENCES

[1] Sin-Joo Lee; Sung-Hwan Jung

A survey of watermarking techniques applied to

multimedia Industrial Electronics, 2001. Proceedings,

ISIE 2001. IEEE International Symposium on 2001,

Page(s): 272 - 277 vol.1

[2] Saeed K. Amirgholipour, Ahmad R. Naghsh-NilchiRobust Digital Image Watermarking Based on Joint

DWT-DCT International Journal of Digital Content

Technology and its Applications Volume 3, Number 2,

June 2009

[3] Navneet Kumar Mandhani Watermarking UsingDecimal Sequences: A thesis.

[4] Vallabha VH Multiresolution Watermark Based on

Wavelet Transform for Digital images

[5] R. A. Ghazy , N. A. El-Fishawy, M. M. Hadhoud, M.

I. Dessouky and F. E. Abd El-Samie An Efficient Block-

By-Block SVD-Based Image Watermarking Scheme

Ubiquitous Computing and Communication Journal.

[6] Er-Hsien Fu Literature Survey on Digital Image

Watermarking EE381K-Multidimensional SignalProcessing 8/19/98

[7] Yuval Cassuto, Michael Lustig and Shay Mizrachy

Real-Time Digital Watermarking for Audio Signals

Using Perceptual Masking

[8] Santi P. Maity, Malay K Kundu and Tirtha S. Das,

Robust SS watermarking with improved capacity,

Pattern Recognition Letters, Volume 28, Issue 3, February

2007, pp 350-356.

[9]http://www.aquaphoenix.com/lecture/matlab10/index.h

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http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7417http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7417http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7417http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7417

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