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Data Hiding in a Binary Image
Enrol. No: 9910103417
Name of Student: Ankit Sharma
Name of Supervisor: Ms. Shraddha Porwal
December-2013
Submitted in partial fulfillment of the Degree of
Bachelors of Technology
In
Computer Science Engineering
DEPARTMENT OF COMPUTER SCIENCE ENGINEERING &
INFORMATION TECHNOLOGY
JAYPEE INSTITUTE OF INFORMATION TECHNOLOGY, NOIDA
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(I)
TABLE OF CONTENTS
Chapter No. Topics Page No.
Student Declaration III
Certificate from the Supervisor IV
Acknowledgement VSummary (Not more than 250 words) VI
List of Figures VII
List of Tables VIII
List of Symbols and Acronyms IX
Chapter-1 Introduction X-X11
1.1 General Introduction
1.2 List some relevant current/open problems.
1.3 Problem Statement
1.4 Overview of proposed solution approach and Novelty/benefits
Chapter-2 Background Study XIII-XVIII
2.1 Literature Survey
2.1.1 Summary of papers2.1.2 Integrated summary of the literature studied
2.1.3 Solution to the problem framed
2.2 Details of Empirical Study
Chapter 3: Analysis, Design and Modeling XIX-XXVII
3.1 Requirements Specifications
3.2 Functional and Non Functional requirements
3.3 Overall architecture with component description
3.4 Design Documentation
3.4.1Use Case diagrams
3.4.2 Class diagrams / Control Flow Diagrams
3.4.3 Sequence Diagram/Activity diagrams3.4.4 Data Structures and Algorithms / Protocols
3.5 Risk Analysis and Mitigation Plan
Chapter-4 Implementation and Testing XXVIII-XXXIV
4.1 Implementation details and issues
4.2 Testing
4.2.1 Testing Plan
4.2.2 Component decomposition and type of testing
4.2.3 List all test cases in prescribed format
4.2.4 Limitations of the solution
Chapter-5 Findings & Conclusion XXXV-XXXVI
5.1 Findings5.2 Conclusion
5.3 Future Work
References ACM Format (Listed alphabetically) XXVIII
Appendices XXVII
Brief Bio-data (Resume) of Student
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DECLARATION
I hereby declare that this submission is my own work and that, to the best of my knowledge
and belief, it contains no material previously published or written by another person nor
material which has been accepted for the award of any other degree or diploma of the
university or other institute of higher learning, except where due acknowledgment has been
made in the text.
Place: Delhi Signature:
Date: 18.11.2013 Name: Ankit Sharma
Enrollment No: 9910103417
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CERTIFICATE
This is to certify that the work titled Data Hiding in a Binary Imagesubmitted by Ankit
Sharma in partial fulfillment for the award of degree of B. Tech. from Jaypee Institute of
Information Technology University, Noida has been carried out under my supervision. Thiswork has not been submitted partially or wholly to any other University or Institute for the
award of this or any other degree or diploma.
Signature of Supervisor:
Name of Supervisor: Ms. Shraddha Porwal
Designation:
Date: 18/11/2013
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ACKNOWLEDGEMENT
My most humble and sincere thanks to:
My mentor, Ms. Shraddha Porwal for helping me and guiding me throughout the project
My teachers for guiding me and giving me new ideas on how to make this a successful
project. My parents for supporting me at every point.
Signature of the Student:
Name of Student: Ankit Sharma
Enrollment Number: 9910103417
Date:18/11/2013
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Summary
Data hiding represents a class of processes used to embed data into various forms of mediasuch as image, audio, or text with a minimum amount of perceivable degradation to the
host signal. Data hiding, while similar to compression, is distinct from encryption. Its goal
is not to restrict or regulate access to the host signal, but rather to ensure that embedded data
remain hidden and recoverable.
Data hiding has mainly two branches Steganography and Digital Watermarking.
Digital watermarking is mainly used to show proof of ownership or for multimedia
authenticity. While steganography is used for covert communication and for confidential data
storage.
I am going to deal with steganography. The main aim is to hide the existence of the message.
It is the art and science of embedding message to datas noise. Only the sender and receiver
know that the message even exits.
