IRJET-Security Based Data Transfer and Privacy Storage through Watermark Detection

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 02 Issue: 01 | Apr-2015 www.irjet.net p-ISSN: 2395-0072
© 2015, IRJET.NET- All Rights Reserved Page 377
Security Based Data Transfer and Privacy Storage through Watermark
Detection
Gowtham.T1 Pradeep Kumar.G2
1PG Scholar, Applied Electronics, Nandha Engineering College, Anna University, Erode, India.
2Assistant Professor, Department of ECE, Nandha Engineering College, Anna University, Erode, India.
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Abstract—Digital watermarking has been proposed
as a technology to ensure copyright protection by
embedding an imperceptible, yet detectable signal in
visual multimedia content such as images or video. In
every field key aspect is the security—Privacy is a
critical issue when the data owners outsource data
storage or processing to a third party computing
service. Several attempts has been made for increasing
the security related works and avoidance of data loss.
Existing system had attain its solution up to its level
where it can be further able to attain the parameter
refinement. In this paper improvising factor been made
on the successive compressive sensing reconstruction
part and Peak Signal-to-Noise Ratio (PSNR).Another
consideration factor is to increase (CS) rate through de-
emphasize the effect of predictive variables that
become uncorrelated with the measurement data which
eliminates the need of (CS) reconstruction.
Keywords— Compressive sensing, Peak Signal To Noise
Ratio, secure computing service, privacy preserving.
1. INTRODUCTION
Rapid growth of the Internet and social networks, made
very easy for a user to collect a large amount of
multimedia data from different sources without knowing
the copyright information of those data. Data theft is the
major issue for data owners when their data been
outsourced in the public or private network. It increased
in the field of photography and defensive system. For this
as a greater result embedding process is carried out.
Embedding a hidden stream of bits in a file is called Digital
Watermarking. The file could be an image, audio, video or
text.
The specifications used for validating watermarking
system are: Robustness (Against intentional attacks or
unintentional ones such as compression), Imperceptibility,
Noise ratio and Capacity[3,6,10]. An effective digital
watermark should be perceptually invisible to prevent
obstruction of the original image.Digital watermark should
be statistically invisible to prevent detection from the
illegal users and it should also be robust to many image
manipulations, such as filtering,additive noise, and
compression.
Techniques have been proposed for a variety of
applications, including ownership protection,
authentication and access control. The cloud computing
technologies are growing, and it is more economical for
the data holders to shift data storage or signal processing
computations to the cloud instead of purchasing hardware
and software by themselves. Ideally the cloud will store
the data and perform signal processing or data-mining in
an encrypted domain in order to preserve the data
privacy[2,5]. Majorly two types of approaches are
determined for secure watermark detection: asymmetric
watermarking [2] and zero-knowledge watermark
detection [5,7,11].
Watermarked copy are publicly available for the usage
were the focus to be done only on the watermark pattern,
while the privacy of the target media on which watermark
detection is performed has received little attention.
Performing privacy preserving storage and secure
watermark detection can be done using the existing secure
watermark detection technologies such as zero-knowledge
proof protocols [5,7] that transform the multimedia data
to a public key encryption domain.
2. RELATED WORKS
[1] In this paper, author represents various types of
attacks in watermarking and solutions for qualifying the
watermarking method are described.
Pros and cons:
In this paper implementation of basic digital
watermarking methods in MATLAB , Fundamental
methods in spatial, spectral, and hybrid domains are
described. It also deals with various attack in
watermarking but its just a initial stage.
[2] In this paper author explains about data privacy in the
networks through Secure Multi-Party Computation (SMC)
allows parties with similar background to compute results
upon their private data, minimizing the threat of
disclosure.
Pros and cons:
This paper introduces encryption and decryption in
embedding watermark through the key access. Quite a few
protocols already exist where it has its way on TTP for
several layers network in order to ensure privacy.

