Final Project New

VISUAL SECRET IMAGE SHARING USING ERROR DIFFUSION

A PROJECT REPORT

Submitted by

SRIRAMACHANDRAN.R.S (9907004266) VIGNESH.S (9907004300) SUGAN.K (9907004271)

in partial fulfillment for the award of the degree

of

BACHELOR OF TECHNOLOGY

in

COMPUTER SCIENCE & ENGINEERING

KALASALINGAM UNIVERSITY

(KALASALINGAM ACADEMY OF RESEARCH & EDUCATION)

KRISHNANKOIL 626190

MAY 2011

i

KALASALINGAM UNIVERSITY

(KALASALINGAM ACADEMY OF RESEARCH & EDUCATION)

KRISHNANKOIL 626190

BONAFIDE CERTIFICATE

Certified that this project report “VISUAL SECRET IMAGE SHARING USING

ERROR DIFFUSION” is the bonafide work of “SRIRAMACHANDRAN.R.S

(9907004266),VIGNESH.S (9907004300), SUGAN.K (9907004271)” who carried

out the project work under my supervision.

SIGNATURE SIGNATURE

Dr.D.DEVARAJ., Mr.S.VEERAMANI.,

HEAD OF THE DEPARTMENT SUPERVISOR

Kalasalingam University Kalasalingam University

Anand Nagar, Anand Nagar,

Krishnankoil-626190 Krishnankoil-626190

Viruthunagar District Viruthunagar District

Submitted for the project viva-voce held on: _____________

INTERNAL EXAMINER EXTERNAL EXAMINER

1

ACKNOWLEDGEMENT

We give all the glory and thanks to our ALMIGHTY GOD who showered the

necessary and grace upon us for accomplishing the project.

I express my sincere gratitude to our beloved vice-chancellor

Dr.S.RADHAKRISHNAN Ph.D., for all his valuable advice to make this project a

great success.

We extent our profound thanks to Dr.D.DEVARAJ., Head of the

Department, COMPUTER SCIENCE &ENGINEERING, for his valuable suggestion

throughout the project work.

We express our sincere complete thanks to our project coordinator

K.KIRUTHIKA.,M.E., Lecturer, for encouraging us to make our project a successful

one.

We are filled with utmost gratitude to thank our project

guide Mr. S.VEERAMANI M.E., Lecturer, for her superlative efforts and

espousals without which our project work would have been stand still for a while

without undergoing completion.

I wish to express my thanks and gratitude to my family and beloved ones and

all the staff members of “Department of computer science & engineering” for having

heled me so generously with their valuable and constructive suggestions to improve

the project and provide steady support.

2

ABSTRACT

Halftone visual cryptography (HVC) enlarges the area of visual

cryptography by the addition of digital halftoning techniques. In

particular, in visual secret sharing schemes, a secret image can be

encoded into halftone shares taking meaningful visual information.

The decoded image shows the secret image with 50%

contrast loss. In this paper, a new HVC scheme is introduce to

overcome the drawback. The secret image is concurrently embedded into

binary valued shares while these shares are halftoned by error diffusion

—the workhorse standard of halftoning algorithms.

Error diffusion has low complexity and provides halftone shares

with good image quality. A reconstructed secret image, obtained by

stacking qualified shares together, does not suffer from cross

interference of share images.

3

TABLE OF CONTENTS

4

5

CHAPTER

NO

TITLE PAGE NO.