In my major project I have implemented the data hiding algorithm given by Chang et al,
Chung et al, Bergman and Davidson.
During my study I found out that no such code implementation of these algorithms is
available on internet, so any person thinking of creating something in data hiding field will
have to first implement these algorithms from scratch. Thats where I come in; I am going to
make my code freely available on internet. So that people can use it to further create new and
exciting applications/inventions.
The algorithms I am going to implement are all using SVD (singular value decomposition)
for data hiding. SVD increases capacity, perceptibility, robustness, speed and decreases
detectability. I will also be finding out which among the said algorithms is the best based on
PSNR value. Peak signal-to-noise ratio, is an engineering term for the ratio between the
maximum possible power of a signal and the power of corrupting noise that affects the
fidelity of its representation
__________________ __________________
Signature of Student Signature of Supervisor:
Name: Ankit Sharma Name: Ms. Shraddha Porwal
Date: 10/12/2013 Date:10/12/2013
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List of Figures
Types of Data Hiding Pg. 15
Architecture with component description Pg. 20
Use Case Pg. 21
Control Flow Pg. 22
Sequence Pg. 23
Tested Images Pg. 35
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List of Tables
Risk Analysis and Mitigation Plan Pg. 27
Testing Plan Pg.29-31
Test Schedule Pg.31
Test Environment Pg.32
Component Decomposition and type of testing required Pg. 3334
Project Plan Pg. 37
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List of Symbols & Acronyms
AlgorithmAlgo
Method1Bergmann Method
Method2- Chung et al
Method3- Chang et al
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Introduction
General Introduction
Data hiding, a form of steganography, embeds data into digital media for the purpose of
identification, annotation, and copyright. Several constraints affect this process: the quantity
of data to be hidden, the need for invariance of these data under conditions where a host
signal is subject to distortions, e.g., lossy compression, and the degree to which the data must
be immune to interception, modification , or removal by a third party.
Data hiding represents a class of processes used to embed data, such as copyright
information, into various forms of media such as image, audio, or text with a minimum
amount of perceivable degradation to the host signal; i.e., the embedded data should be
invisible and inaudible to a human observer. Note that data hiding, while similar to
compression, is distinct from encryption. Its goal is not to restrict or regulate access to the
host signal, but rather to ensure that embedded data remain inviolate and recoverable.
Two important uses of data hiding in digital media are to provide proof of the copyright, and
assurance of content integrity. Therefore, the data should stay hidden in a host signal, even if
that signal is subjected to manipulation as degrading as filtering, resampling, cropping, or
lossy data compression. Other applications of data hiding, such as the inclusion of
augmentation data, need not be invariant to detection or removal, since these data are there
for the benefit of both the author and the content consumer. Thus, the techniques used for
data hiding vary depending on the quantity of data being hidden and the required invariance
of those data to manipulation. Since no one method is capable of achieving all these goals, a
class of processes is needed to span the range of possible applications.
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Relevant Problems
Steganography for binary images: It is also more challenging to hide secret data in binary
images because there are only two alternatives to the color of a binary image. The
modifications done to the image due to the embedding of the secret data can be easily
observable by the human eye, which gives a strict limit to the hiding capacity of the binary
image in comparison with the grayscale image.
The following problems require particular attention:
Distortion: The distortion introduced must be imperceptibly small for commercial or artistic
reasons. However, an adversary intending to obliterate may be willing to tolerate certain
degree of visible artifacts. Therefore, the distortions by embedding and by attack are often
asymmetric, leading to a wide range of possible point-to-noise ratio.
Actual noise conditions: An embedding system is generally designed to survive certain
noise conditions. The embedded data may encounter a variety of legitimate processing
and malicious attacks, so the actual noise can vary significantly. Targeting conservatively at
surviving severe noise would lead to the waste of actual payload, while targeting aggressively
at light noise could result in the corruption of embedded bits. In addition, some bits, such as
the ownership information and control information, are required to be more robust.
Uneven distribution of embedding capability: The amount of data that can be embedded
often vary widely from region to region in image and video. This uneven embedding
capacity causes serious difficulty to high-rate embedding.
