Implementation of digital image watermarking techniques using dwt and dwt svd method

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 05 Issue: 08 | Aug-2016, Available @ http://ijret.esatjournals.org 50
IMPLEMENTATION OF DIGITAL IMAGE WATERMARKING
TECHNIQUES USING DWT AND DWT_SVD METHOD
Smita Patil1, P. V. Baviskar2
1
Department of Electronics & Telecommunication, SSVPS’s, B.S.D., College of Engineering, Dhule-424005,
Maharashtra, India
2
Department of Electronics & Telecommunication, SSVPS’s, B.S.D., College of Engineering, Dhule-424005,
Maharashtra, India
Abstract
These days, in every field there is gigantic utilization of computerized substance. Data took care of on web and mixed media
system framework is in advanced structure. Computerized watermarking is only the innovation in which there is inserting of
different data in advanced substance, which we need to shield from illicit replicating. Computerized picture watermarking is
concealing data in any structure (content, picture, sound and video) in unique picture without corrupting its perceptual qual ity.
On the off chance that of Discrete Wavelet Transform (DWT), deterioration of the first picture is completed to insert the
watermark. Moreover, if there should arise an occurrence of cross breed system (DWT-SVD) firstly picture is decayed by and
after that watermark is installed in solitary qualities acquired by application of Singular Value Decomposition (SVD). DWT and
SVD are utilized in combination to enhance the nature of watermarking. We have the procedures which are looked at on the
premise of Peak Signal to Noise Ratio (PSNR) esteem at various benefits of scaling component; high estimation of PSNR is
coveted because it displays great intangibility of the strategy.
Keywords: PSNR, MSE and Hybrid DWT-SVD
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1. INTRODUCTION
Since last couple of years, there is a noteworthy issue
around unapproved and illicit get to and control of media
records over web. Almost everyone can get copies of
copyrighted sight and sound straight forwardly. So we need
to make a lively system remembering the deciding objective
to secure the copy benefits of media. Automated
watermarking gives copyright security of data. The
Watermarking is a methodology wherein the main picture
generally called spread picture is changed by watermark
picture. Certain properties of the spread picture are changed
remembering the finished objective to disguise the data used
for the recognizing verification of the proprietor of the
principal substance [5].
Generally, watermarking uses two types of methods.
Starting one is spatial space system another is change zone
technique. In case of spatial space, the puzzle messages are
generally embedded in the pixels of photo. The most
generally perceived systems are histogram-based and LSB
techniques in the spatial space. Watermarking frameworks
which are based on spatial range, are sometimes supported
over change space based watermarking systems in light of
the way that the watermark set by them could be simply
decimated and improved by the aggressors. However in the
change space the watermark is embedded by altering the
significance of coefficients in a change range with the
assistance of discrete cosine change, discrete wavelet
transform (DWT), discrete Fourier change and specific
quality disintegration (SVD) methods.
1.1 General Watermarking Methods
In digital watermark, the information is generally embedded
in a single, sometimes, the difference between the signal of
watermarked and the signal of cover also refers as digital
watermark. In the host signal, watermark has to be
embedded. Embedding, attack, and detection are three
important distinct steps in watermarking. In case of
embedding, an algorithm accepts the host and subsequently
it embeds the data, which ultimately resulted into the
watermarked signal. In the next step, the watermarked
digital signal proceeds to transmission and finally it gets
stored, usually it gets transmitted to another person. If the
modifications happen at this persons end, this is termedas
attack. Though these modifications are not malicious, the
term attack ascends from application of copyright
protection, where third parties may challenge to eliminate
the digital watermark by doing some modifications. Many
potential modifications could happen, for example, lossy
compression of the data (diminishing of resolution), cutting
an image or video or purposefully additional of some noise.
Detection (i.e. extraction) is an examples of algorithm
generally used to attacked the signal to identify and remove
the watermark from it. If the signal remains unmodified
during the process of transmission, in this case watermark
could be extracted. The digital watermarking applications

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are robust in nature and the process of algorithm extraction
has to yield the watermark correctly, under strong
modifications scenario. If changes made to the signal, then
the fragile digital watermarking is unsuccessful.