ABSTRACT

LIST OF TABLES

LIST OF FIGURES

LIST OF ABBREVIATION

iv

vii

viii

ix

1. INTRODUCTION

1.1 Objective

1.2 About HVC

1

1 3

2. SYSTEM ANALYSIS

2.1 Existing System

2.1.1 Drawback

2.2 Proposed System

2.2.1 Advantage

2.3 Problem Description

4

4

4

5

5

5

3. SYSTEM SPECIFICATION

3.1 Software Requirements

3.2 Hardware Requirements

3.3 Methodology

3.3.1 Rapid Prototyping

8

8

8

9

9

4. LANGUAGE DESCRIPTION

4.1 About Mat lab

4.2 Interface with other languages

4.3 Features

4.4 Development Environment

4.5 File I/O and external interfacing

4.6 Performance

4.7 About Visual Basic

4.7.1 Features

10

10

10

11

12

12

13

13

13

5. PROJECT DESCRIPITION 15

6. SYSTEM DESIGN

6.1 System Design

6.2 Algorithm

6.2.1 Error Diffusion

6.2.2 Analysis of ED Algorithm

6.2.3 Parallel ED

20

20

20

20

22

22

LIST OF TABLE

S.NO. TABLE NO. TABLE NAME PAGE NO.

1 6.3 Phase Description 24

6

LIST OF FIGURES

7

S.NO. FIGURE NO. FIGURE NAME PAGE NO.

1 8.2.1 Transmission side form

33

2 8.2.2 Reception side form

34

3 8.2.3 Before error diffusion

35

4 8.2.4 Error diffusion 36

5 8.2.5 Encrypt the secret image

37

6 8.2.6 Sending of the image

38

7 8.2.7 Receiving of output image

39

8 8.2.8 View of the secret image

40

8

CHAPTER 1

INTRODUCTION

1.1 Objective

Cryptography is the practice and study of hiding information. Modern

cryptography intersects the disciplines of mathematics, computer science, and

electrical engineering. Applications of cryptography include ATM cards,

computer passwords, and electronic commerce.It to the modern age was

almost synonymous with encryption, the conversion of information from a

readable state to apparent nonsense.

The sender retained the ability to decrypt the information and therefore

avoid unwanted persons being able to read it. Since WWI and the advent of

the computer, the methods used to carry out cryptology have become

increasingly complex and its application more widespread.

Before the modern era, cryptography was concerned solely with

message confidentiality (i.e., encryption)—conversion of messages from a

comprehensible form into an incomprehensible one and back again at the

other end, rendering it unreadable by interceptors or eavesdroppers without

secret knowledge .Encryption was used to (attempt to) ensure secrecy in

communications, such as those of spies, military leaders, and diplomats. In

recent decades, the field has expanded beyond confidentiality concerns to

include techniques for message integrity checking, sender/receiver identity

authentication, digital signatures, interactive proofs and secure computation,

among others

9

Visual cryptography is a cryptographic technique which allows visual

information (pictures, text, etc.) to be encrypted in such a way that the

decryption can be performed by the human visual system, without the aid of

computers. It demonstrated a visual secret sharing scheme, where an image

was broken up into n shares so that only someone with all n shares could

decrypt the image, while any n-1 shares revealed no information about the

original image. Each share was printed on a separate transparency, and

decryption was performed by overlaying the shares. When all n shares were

overlaid, the original image would appear.

If each pixel in the original image is split randomly into two shares as

described above, the shares are correlated together. Still, when each

individual share is considered alone , it is a totally random collection of

blocks. Given only one share, a second share can be crafted to reveal any

possible image; therefore, individual shares reveal no information about the

original image.

Visual Cryptography is a paradigm for cryptographic schemes that

allow the decoding of concealed images without any cryptographic

computation. Particularly in a -out-of- visual secret sharing (VSS) scheme, a

secret image is cryptographically encoded into n shares. Each

share resembles a random binary pattern.

10

1.2 About HVC

Visual cryptography encodes a secret binary image (SI) into n shares

of random binary patterns. If the shares are xeroxed onto transparencies, the

secret image can be visually decoded by superimposing a qualified subset of

transparencies, but no secret information can be obtained from the

superposition of a forbidden subset.

The binary patterns of the n shares, however, have no visual meaning

and hinder the objectives of visual cryptography. Extended visual

cryptography was proposed recently to construct meaningful binary images

as shares using hypergraph colourings, but the visual quality is poor.

In the proposed HVC methods, pixels that carry the secret

image information are predetermined before a halftone share

is generated. These pixels are then naturally embedded into

the halftone shares when the grayscale images are halftoned.