Problem Statement
The aim of this project is to implement different algorithms using SVD for hiding data in
binary images and then finding out which among them is the best approach using a set of
parameters such as mean, variance, covariance and PSNR value.
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Overview of proposed solution approach
I am going to implement the following data hiding algorithms and then will try to find out the
best among them based on set of parameters.
Method 1 is based on the one proposed by Bergman and Davidson
Method 2 is based on the one proposed by Chang et al
Method 3 is the method proposed by Chung et al
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Background Study
Literature Survey
Summary of Research Paper
Title of Paper: - Techniques for Data Hiding
Authors: - W. Bender, D.Gruhl, N.Morimoto and A.Lu
Year of Publication:- 1996
Publishing Detail:- It was published in IBM Systems Journal
Summary:-Several techniques are discussed as possible methods for embedding data in
host text, image, and audio signals. All of the proposed methods have limitations.
The goal of achieving protection of large amounts of embedded data against intentional
attempts at removal may be unobtainable. Automatic detection of geometric and no
geometric modifications applied to the host signal after data hiding is a key data-hiding
technology. The optimum tradeoff between bit rate, robustness, and perceiveability
need to be defined experimentally. The interaction between various data-hiding technologies
needs to be better understood. While compression of image and audio content continues
to reduce the necessary bandwidth associated with image and audio content, the need for a
better contextual description of that content is increasing. Despite its current shortcomings,
data-hiding technology is important as a carrier of these descriptions.
Web Link:-www.cs.utsa.edu/~jortiz/.../Techniques%20for%20Data%20Hiding-p.pdf
Title of Paper: - Steganography Technique based on SVD
Authors: - Yambem Jina Chanu, Kh. Manglem Singh and Themrichon Tuithung
Year of Publication:- 2007
Publishing Detail:- It was published in International Journal of Research in Engineering
and Technology (IJRET)
Summary:-The paper proposes an image steganography technique that embeds the secret
message in the left singular vectors, singular values and right singular vectors of the vectors
of blocks of the image in such a way that the visual quality of the image is not affected due toembedding of the message. The technique was compared with other two existing methods on
http://www.cs.utsa.edu/~jortiz/.../Techniques%20for%20Data%20Hiding-p.pdfhttp://www.cs.utsa.edu/~jortiz/.../Techniques%20for%20Data%20Hiding-p.pdfhttp://www.cs.utsa.edu/~jortiz/.../Techniques%20for%20Data%20Hiding-p.pdfhttp://www.cs.utsa.edu/~jortiz/.../Techniques%20for%20Data%20Hiding-p.pdf -
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different color images, and was found that the proposed method is comparatively better than
the other methods under consideration. The future plan is to test the proposed method under
different steganalytic attacks.
Web Link:-http://psrcentre.org/images/extraimages/IJRET016061.pdf
Title of Paper: - Singular Value Decomposition and Principal Component Analysis
Authors: - Rasmus Elsborg Madsen, Lars Kai Hansen and Ole Winther
Year of Publication:- 2004
Publishing Detail:- It was published in Neural Networks for Pattern Recognition Journal
Summary:- In principal component analysis wend the directions in the data with the most
variation, i.e. the eigenvectors corresponding to the largest eigenvalues of the covariance
matrix, and project the data onto these directions. The motivation for doing this is that the
most second order information are in these directions.1 The choice of the number of
directions are often guided by trial and error, but principled methods also exist.
Web Link:-www.imm.dtu.dkpubdbviewsedocdownload.php...imm.pdf
http://psrcentre.org/images/extraimages/IJRET016061.pdfhttp://psrcentre.org/images/extraimages/IJRET016061.pdfhttp://psrcentre.org/images/extraimages/IJRET016061.pdfhttp://psrcentre.org/images/extraimages/IJRET016061.pdf -
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Integrated Summary
Applications OF Data Hiding
Placing data in images is useful in a variety of applications. We highlight below four
applications that differ in the quantity of data to be embedded and the type of transforms to
which the data are likely to be subjected.