2. LITERATURE SURVEY
Various studies have proposed various procedures of
composing For example, Hui-Yu Huang [1] proposed
system lossless data disguising procedure for a DWT. Our
proposed strategy uses quantization segments for DWT,
which could offer higher covering point of confinement
which could protect the photo way of stego-pictures. In
order to recover the principal picture losslessly, the riddle
data has to be removed from stego-images. Cui-ling JIANG
et al., [2] have offered the spread picture which is detached
into non-covering squares of 16×16 pixels instead of
standard parceling spread picture into 8×8 pieces and the
DCT is used to change each square. The beauty of the DCT
coefficients is, they are quantized and subsequently
embedded the messages secretly. However, this strategy has
many Steganography limitations which favored stego-
picture quality over interchange procedures. The lossless
data covering methodology for a DWT-based technique has
recently been proposed by Hui-Yu Huang et al., [5].In this
paper we proposed our strategy which uses quantization
segments for DWT, this approach could offer higher
covering utmost and recovery the photo way of stego-
pictures. By using our approach, the primary picture could
be well again losslessly even when the puzzle data had been
barred from stego-pictures. Satyanarayana Murty et al. [10]
suggested procedure (DWT DCT-SVD) is exceptionally
solid and can restrict various picture planning ambushes.
The way of the watermarked picture is awesome to the
extent recognizable quality and PSNR (42db). The proposed
count is given off an impression of being lively to regardless
of the ambushes said before from JPEG 2000. The singular
qualities in every quadrant are later balanced by the specific
estimations of the DWT-DCT changed visual watermark.
We have demonstrated that by introducing the data at
minimum frequencies is impenetrable to most of the strikes
and ambushes at some extent are impenetrable to other
repeat bunches. Moreover, Vishal Verma et al.., [11] have
also successfully demonstrated an unequivocal survey of
existing picture watermarking methodologies. In this paper,
we describe the frameworks in perspective of different
territories in which data is embedded. We have also
examined particular sort of frameworks of introducing
watermark and as indicated by come to fruition showed our
proposed system in light of SVD DWT cream technique
which have higher PSNR for extracted watermark picture.
In order to further improve the watermarking in cutting edge
picture, watermark could be embedded by using new lifted
wavelets and also stationary wavelets, S-change etc. This
approach would certainly further improve the watermarking
technique.
3. PROPOSED SYSTEM
We have proposed the watermarking technique which is
shown below. This method aims to embed a secret message
or log into DWT coefficient at medium to high frequency
parts and which subsequently bring back the original image
coefficient when the secret message gets retrieved. The
wavelet transform is very useful for conversion of a picture
from spatial domain to frequency domain. In this paper we
proposed the pairing of decomposition of digital picture to
waveform with high frequency which generally corresponds
to thorough parts of an image and low frequency
corresponds to smooth parts of the image. The message
which is digitally marked will be patched in high frequency
parts and the image has to be rebuilt for cover image which
will have concealed digital marked message. In this
methodology, the embedded image gets decayed into
inverse discrete wavelet transform (IDWT). The IDWT is
generally very useful to transform fromfrequency domain to
spatial domain. Therefore it’s called as frequency-time
representation. By using DWT method, patched image will
get retrieved in to sub-band frequencies. We propose to take
the digital marked data from the medium to high frequency
parts and the retrieved digital marked data is going to
perform matching through original message which includes
the process of embedding and extraction:
3.1 Algorithm
3.1.1 DWT-Embedding Algorithm
1. Read & resize the cover image (512×512)
2. Read & resize the watermark logo (512×512)
3. Decomposed cover image to first stage decomposition.
4. Application of dwt method on message image.
5. Entering of scale factor.
6. Modification ofhh sub band and its component.
(hh_mod = hh + f * wl_hh)
7. Application ofidwt method with altered hh sub band.
8. Obtaining of watermarked image.
9. Addition of noise on watermarked image.
3.1.2 Extraction of Algorithm of DWT
1. Application of dwt method on watermarked image.
2. Identify the difference use below equation.
hh_Ext = (hh_wl - hh)/f
3. Application of idwt-2 using retrieved hh sub band
component.
4. Obtaining the retrieved message and original image.
5. Calculation of PSNR, NC and MSE.
3.1.3 DWT_SVD: Embedding Algorithm
1.Read and resize the cover image (512x512)
2. Read and resize the watermark logo (256x256)
3. Application of haar wavelet method and decompose cover
image into further 4 sub bands.
4. Application of SVD method on hh sub band: (U_hh,
S_hh, V_hh
5. Application of SVD method on watermark logo.
6. Entering scale-factor (f)
7. Modification of S component, from svd method, of hh
band
8. S_hh_mod = S_hh + f * S_wm;
9. Application ISVD method on modified hh part.

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10. Application idwt method with modified hh band for
obtaining watermarked image.
11. Addition of noise onto watermarked image.
3.1.4 Extracting Process Algorithm of DWT_SVD
1. Application of haar wavelet method to watermarked
image.