Error diffusion, a simple and widely used halftone method

that yields a good compromise between the image quality

and the computational complexity

11

CHAPTER 2

SYSTEM ANALYSIS

2.1 Existing System

Halftone visual cryptography (HVC) enlarges the area of

visual cryptography by the addition of digital half toning

techniques.

In visual secret sharing schemes, a secret image can be

encoded into halftone shares taking meaningful visual

information. The scheme is secure and very easy to

implement.

2.1.1 Drawbacks of Existing System

The decrypted image is fluctuated from the original image

in size.

Also the output image is reducing in contrast.

12

2.2 Proposed System

In this proposed system the method error diffusion is used to

overcome the limitation in the HVC method

The contrast of the image is improved by this method, the

decrypted image is equally relative to original image and it is better than

HVC image. Error diffusion has low complexity and provides

halftone shares with good image quality.

2.2.1 Advantage of Proposed System

The output image is reducing in contrast.

It is very easy to implement

A reconstructed secret image, obtained by

stacking qualified shares together, does not

suffer from cross interference of share

images.

2.3 Problem Description

The HVC proposed by Zhi Zhori and Gonzalo R. Arce is

enlarges the area of visual cryptography by the addition of digital halftoning

techniques. In particular, in visual secret sharing schemes, a secret image can

be encoded into halftone shares taking meaningful visual information.

13

In this proposed system visual cryptography method based on

Error diffusion. Error diffusion has low complexity and provides

halftone shares with good image quality. A reconstructed secret

image, obtained by stacking qualified shares together, does not suffer from

cross interference of share images.

Error diffusion is a simple, yet efficient algorithm to

halftone a grayscale image. The quantization error at each

pixel is filtered and fed back to a set of future input samples.

The first component is the thresholding block where the output g(m, n) is

given by

The threshold t(m,n) can be position-dependent.

The second component is the error filter h(k,i) whose input

e(m,n) is the difference between d(m,n) and g(m,n). Finally,

we can compute as

As an example, the widely used Floyd–Steinberg error filter ,

where indicates the current pixel. The weights of the filter are given by

,

The recursive structure indicates that the quantization

error e(m,n) depends not only on the current input and

14

output but also on the entire past history. The error filter is

designed in such a way that the low frequency

Difference between the input and output image is minimized.

The error that is diffused away by the error filter is

high frequency or “blue noise” in nature, leading to visually

pleasing halftone images. As will be described all the shares in

our proposed HVC method are generated by a constrained

error diffusion based on the algorithm introduced above.

In the second construction method to be

introduced shortly, it is necessary to generate mutually

exclusive sets of pixels. To this end, the method of error

diffusion is modified so as to produce multitone output pixels

where the pixels of each tone are assigned to a pixel set.

Multitone error diffusion is obtained by simply

replacing the thresholding block by a multilevel quantization

block in halftone error diffusion. The number of output levels

of the quantization block is the same as the number of tones

of the multitone image.

Multitone error diffusion can generate multitone

images where the pixels of each tone are homogeneously

15

distributed. The multitone error diffusion algorithm used here

for the generation of mutually exclusive pixel sets. This

algorithm jointly optimizes the distribution of multitone pixels

by locating the pixels of different tones in a correlated

fashion so that the mutual interference between different

tones is minimized and multitone pixels are well separated

from each other.

CHAPTER 3

SYSTEM SPECIFICATION

3.1 Software Requirements

The software required for the system is as follows

Language : Matlab 7.4

Front End Tool : Visual Basic

Back End Tool : Matlab 7.4

Operating System : Windows XP, Windows 7

3.2 Hardware Requirements

Monitor : 1024*768 minimum resolutions of 256 colors

Processor : Atleast 2GHz Intel Core 2 processor

16

Input : Two or Three button mouse and standard 104 Keyboard.