First techniques included invisible ink, secret writing using chemicals, templates laid over
text messages, microdots, changing letter/word/line/paragraph spacing, changing fonts
Images, video, and audio files provide sufficient redundancy for effective data hiding
Postscript files, PDF files, and HTML can also be used for non-robust data hiding to a limited
extent Executable files, provide very little space for data hiding Fonts
Need Of Data Hiding
Covert communication using images (secret message is hidden in a carrier image)
Ownership of digital images, authentication, copyright, Data integrity, fraud detection, self-
correcting images Traitor-tracing (fingerprinting video-tapes) Adding captions to images,additional information, such as subtitles, to video, embedding subtitles or audio tracks to
video (video-in-video) Intelligent browsers, automatic copyright information, viewing a
movie in a given rated version Copy control (secondary protection for DVD)
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Properties Of Data Hiding
Robustness
The ability to extract hidden information after common image processing operations: linear
and nonlinear filters, lossy compression, contrast adjustment, recoloring, resampling, scaling,
rotation, noise adding, cropping, printing / copying / scanning, D/A and A/D conversion,
pixel permutation in small neighborhood, color quantization (as in palette images), skipping
rows / columns, adding rows / columns, frame swapping, frame averaging (temporal
averaging), etc.
Un-Detectability
Impossibility to prove the presence of a hidden message. This concept is inherently tied to thestatistical model of the carrier image. The ability to detect the presence does not
automatically imply the ability to read the hidden message. Undetectability should not be
mistaken for invisibility a concept related to human perception.
Invisibility
Perceptual transparency. This concept is based on the properties of the human visual system
or the human audio system.
Security
The embedded information cannot be removed beyond reliable detection by targeted attacks
based on a full knowledge of the embedding algorithm and the detector(except a secret key),
and the knowledge of at least one carrier with hidden message.
Steganography v/s Digital Watermarking
In digital watermarking, the focus is on ensuring that nobody can remove or alter the content
of the watermarked data, even though it might be plainly obvious that it exists.
Steganography on the other hand, focuses on making it extremely difficult to tell that a secret
message exists at all. If an unauthorized third party is able to say with high confidence that a
file contains a secret message, then steganography has failed.
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Steganography v/s Cryptography
Cryptography does not attempt to hide the fact that a message exists. Instead, it merely
obscures the integrity of the information so that it does not make sense to anyone but thecreator and the recipient. The adversary will be able to see that a message exists, and the
inverse process of cryptanalysis involves trying to turn the meaningless information into its
original form.
The "secrecy" of the embedded data is essential in this area. Historically, steganography have
been approached in this area. Steganography provides us with:
(A) Potential capability to hide the existence of confidential data
(B) Hardness of detecting the hidden (i.e., embedded) data
(C) Strengthening of the secrecy of the encrypted data
Details of empirical study (Field survey, Experimental studies)
Data hiding represents a class of processes used to embed data, such as copyright
information, into various forms of media such as image, audio, or text with a minimum
amount of perceivable degradation to the host signal; i.e., the embedded data should be
invisible and inaudible to a human observer.
Data hiding, while similar to compression, is distinct from encryption. Its goal is not to
restrict or regulate access to the host signal, but rather to ensure that embedded data remain
inviolate and recoverable.
Two important uses of data hiding in digital media are to provide proof of the copyright, and
assurance of content integrity. Therefore, the data should stay hidden in a host signal, even if
that signal is subjected to manipulation as degrading as filtering, resampling, cropping, or
lossy data compression. Other applications of data hiding, such as the inclusion of
augmentation data, need not be invariant to detection or removal, since these data are therefor the benefit of both the author and the content consumer. Thus, the techniques used for
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data hiding vary depending on the quantity of data being hidden and the required invariance
of those data to manipulation. Since no one method is capable of achieving all these goals, a
class of processes is needed to span the range of possible applications.
The technical challenges of data hiding are formidable. Any holes to fill with data in a host
signal, either statistical or perceptual, are likely targets for removal by lossy signal
compression. The key to successful data hiding is the finding of holes that are not suitable for
exploitation by compression algorithms. A further challenge is to fill these holes with data in
a way that remains invariant to a large class of host signal transformations.
