data hiding in a binary image full report

7/27/2019 Data Hiding in a binary image Full Report

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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


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


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.

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