2. Application of SVD method to HH band.
3. D = (S_wmI - S_hh)/f.
4. Extraction of S parts of watermark logo.
5. Application inverse-SVD method.
6. Obtaining watermark logo.
7. Calculation of PSNR, MSE & NC
The steps of evaluation and comparison of the performance
of DWT and DWT-SVD are given by
Where,
MSE is mean square error,
M x N is the dimension of images,
I (i,j) is original image dimension,
I’ (i,j) is the dimension of watermarked image
By using the above result of MSE PSNR can be calculated
using the formula,
For evaluation of similarities between original cover image
and obtained watermarks the Normalize correlation
coefficient are given by,
3.2 Mathematical Model
S{I,CI,D,EM,ID,EI,DE,EHH,OM}
I= I is set of input image.
I= {I1, I2….In}
CI=CI is set of cover image.
CI= {CI1, CI2….CIn}
D=D is set of DWT method.
D= {D1, D2,…..Dn}
EM= EM is set of embedding message.
EM= {EM1, EM2…EMn}
ID=ID is set of IDWT method
ID= {ID1, ID2….IDn}
EI= EI is set of embedding image.
EI= {EI1, EI2….EIn}
DE= DE is set DWT for extraction
DE= {DE1, DE2…..DEn}
EHH=EHH is set of extraction of HH frequency.
EHH= {EHH1, EHH2….EHHn}
OM=OM is set of output images.
OM= {OM1, OM2…OMn}
3.3 Process
1) Take input image.
I= {I1, I2….In}
2) Take cover and message image
CI= {CI1, CI2….CIn}
3) Apply DWT method.
D= {D1, D2,…..Dn}
4) Embedding message image into cover image.
EM= {EM1, EM2…EMn}
5) Apply IDWT.
ID= {ID1, ID2….IDn}
6) Take Embedding image for extraction.
EI= {EI1, EI2….EIn}
7) Apply DWT for Extraction.
DE= {DE1, DE2…..DEn}
8) Extract HH frequency component.
EHH= {EHH1, EHH2….EHHn}
9) Original image.
OM= {OM1, OM2…OMn}
Result: We get original image.
3.4 System Architecture
Fig1 (a) Embeding process
Fig1 (b) Extracting process

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4. RESULT
(a) Original Image (b) Watermarked Logo
(a) Watermarked Image (b) Extracted Logo
Fig 3 Matlab Simulation result for single level DWT with
Gauassian attack
The PSNR value of extracted logo in DWT _SVD after
extraction is observed to be 87.09 and 91.26, respectively.
These values indicate the accuracy of, DWT-SVD over
DWT technique (Table 1). The scale factors were added for
safety reasons for purses. We offered the method, in which
the values of the scale factors are observed to be in
continuous range from 0.01 to 0.07 (Table 1). Our results
clearly indicated that, when the scale factor was larger, the
robustness was managed efficiently. In contrast, quality of
extracted logo was observed to be good when the scale
factor was smaller. We also evaluated the robustness of the
new watermarking scheme with several attacks which
includes addition of Speckle noise, salt & pepper noise,
Gaussian low pass filter and even Local variance noise.
Table 2 represents, the MSE and PSNR of distorted
watermarked images under above distortions. The PSNR
was observed to be less in comparison with hybrid technique
(DWT-SVD), when the watermarked image using DWT
technique was attacked.
Table 1: Comparison between PSNR (db) of others &
proposed method
Scaling
factor
values
PSNR
(db) for
others
method
DWT
PSNR (db)
values for
DWT_SVD
(other
method)
PSNR (db)
values for
DWT
(Proposed
Method)
after
extraction
PSNR (db)
values for
DWT-SVD
(Proposed
method)
after
extraction
0.01 43.63 76.2096 79.02 84.53
0.02 37.58 60.8586 78.56 85.78
0.03 34.08 52.6066 87.09 91.26
0.04 31.57 49.4157 78.52 81.47
0.05 29.64 45.9157 79.02 80.21
0.06 28.04 43.1116 78.62 80.72
0.07 26.71 40.8654 79.02 81.46
Table2. Comparison of PSNR & MSE of watermarked
image against different attack byour proposed method
Cover
image
Attack
Control
parameters
DWT DWT_SVD
CARS
Gaussian PSNR(db) 49.4867 80.5406
MSE 0.73184 0.00057414
Local variance PSNR(db) 48.2597 80.5463
MSE 0.38647 0.00057336
Speckle PSNR(db) 49.7416 79.8675
MSE 0.69011 0.0006704
Pepper & salt PSNR(db) 60.96667 79.3662
MSE 0.05201 0.00075242
Couple
Gaussian PSNR(db) 49.4419 80.721
MSE 0.73942 0.000550
Local variance PSNR(db) 52.2286 80.1298
MSE 0.3892 0.0005013
Speckle PSNR(db) 45.702 79.6314
MSE 1.7494 0.0007078
Pepper & salt PSNR(db) 53.2131 79.5002
MSE 0.3103 0.0007295
Table 3: Using DWT-SVD method, the PSNR (db), MSE &
NC of watermarked noisy image on different logo.