Feature tagging. Another application of data hiding is tagging the location of features within
an image. Using data hiding it is possible for an editor (or machine) to encode descriptive
information, such as the location and identification of features of interest, directly into
specific regions of an image. This enables retrieval of the descriptive information wherever
the image goes. Since the embedded information is spatially located in the image, it is not
removed unless the feature of interest is removed. It also translates, scales, and rotates exactly
as the feature of interest does.
Each application of data hiding requires a different level of resistance to modification and a
different embedded data rate. These form the theoretical data-hiding problem space. There is
an inherent trade-off between bandwidth and robustness, or the degree to which the data
are immune to attack or transformations that occur to the host signal through normal usage,
e.g., compression, resampling, etc. The more data to be hidden, e.g., a caption for a
photograph, the less secure the encoding. The less data to be hidden, e.g., a watermark, the
more secure the encoding.
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Analysis, Design and Modeling
Requirement Specifications
Computer (Desktop, Laptop)
Windows/Linux/Mac OS
Microsoft Visual C++
Compiler
Functional requirements
For the project to work we need Matlab to be installed in the system other than that a working
pc/laptop with windows/mac/linux installed.
The user will need to select an image to work on. Then the system will pop out histogram of
original image and steganography image along with window of original image and
steganography image.
Then go check out the original image and steganography images mean, PSNR value,
correlation we will have to go the output tab. There we will get all our details.
Input: A binary image
Output: A steganography image (image with data hidden)
Non Functional requirements
Performance Requirements
Performance requirements define acceptable response times for system functionality.
The load time should not be longer.
The data hiding should be done in a matter of seconds.
Safety Requirements
It is completely safe to use but keep it in mind that the data is not fully secure others too can
extract it. So be careful
Security Requirements
As the data can be retrieved by other people too as the techniques are not 100% secure so
while dealing with sensitive data you have to be more careful
Efficient: The product is completely efficient.
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Overall architecture with component description and dependency
details
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Design Documentation
Use Case Diagram
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Class Diagram/ Control Flow Diagram
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Sequence Diagram/ Activity Diagram
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Algorithms
Method 1: Algorithm by Bergman and Davidson
Embedding Algorithm
1. Compute the normal singular value decomposition, , of each block of A.
2.Transform into :
(a) Set certain components =.||, where=message [1 -1]
(b) Choose remaining components to ensure that is still
orthogonal.
3.Compute=.
4. Round the entries in .
Resulting matrix, will be a block of the stego-image.
Extraction Algorithm
denotes the stego image and is applied to retrieve the hidden message.
1. Compute the U~S~VT~of .
2. Extract payload bits from the signs of the entries in the triangular portion of
~:=~/|~|
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Method 2: Algorithm by Chang et al
Embedding Algorithm
Input: Block , where =,1,,3,,161
=binary secret message
Output:A stego image
Perform SVD on the block , generating the corresponding , Sand matrices.
If W(, j)==1
4= 23,if 3>(23)
0, otherwise.
and 2=2+, where is threshold.
Perform inverse SVD.
Repeat these steps until all secret message have been embedded.
Combine all stego blocks.
Extracting Algorithm
Input: Stego block , where =,1,,3,,161
Output: The extracted hidden message
Perform SVD on each blocks of image
10 0 0
3. Let SWK= 0 20 0
0 0 30
0 0 0 4
The extracted hidden message is given by
EW(,)= 1,if 23>/2.
0, Otherwise.
Repeat the above steps until all hidden message bits are extracted.
Combine all stego blocks to form the stego image .
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Method 3: Algorithm by Chung et al
Embedding Algorithm
Input: Block , where =,1,,3,,161, binary secret message
Output: A stego image
Perform SVD on the block of 44 size
If W(,j) = = 1
Modify 2,1and 3,1are modified to satisfy the condition|2,1| |3,1| .
Otherwise the condition
|3,1||2,1| >
must be held.
Perform inverse SVD.
Repeat the above steps until all binary pixels of the secret message have been
embedded. Combine all stego blocks to form the stego.
Extracting Algorithm
Input: Stego block , where =,1,,3,,161
Output: The extracted hidden message
Perform SVD on the block generating the corresponding , and
matrices.