Hard Disk : 40 GB

Primary Memory : 512MB RAM

3.3 Methodology

3.3.1 Rapid Prototyping

The methodology deployed for the development of this project is

prototyping. This is one of the best options to develop real time projects. The

applications which are to be built in the quick space of time can be

effectively created using this method.

First of all, a model of the application is examined with the

minimal and sufficient requirements. Then the application further evolves

based on the user views about the model. Due to this end user can get a clear

picture of what and how the product will look like and the programmer can

accommodate changes easily. Hence this method of specification

development was chosen for this project.

17

CHAPTER 4

LANGUAGE DESCRIPTION

4.1 About MATLAB

MATLAB (for matrix laboratory) is a numerical computing

environment and fourth-generation programming language. Developed by

MathWorks, MATLAB allows matrix manipulations, plotting of functions

and data, implementation of algorithms, creation of user interfaces, and

interfacing with programs written in other languages, including C, C++, and

Fortran.

Although MATLAB is intended primarily for numerical computing, an

optional toolbox uses the MuPAD symbolic engine, allowing access to

symbolic computing capabilities. An additional package, Simulink, adds

graphical multi-domain simulation and Model-Based Design for dynamic and

embedded systems.

In 2004, MATLAB had around one million users across industry and

academia. MATLAB users come from various backgrounds of engineering,

18

science, and economics. MATLAB is widely used in academic and research

institutions as well as industrial enterprises.

4.2 Interface with other Language

MATLAB can call functions and subroutines written in the C

programming language or Fortran. A wrapper function is created allowing

MATLAB data types to be passed and returned. The dynamically loadable

object files created by compiling such functions are termed "MEX-files" (for

MATLAB executable).

Libraries written in Java, ActiveX or .NET can be directly called from

MATLAB and many MATLAB libraries are implemented as wrappers

around Java or ActiveX libraries.

Calling MATLAB from Java is more complicated, but can be done

with MATLAB extension, which is sold separately by Math Works, or using

an undocumented mechanism called JMI (Java-to-Mat lab Interface), which

should not be confused with the unrelated Java Metadata Interface that is also

called JMI.As alternatives to the MiPads based Symbolic Math Toolbox

available from Math Works, MATLAB can be connected to Maple or

Mathematical.

4.3 Features

MATLAB can call functions and subroutines written in the C programming

language or Fortran. A wrapper function is created allowing MATLAB data

types to be passed and returned. The dynamically loadable object files

19

created by compiling such functions are termed "MEX-files" (for MATLAB

executable).

Libraries written in Java, ActiveX or .NET can be directly called from

MATLAB and many MATLAB libraries (for example XML or SQL support)

are implemented as wrappers around Java or ActiveX libraries. Calling

MATLAB from Java is more complicated, but can be done with MATLAB

extension, which is sold separately by MathWorks, or using an

undocumented mechanism called JMI (Java-to-Matlab Interface), which

should not be confused with the unrelated Java Metadata Interface that is also

called JMI.

As alternatives to the MuPAD based Symbolic Math Toolbox available from

MathWorks, MATLAB can be connected to Maple or Mathematica.

4.4. Development Environment

Editor highlighting enhanced to show all uses of a variable or sub

function and identify shared variables

Ability to manage ZIP files as folders in the Current Folder

Browser

File previews in Current Folder Browser that show image file

contents and unsaved content in MATLAB files

Access to additional toolbox plots in the Plot Selector, including

plots for System Identification, Mapping, and Bioinformatics

Toolboxes

Comparison Tool enhanced, allowing comparison among ZIP files,

folders, and Simulink manifests, and improving MAT-file

comparisons

20

4.5 File I/O and External Interfacing

New VideoWriter object for creating Motion JPEG and

uncompressed AVI files larger than 2 GB

netCDF 4.0.1 support, enabling use of HDF5 as the data storage

layer for the netCDF API

Enhanced interface to Microsoft® .NET framework, supporting

delegates and interaction with Microsoft Office products

4.6 Performance Ability to perform FFTs on vectors larger than 2 GB

Multithreaded computation support for sort, filter, bsxfun, sparse

matrix QR decomposition, gamma functions, and error functions

Sparse matrix performance improvements for basic math, binary

and relational operators, and exponential functions

4.7 About Visual Basic

Visual Basic (VB) is the third-generation event-driven programming

language and integrated development environment (IDE) from Microsoft for

its COM programming model. Visual Basic is relatively easy to learn and

use.