Gaussian
Noisy
Image
Extracted
Logo
PSNR MSE NC
66.1926 0.015625 0.99194
68.1216 0.010021 0.96557
61.3922 0.047192 0.95613
The results obtained by simulation are presented in Table 3.
The different images of Gaussian noisy, by using hybrid
technique we embedded different logos and the subsequent
simulation result are tabulated in table-3. The combination
of DWT and SVD are used for improvement of quality of
the watermarking fusion which resulted into the very
striking technique.
5. CONCLUSION
On looking at the estimations of PSNR at various benefits of
mounting component C, it’s presumed that the cross breed
system DWT-SVD is vastly improved as compared to DWT
procedure. We have tried DWT-SVD half breed strategy,
the scale component diminishes as the PSNR increments.
Lower the estimation of PSNR, higher would be the
corruption in the nature at the first picture. Half breed
Watermarked noisy image
Extracted watermark
Extracted watermark
Extracted watermark

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method utilized in combination to enhance the nature of
watermarking. The strategy proposed in this paper where
the distinction from customary plan is that the insertion of
watermarking in high recurrence segments which gives
prodigious execution in an assortment of picture application.
ACKNOWLEDGEMENT
The authors would like to thank the Principal, Head of
Department (Department of Electronics and
Telecommunication), SSVPS, B. S. D., College of
Engineering, Dhule (North Maharashtra University) for
providing necessary facilities for carrying out the work.
Authors also thankful to the anonymous reviewer for their
valuable comments and further improvement of the
manuscript.
REFERENCES
[1]. Hui-Yu Huang and Shih-Hsu Chang “A lossless data
hiding based on discrete Haar wavelet transform”,10th IEEE
International Conference on Computer and Information
Technology, 2010.
[2]. Cui-ling JIANG “A Steganographic Method based on
the JPEG Digital images” Institute of Information, East
China University of Science and Technology, 2011.
[3]. Anjali A. Shejuland U.L Kulkarni “A DWT based
Approach for Steganography Using Biometrics”,
International Conference on Data Storage and Data
Engineering, 2010.
[4]. Souvik Bhattacharyya and Gautam Sanyal “Data Hiding
in Images in Discrete Wavelet Domain Using PMM”, World
Academy of Science, Engineering and Technology, 2010.
[5]. Mohammad Reza Soheili “A Robust Digital image
Watermarking Scheme Based on DWT” Journal of
Advances in Computer Research, m2(2010) 75-82.1.
[6]. Qing zhong Li, Chen Yu and Dongsheng Chu “A
Robust Image Hiding Method Based on Sign Embedding
and Fuzzy Classification”, Proceedings of the 6th World
Congress on Intelligent Control and Automation, 2006.
[7]. Po-Yueh Chen and Hung-Ju Lin, “A DWT Based
Approach for Image Steganography”, IEEE International
Journal of Applied Science and Engineering, 2006.
[8]. Guorong Xuan, Yun Q. Shi and Chengyun Yang
“Lossless Data Hiding Using Integer Wavelet Transform
and Threshold Embedding Technique” 0-7803-9332-
5/05/$20.00 ©2005 IEEE.
[9]. Y. K. Lee and L.-H. Chen, “High capacity image
steganographic model”, Vision, Image and Signal
Processing, IEEE Proceedings, 2000.
[10]. Satyanarayana Murty and P. Rajesh Kumar, “A Robust
Digital Image Watermarking Scheme Using Hybrid DWT-
DCT-SVD Technique” IJCSNS International Journal of
Computer Science and Network Security, VOL.10 No.10,
October 2010.
[11]. Vishal Verma and Jyotsna Singh “Digital Image
Watermarking Techniques: A Comparative Study”
International Journal of Advances in Electrical and
Electronics Engineering ISSN: 2319-1112 /V2N1:173-
184,2013.
[12]. Nidhi Bisla and Prachi Chaudhary “Comparative Study
of DWT and DWT-SVD Image Watermarking Techniques”
International Journal of Advanced Research in Computer
Science and Software Engineering Volume 3, Issue6, June
2013 ISSN: 2277 128X
BIOGRAPHIES
Ms. Smita Patil has completed her B.E. (Electronics and
Telecommunication) from the Pune University, Pune. She
has been pursuing M.E. (Communication Systems) from
SSVPS’s, B.S.D., College of Engineering, Dhule (North
Maharashtra University, Jalgaon)
Associate Prof. P. V. Baviskar has more than 26 years of
experience in the field of Electronics. He has published
paper on “Nanoscale SOI MOSFET structures and study of
performance factors”, IJCA.

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