[3] In this paper, author presents a general framework for
robust nonlinear regression that leverages concepts from
the ﬁeld of compressive sensing to simultaneously detect
outliers and determine optimally sparse representations
of noisy data from arbitrary sets of basis functions.
Pros and cons:
In this paper, author replaces usage of Least Square
(LS)regression which is not robust to violations. More
techniques were introduced robust compressive sensing
but all of the techniques does not have residual reduction
except few of them.
[4]In this paper author demonstrated that it is possible to
substantially decrease noise measurements(M) without
sacriﬁcing robustness by leveraging more realistic signal
models that go beyond simple sparsity and compressibility
by including dependencies between values and locations
of the signal coefficients.
Pros and cons:
In this paper author have only considered the recovery
of signals from models that can be geometrically described
as a union of subspaces; and not for more complex
geometries (for example, high-dimensional polytopes,
nonlinear manifolds.)
[5] In this paper, author presented watermarking scheme
using Genetic Algorithm(GA).Genetic algorithms are a part
of evolutionary computing, which is a rapidly growing
area of artificial intelligence.Pros and cons:
In this paper GA has been applied to clusters of the
image instead of the complete image so the processing
speed is higher.This paper has poor response for data
hiding capacity, using Multiple Optimization GA.
3. PROCEDURAL FLOW OF THE SYSTEM
The proposed framework has several subtasks where
each has its specific operation, they are given below
 Watermark insertion and generation
 Embedding Process
 Watermark Extraction
 Decoding Process
3.1 Watermark insertion and generation
In the initial stage select the file which been going to
watermarked and also the data that to be embedded in it.
Later DWT of an image and the watermark pattern to be
calculated which resembles like picture matrix.
Watermark insertion involves watermark generation
and encoding process. Watermark Generation: Each owner
has a unique watermark or an owner can also put different
watermarks in different objects, the marking algorithm
incorporates the watermark into the object. The
verification algorithm authenticates the object
determining both the owner and the integrity of the object.
The watermark can be a logo picture, sometimes a binary
picture, sometimes a ternary picture; it can be a bit stream
or also an encrypted bit stream etc. The encryption may be
in the form of a hash function or encryption using a secret
key.
The watermark generation process varies with the
owner. In the encoding process both the original data and
the watermark data are passed through the encoding
function. The payload signal and the original host signal
now together occupy space, which was previously
occupied only by the host signal. For this purpose either
the original data is compressed or redundancy in digital
content is explored to make space for the payload.
3.2Embedding Process
It provides the action of mixer where it has various
algorithms for embedding process. These are commonly
divided into three categories (1)
 Watermarking in Spatial Domain
 Watermarking in Spectral Domain
 Watermarking in Hybrid Domain
There are several transforms that brings an image into
frequency domain. Among most common of those, we can
mention are: Discrete Cosines Transform (DCT) and Fast
Fourier Transform (FFT). In frequency domain,
coefficients are slightly modified. This will make some
unnoticeable changes in the whole image and makes it
more robust to attack compared to what we have in spatial
methods. Coefficients are modified according to the
stream bits of the message using to the equation
CAW = CA(1 +α⋅ (Wi) )
In which CAW is the watermarked coefficient, CA is the
original one, α represents watermarking strength (e.g.
0.3), and Wi is the corresponding bit of the message data.
Embedding can be done to obtain higher PSNR values
(higher fidelity) and higher NCC values (better robustness
to attacks)[10].
3.3 Watermark Extraction
Extraction is achieved in twosteps[1]. First the watermark
is extracted in the decoding process and then the
authenticity is established in the comparing process. After
the embedding process through key providence now the
extraction of the image takes place. Inverse action of
image scan been done and obtain IDCT of an image. The
decoding process can be itself performed in two different
ways. In one process the presence of the original
unwatermarked data is required and other where blind
decoding is possible. A decoder function takes the test
data (the test data can be a watermarked or un-
watermarked and possibly corrupted) whose ownership is
to be determined and recovers the payload.
3.4Data Admin(Holder)
DH (e.g., media agencies), when it collects a large volume
of multimedia data from the Internet and stores their
encrypted versions in the CLD, it wants to make sure those
multimedia can be edited and republished legally.

3.5 Watermark Owner Module
Watermark owners (WOs) are also the content providers
who distribute their watermarked content (the watermark
embedding is performed by WO before the contents are
published). WOs always want to know if their contents are
legally used and republished.
4 Compressive Sensing
The compressive sensing theory asserts that when a signal
can be represented by small number of nonzero
coefficients, it can be perfectly recovered after being
transformed by a limited number of incoherent, non-
adaptive linear measurements. Most of the literature of
compressive sensing has focused on improving the speed
and accuracy of compressive sensing reconstruction take
some initial steps towards a more general framework
called compressive signal processing (CSP), which shows
fundamental signal processing problems such as detection,
classification, estimation, and filtering can be solved in the
compressive sensing domain.
Figure.1Architecture of the proposed framework
DCT of the image been obtained using the formula
FDCT





 





 
 



 2N
1)l(2n
cos
2N
1)k(2m
cosn)u(m,(l)(k)l)v(k,
1N
0m
1N
0n


where k, l  0, 1, ... N-1.
IDCT
u(m,n) (k) (l) v(k,l) cos
(2m 1)k
2N
cos
(2n 1)l
2Nl=0
N 1
k=0
N 1
   