Visual Basic was derived from BASIC and enables the rapid

application development (RAD) of graphical user interface (GUI)

applications, access to databases using Data Access Objects, Remote Data

Objects, or ActiveX Data Objects, and creation of ActiveX controls and

21

objects. Scripting languages such as VBA and VBScript are syntactically

similar to Visual Basic, but perform differently

4.7.1 Features

Like the BASIC programming language, Visual Basic was designed to

be easily learned and used by beginner programmers. The language not only

allows programmers to create simple GUI applications, but can also develop

complex applications. Programming in VB is a combination of visually

arranging components or controls on a form, specifying attributes and actions

of those components, and writing additional lines of code for more

functionality.

Since default attributes and actions are defined for the components, a

simple program can be created without the programmer having to write many

lines of code. Performance problems were experienced by earlier versions,

but with faster computers and native code compilation this has become less

of an issue.

The language is garbage collected using reference counting, has a large

library of utility objects, and has basic object oriented support. Since the

more common components are included in the default project template, the

programmer seldom needs to specify additional libraries. Unlike many other

programming languages, Visual Basic is generally not case sensitive,

although it will transform keywords into a standard case configuration and

force the case of variable names to conform to the case of the entry within the

symbol table. String comparisons are case sensitive by default, but can be

made case insensitive if so desired.

22

CHAPTER 5

PROJECT DESCRIPTION

Visual Cryptography is a paradigm for cryptographic schemes that

allow the decoding of concealed images without any cryptographic

computation. Particularly in a -out-of- visual secret sharing (VSS) scheme, a

secret image is cryptographically encoded into n shares. Each share

resembles a random binary pattern. The n shares are then copied onto

transparencies, respectively, and distributed among n participants.

The secret images can be visually revealed by stacking together any or

more transparencies of the shares and no cryptographic computation is

needed. However, by inspecting less than shares, one cannot gain any

information about the secret image, even if infinite computational power is

available.

Aside from the obvious applications to information hiding, VC can be

applied to access control, copyright protection watermarking, visual

authentication and identification. To illustrate the principles of VSS, consider

a simple 2-out-of-2 VSS scheme shown. Each pixel p taken from a secret

23

binary image is encoded into a pair of black and white sub pixels in each of

the two shares.

If p is white/black, one of the first/last two columns tabulated under the

white/black pixel in is selected. The selection is random such that each

column is selected with 50% probability. Then, the first two sub pixels in that

column are assigned to share 1 and the following two sub pixels are assigned

to share 2. Independent of whether is black or white is encoded into two sub

pixels of black–white or white–black with equal probabilities.

Thus an individual share gives no clue as whether is black or white. If

the pixel p is black, the preposition of the two shares outputs two black sub

pixels corresponding to a gray level 1. If is p white, it results in one white and

one black sub pixel, corresponding to a gray level 1/2. Then by stacking two

shares together, we can obtain the full information of the secret image. The 2-

out-of-2shows a secret binary image SI to be encoded.

According to the encoding rule, each pixel p of SI is split into two sub

pixels in each of the two shares, superimposing the two shares leads to the

output secret image. The decoded image is clearly identified, although some

contrast loss occurs.

The width of the reconstructed image is twice that of the original secret

image since each pixel is expanded to two sub pixels in each share. The 2-

out-of-2 VSS scheme demonstrated above is a special case of the k-out-of- n

VSS scheme proposed an optimal contrast -out-of- scheme to alleviate the

problem of contrast loss in the reconstructed images designed a more general

model for VSS schemes based on general access structures.