The extracted hidden message is given by
1, if |2,1| |3,1|
EW(,)= 0, otherwise
Repeat the above steps until all hidden message bits are extracted
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Risk Analysis and Mitigation Plan
Risk
Id
Risk
Description
Risk
AreaProb Impact RE
(P*I)
Risk
forM Plan
M Plan C
Plan
1
Matlab needsspecificHardwareRequirementspeople having oldcomputers mightnot be able to run italso it is a paidsoftware
Hardware Low High Medium No __ __
2Sometimes thecode might notfunction properlydue to the image
type
Software Medium High High Yes
Specificallyinformingthe userthat onlyimages of
certaindimensionthat toobinaryimages willwork
__
3
Not all users mightbe able to work itout properly
Software Low Medium Low Yes
A proper
SRS willbe providedso that
users canlearn how
to properlyoperate thesoftware
If the user
still needshelp evenafter
readingSRS then
online helpcan beprovidedwhere anybody could
askquestions
andanybodycould
answerthem
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Implementation and Testing
Implementation details and issues
I have been able to complete the method 2 and method 3 by Chung et al and Chang et al
respectively. The above two algorithms use SVD to hide data in the image.
SVD includes :
Matrix A of size m n
factoring the matrix (A) into three part USVT
where
Orthogonal matrices:
U : Left singular vector
V : Right singular vector
S: is a diagonal matrix
While implementing the algorithms there were some issues coming with calculating PSNR
value. I tried to calculate it by using different methods and was finally able to get it right.I am implementing the algorithms to work on binary images but for experimental purposes I
tried these algorithms on colored images and there came out matrix mismatch then I tried
converting the colored images in greyscale then in black & white then the results were
favorable. So my project can handle both colored as well as binary images, only we have to
convert the colored image at run time. Finally I was able to implement 2/3 algorithms due to
shortage of time. The results of the algorithm came out to be favorable and I was able to
come to a conclusion.
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Testing
Testing Plan
Type of TestWill Test Be
Comments/Explanations Software ComponentPerformed?
Requirements Testing Yes
Without proper requirementfulfillments the project will not beable to run. Majority of errors are
associated with not being able tocomplete the requirements.
Testing will beperformed onwhether the systemis having the properoperating system
with matlab installedor not
Unit Yes
Unit testing needs to be done aseven if a small module isnt workingthe way it is supposed to then thewhole product is doomed
There are 3algorithms being
used each will betested to see whetherthe output is comingout to be what weaccepted or not
Integration No
There is no need to do integrationtesting as the modules are separatethere is no integration between them ______
Performance Yes
To check whether my product ismeeting the performancerequirement as performance is themajor core of any product it can
Whether the
algorithms are
providing result
within a stipulatedtime
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build and destroy products
Stress Yes
Stress testing is an important
criteria which helps us in
knowing what all can our
product handle
All the algorithmswill be tested withdifferent types ofimages to see whatall can our producthandle
Compliance No
Because it is related with whetherIT standard are followed or notwhich is not applicable here
_______
____
_______
Security Yes
To check whether our data is
secure or not
We will try to
decrypt the
message using
other algorithms
Load NoBecause we are using a single
image at a time so there is noneed to do load test _______
Volume NoWe are only dealing with one
image at a time. So need for
volume test. ____
____
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Test Schedule
Activity Start Date Completion
Date
Hours Comments
Requirement
Testing
06/12/2013 06/12/2013 1 System tested
for availability
of matlab
Unit Testing 07/12/2013 07/12/2013 4 The output
coming is
favorable
Performance
Testing
08/12/2013 08/12/2013 3 Time to get the
embedded image
is very low
hence very good
Stress Test 09/12/2013 09/12/2013 5 The results were
favorable
Security Test 10/12/2013 10/12/2013 7 The system if
almost secure
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Test EnvironmentSoftware Items
OS:
Min Requirement:Windows XP x64 Edition Service Pack 2
Disk Space:
1 GB for MATLAB only,34 GB for a typical installation
Hardware Items
Ram:1024 MB(At least 2048 MB recommended)
Processor :Any Intel or AMD x86 processor supporting SSE2 instruction set
-PROVIDE A DESCRIPTION OF THE TEST PLATFORMS
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Component decomposition and types of testing required
S.No. Components Type of Testing Reqd. Technique for Writing Test cases
1 Matlab(algorithms) Requirement White Box Testing
2 Matlab
(algorithms)
Unit White Box Testing
3 Matlab
(algorithms)
Performance Black Box Testing
4 Matlab
(algorithms)
Stress Black Box Testing
RequirementWhite Box Testing
Id 1:
Checking to see whether the system is meeting the minimum requirement
UnitWhite Box Testing
Id 2:
All the 2/3 algorithms will be checked to see that there is no discrepancy coming out in any
result.