  
 
where m, n  0, ... N-1
Figure.2(a)Original image; (b)Image in 8×8 DCT
domain; (c)DCT coefficient after CS transformation;
(d)Image reconstruction with the wrong CS matrix. (CS
rate 1.0 is chosen here, similar effects are observed
under other CS rates).
In existing system DCT coefficient of each piece of the
image will form a vector and be transformed to a CS
domain with the same CS rate but using different CS
matrices[11].
For privacy preserving storage, since the DCT
coefficients are not perfectly sparse, the CS
reconstruction will introduce distortion to there
constructed image, especially when CS rate is low. In
order to have a good quality image after the CS
reconstruction, the CS rate needs to be high. In the
existing system experimental result shows that the
PSNR(Peak Signal-to-Noise Ratio) is around 65 after
the CS transformation/reconstruction process when
the CS rate is 0.8. Even when the CS rate is set to 1.0,
the CS reconstruction algorithm (Orthogonal Matching
Pursuit) still introduces distortion as we can see the
PSNR is around 45. However, it should be noted that
when the CS rate equals1.0,theoriginal DCT coefficient
can be recovered perfectly given the inverse of the CS
matrix, in which case CS reconstruction is not
necessary.
Figure.3CS reconstruction distortion when AC
coefﬁcients are transformed to the CS domain.

Figure. 4Calculation of PSNR and MSE value through
simulation
5.EXPERIMENTAL RESULTS
PARAMETERS VALUES
Protocol
Multi Party Computation(MPC)
Algorithm DWT
Image Size 1024 x 1024
Character Length Upto 100
Key Public or Private
TABLE 1:Simulation Parameters
In proposing system instead of using DCT we would like to
develop system with the usage of DWT for the image
coefficients were it been segregated into various image
blocks.
Figure. 5Energy segments of the processed image
Figure. 6a).Original image b).Secret image c).Gray scale of
original image d.)Bit plane slicing of secret image
In the above image work been carried out with DWT
process through spatial domain and frequency domain.
Fig(6.b)shows the gray scale of the original image in that
color of the image been transformed to gray value and
Fig(6.d)shows the bit plane slicing of secret image.(In this
figure shown is 6-bit plane slicing).
Figure. 7a).DWT of original image(3 bit plane) b).DWT of secret
image(3 bit plane) c).Watermarked image d).Extracted
watermark image
6.CONCLUSION
The main aim of the project is to improve the security of
the data where user prefers to obtain it. Further analysis
being carried out to increase CS rate through DWT
process. Increase in CS rate reduces the reconstruction of
the image where image would be recovered perfectly with
the image coefficients.
In this paper considering existing system parameters as
the guideline, work been takes place for improving the
HH
(High
High)
LH
(Low
High)
HL
( High
Low)
LL
(Low
Low)

performance of the system with the available methods and
algorithms.
ACKNOWLEDGEMENT
The authors would like to thank the anonymous reviewers
for their constructive comments that greatly improved the
quality of this paper.
REFERENCES
1. “Digital Watermarking Using MATLAB” ,Pooya
Monshizadeh Naini University of Tehran, Iran,2009.
2. “A Secure Multi-Party Computation Protocol for
Malicious Computation Prevention for preserving
privacy during Data Mining”,Dr. Durgesh Kumar
Mishra,International Journal of Computer Science and
Information Security, Vol. 3, 2009
3. “A General Framework for Robust Compressive
Sensing Based Nonlinear Regression”,Brian Moore,
Manhattan, Kansas 66506, USA,2009.
4. “Model-Based Compressive Sensing”,Richard G.
Baraniuk,Rice university,2009.
5. “Secure Multiparty Computation and Secret Sharing
An Information Theoretic Approach”,Ronald
Cramer,May 11, 2013.
6. “Study and Implementation of Watermarking
Algorithms”,Alekhika Mohanty,Rourkela, India .April
2006.
7. “Watermark Detection Schemes with High Security”
,Liu Yongliang,Institute of Computing Technology,
China,(ITCC’05).
8. “Steganography And Digital Watermarking”, Jonathan
Cummins, The University of Birmingham,2004.
9. “Digital Watermark Detection in Visual Multimedia
Content”,Peter Meerwald, University of
Salzburg,2010.
10. ”Practical challenges for digital watermarking
applications”, Ravi.K.Sharma,USA,2002.
11. “A Compressive Sensing based Secure Watermark
Detection and Privacy Preserving Storage
Framework”, Qia Wang, Wenjun Zeng,ieee
transactions on image processing, vol. 23, no. 3,
march 2014.

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