24

An access structure is a specification of all the qualified and forbidden

subsets of shares. The participants in a qualified subset can recover the secret

image while the participants in a forbidden subset cannot. The concept of

VSS has also been extended such that the secret image can be a gray scale

image rather than a binary image.

The aforementioned VC methods all have the disadvantage that the

shares consisting of random pixel patterns do not take any visual information

and may lead to suspicion of secret information encryption. Shares showing

meaningful images are more desirable in terms of the steganography aspect.

To alleviate this problem, developed the method of extended visual

cryptography (EVC).

In EVC, the shares not only contain the secret information, but are also

themselves meaningful halftone images. Secret images can still be decoded

when qualified shares are stacked together. Shares in an EVC scheme,

however, provide very low quality visual information and suffer from low

contrast between per graph black and white pixels. Then extended the EVC

approach to natural gray scale images to improve the image quality.

Generated halftone shares that carry visual information by using joint

visual cryptography and watermarking to generate meaningful halftone

images by using threshold arrays .The major shortcomings with these

methods are that either the security property is not strictly guaranteed or the

decoded image is not immune to the interference from the share images.

To produce halftone shares that can carry signification visual

information halftone visual cryptography (HVC) based on the principle of

void and cluster dithering .Compared with EVC, the image quality of the

25

shares is greatly improved and the reconstructed image contains much less

cross interference from the share images.

The HVC method inserts the pixels carrying secret information into

preexisting encoded halftone shares. While this method is effective at

generating shares with pleasing visual results, it is computationally

expensive. Moreover, using the void and cluster algorithm to choose the

pixels to be modified may make the positions of the modified pixels

dependent on the content of the original halftone image. As a result, the

reconstructed image may bear residual features of the original halftone

images. The method has the disadvantage that a pair of complimentary shares

is required to prevent the share visual information from showing on the

decoded image.

To further improve the image quality of the halftone shares and

completely remove the cross interference of the share images on the

reconstructed image, novel HVC construction methods are proposed in this

paper. Instead of modifying halftone images to encode secret information, it

is shown that it is viable to concurrently halftone gray scale images and

encodes the secret information into the halftone images.

In the proposed HVC methods, pixels that carry the secret image

information are predetermined before a halftone share is generated. These

pixels are then naturally embedded into the halftone shares when the gray

scale images are halftone. Error diffusion, a simple and widely used halftone

method that yields a good compromise between the image quality and the

computational complexity is employed in this paper as the underlying half

26

toning algorithm. Error diffusion then halftones the input gray scale images

but does not change the predetermined pixels.

When half toning a gray scale image, error diffusion diffuses away the

quantization error into the neighboring gray scale pixels so that a visually

pleasing halftone image is obtained. In our approach, encoding of the secret

information imposes extra constraints on the error diffusion. However, the

additional quantization error introduced by the encoding of the secret

information is diffused away by error diffusion to the neighboring gray scale

pixels. Thus, visually pleasing halftone shares can still be obtained.

Three methods are developed to make the reconstructed image immune

to the interference from the share images. The first method employs a

complementary halftone image pair. The second method deliberately

introduces homogeneously distributed black pixels into each share, which has

the advantage that complementary image pairs are not needed. The third

method exploits the fact that the half toning of the gray scale images alone

may generate a sufficient number of black pixels to satisfy the contrast

condition of image decoding.

A black pixel is deliberately introduced only when a sufficient number

of black pixels have not yet been produced. Thus, complementary shares are

also not required. With fewer constraints on error diffusion, the third method

has the potential to obtain shares showing natural images with fine details.

27

CHAPTER 6

SYSTEM DESIGN

6.1 System Design

Design is concerned with identifying system components specifying

relationship upon components. Specifying system structure and providing

architecture for development files. Modularity is one of the desirable

properties of the large systems. It implies that system is divided into several

parts in such a manner the interaction between parts is minimal clearly

specified, this wing ill help the implementation of the system. Moreover, this

will guide the future changes in the system to satisfy the future requirements.