Performance
Id 3: Various set of images are used to check whether the steganography images are coming
out in justifiable time or not.
Stress
Id 4: Images of different type like binary and coloured images are used to check whether the
system can handle them or not
Security
Id 5: Other algorithms are used to try to retrieve the message embedded by some other
algorithm to find out whether the system is secure or not
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Test Case ID Input Expected Output Pass/Fail1 Code Some outcome Pass
2 Binary Image Steganography Image Pass
3 Binary Image Fast Result Pass (Within a minute)
4 Colored & Binary Image Steganography Image Pass
5 Steganography Image Failure to decrypt message Pass
Limitation of the Solution
We can only use images of dimension 256*256
The system is not totally secure as after finding out which algorithm has
been used the embedded information can be retrieved easily
There is no graphical user interface
The image to be worked upon needs to be hard coded
The project will not be able to work on coloured images without
converting them to greyscale then to black & white.
You can only embed data in binary form
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Finding and Conclusion
Findings
I tested my algorithm on these images
The method 2 is showing higher PSNR value then method1. The mean, variance and
correlation value are coming to be almost close
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Conclusion
I have tried to implement image steganography technique that embeds the secret message in
the left singular vectors, singular values and right singular vectors of the vectors of blocks of
the image in such a way that the visual quality of the image is not affected due to embedding
of the message. The technique was compared between method proposed by Chang et al and
Chung et al on different color as well as binary images, and was found that the method 2 is
comparatively better than the other method under consideration.
Future Work
The future plan is to come up with a much better technique for data hiding and also to test the
different methods under different steganalytic attacks
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Appendix
Project Plan
Phase Description of Work Start and End Dates
Phase One Project Proposal Week 1
Phase Two Setting up environment Week 1
Phase Three Understanding the Various Methods Week 28
Phase Four Implementation of the various methods Week 9-10
Phase Five Differentiating between different methods Week 10-12
Phase Six Coming up with a conclusion Week 12-13
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References
[1] F. Petticolas, Information hiding techniques for steganography and digital watermarking, Stefen
Katzenbeisser, Artech house books, ISBN 158053-035-4, Dec. 1999.
[2]C. Bergman and J. Davidson, Unitary embedding for data hiding with the SVD, Security, Steganography, and
Watermarking of
Multimedia Contents VII, SPIE, vol. 5681, San Jose, Jan., 2005.
[3] M. Y. Wu and J. H. Lee. A Novel Data Embedding Method for Two-Color Facsimile Images. In
Proceedings of International Symposium on Multimedia Information Processing, Chung-Li, Taiwan, R.O.C,
December 1998
[4] F. Petitcolas, R. Anderson and M. Kuhn Information Hiding, A Survey Proceedings of the IEEE, special
issue on protection of multimedia content, July 1999.
[5] W. Bender, D. Gruhl, N. Morimoto, and A. Lu, Techniques for data hiding,IBM Systems Journal, Vol. 35,No. 3 and 4, pp. 313-336, 1996.
[6] C-C. Chang, C-C. Lin and Y-S. Hu, An SVD oriented watermark embedding scheme with high qualities for
the restored images, IJICIC, vol. 3, no. 3, pp. 609-620, June 2007
[7] M.M. Hadhoud and A.A. Shallan, An efficient SVD image steganographic approach, IEEE ICCES, pp. 257-
262, 14-16, December 2009.