This system design can be subdivided into as follows

Input design

Output design

Code design

6.2 Algorithm

28

6.2.1 Error Algorithm

Error diffusion algorithms that is used in many computer graphics and

image processing programs. Error diffusion is a technique for displaying

continuous tone in digital images on devices that has limited colors (tone)

range. Printing an 8 bit grey scale image to a black and white printer is

problematic. The printer, being a bi-level device, can’t print the 8 bit image

natively. It must stimulate multiple shares of grey by using an approximation

technique. The basic error diffusion algorithm does its work in the following

base.

1) Determine the output value given the input value of the current

result this often uses quantization or in binary case thresholding.

2) For an 8 bit grey scale image that is displayed on a 1 bit output

device, all input devices in range [0,127] are to be displayed as 0.

3) Input value between [128,255] is to be displayed as a 1 on the

output device.

4) Once the output values are determined, the code computes the error

between what should be displayed on the output device and what is actually

displayed.

5) Assume the input pixel value 168 given that is greater than our

threshold value(128) it determine that output value will be a 1, this value is

stored in the output array.

29

6) To compute the error, the program must normalize output first, so it

is in the same scale as input value. Finally the error value is distributed on a

fractional basis to neighboring pixel in the region.

6.2.2 Analysis of the ED Algorithm

At first glance, one might think that error diffusion algorithm is an

inherently serial process. The conventional approach distributes error to

neighboring pixels as they are computed. As a result, the previous pixels

error must be known in order to compute the values of the next pixels. This

interdependency implies that the code can only process one pixel at a time.

It’s not that difficult, however, to approach this problem in a way that is more

suitable to a multithreaded approach.

6.2.3 Parallel ED

To transform the conventional error diffusion algorithm into an

approach that is more conducive to a parallel solution, consider the different

decomposition that was covered previously. Given that a pixel may not be

processed until its spatial predecessors have been processed, the problem

appears to lend itself to an approach where we have a producer-or in this

case, multiple producers-uses to compute the proper output pixels.

30

The flow of error data to the current pixel is critical. Therefore, the

problem seems to break down into data-flow decomposition. Now that we

identified the approach, the next step is to determine the best pattern that can

be applied to this particular problem. Each independent thread of execution

should process an equal amount of work.

One way, based on the algorithm presented in the previous section,

would be to have a thread that processed the even pixels in a given row, and

another thread that processed the odd pixels in the same row. This approach

is ineffective however; each thread will be blocked waiting for the other to

complete, and the performance could be worse than in the sequential case.

To effectively could the work among threads, we need a way to reduce

the dependency between pixels. Thus once these pixels immediately to the

left on the current rows. Thus, once these pixels are processed, the current

pixels may complete its processing. This ordering suggests an

implementation where each thread processes a row of data. Once a row has

completed processing of first few pixels, the thread responsible for the next

row may begin its processing.

Notice that a small latency occurs at the start of each row. This latency

is due to the fact that the previous row’s error data must be calculated before

the current row can be processed. These types of latency are generally

unavoidable in producer-consumer implementation; however you can

minimize the impact of the latency. The trick is to derive the proper workload

partitioning so that each thread of execution works as efficiently as possible.

In this case you incur two-pixels latency before processing of the next thread

can begin. An 8.5” * 11” per row. The two-pixel latency is insignificant here.

31

6.3 Phase Description

PHASE TASK DESCRIPTION

Phase 1 AnalysisAnalyze the information given in the IEEE paper.

Phase 2 Literature surveyCollect raw data and elaborate on literature surveys.

Phase 3 DesignAssign the module and design the process flow control

Phase 4 ImplementationImplement the code for all the modules and integrate all the modules

Phase 5 Documentation

Prepare the document for this project with conclusion and future enhancement

6.4 Dataflow Diagram

32

Dataflow is the movement of data in a system from

point of origin to a specified destination indicated by lines or

arrows. Dataflow diagram is the graphical representation of

data movement process and files used in support of

information system.

The dataflow diagram is one of the most important

tools used by system analysis. One of the important feature

of DFD’s is that it is logical rather than physical. The

elements of the system do not depend on vendor or

hardware. They specify in precise, concise, manner the

working of the system and how it hangs together.

Unlike detail flow chart dataflow diagram not supplied

detailed description of the module but graphically describes a

systems data and how the data interact with the system.

33

SIRename to HVC

Halftoning process

Encrypted image

Generation of halftone

Encryption

DFD diagram for generating halftoningCHAPTER 7

SYSTEM TESTING

7.1 Software Testing

Testing is the process of executing a program with the

intent of finding an error. A successful test in one that uncovers on as yet

undiscovered errors.

Software testing is a critical element of software

quality assurance and represents the ultimate review of specification, design

coding, testing is a process of executing a program with intent of finding an

error

The objective of software testing is to uncover errors.

To fulfill this objective, a series of test steps-unit, integration, validation and

system test are planned and incorporation of module into program.

34

DecryptionDecrypted image

Testing is the integral part of the software process and

activity that must be carried put throughput the life cycle. Is testing is

conducted successfully will uncover errors on the software. As the benefit

testing demonstrates that the software function appears to be working

according to specification and performance requirements appear to have been

met.

Here the term verification and validation refers to the

process of determining where a phase has been correctly carried out and

intensive evolution process that place just before the product is delivery to the

customer.

7.2 Types of Testing

7.2.1 Unit Testing

Unit testing focuses verification effort on the smallest unit of the

software design. In this testing, each module is tested to be working

satisfactorily as the expected output from the module.

Test Cases

Our project consists of three modules. All the modules were unit

tested. And the three units are

Haftoning generation

Encryption

Decryption

Unit 1

Input : The secret image has to insert into its

corresponding location.

Output : The halftone is generated.

35

Unit 2

Input : The Halftoning secret image

Output : Encrypted image

Unit 3

Input : The Encrypted secret image

Output : Original image

7.2.2 Integration Testing

All the three modules are integrated and tested. The data

transfers among various modules are verified so that create no bugs

and function as excepted.

Test Cases

Input : The Halftoning process and then the encryption

and the decryption of secret images takes place.

Output : Encrypted and Decrypted secret images are

displayed

7.2.3 Validation Testing36

Validation testing is used to demonstrate that the software

functions in a manner that can be reasonably expected by the

customer.

Test Cases

(i) Input : The secret image must be correctly renamed.

(ii) Output : The secret image is successfully halftoned.

(i) Input : The secret image is not renamed.

(ii) Output : The image is not halftoned.

37

CHAPTER 8

IMPLEMENTATION AND RESULTS

8.1 Module Description:

There are three main modules like

1. Halftone

2. Secret Image Transmission

3. Receiving of Secret Image

The Explanation for both the modules are described below.

8.1.1 Halftone

Halftone is the reprographic technique that simulates continuous

tone imagery through the use of dots, varying either in size, in shape or in

spacing.

Where continuous tone imagery contains an infinite range of

colors or greys, the halftone process reduces visual reproductions to a binary

image that is printed with only one color of ink. This binary reproduction 38

relies on a basic optical illusion—that these tiny halftone dots are blended

into smooth tones by the human eye. At a microscopic level, developed black

and white photographic film also consists of only two colors, and not an

infinite range of continuous tones.

Halftoning is also commonly used for printing color pictures.

The general idea is the same, by varying the density of the four primary

printing colors, cyan, magenta, yellow and black any particular shade can be

reproduced. Error diffusion is a type of halftoning in which residual is

distributed to neighboring pixels that have not yet been processed. the

quantization Its main use is to convert a multi-level image into a binary

image, though it has other applications. Unlike many other halftoning

methods, error diffusion is classified as an area operation, because what the

algorithm does at one location influences what happens at other locations.

This means buffering is required, and complicates parallel processing. Point

operations, such as ordered dither, do not have these complications. Error

Diffusion has the tendency to enhance edges in an image.

After the distribution of SIPs is generated, the next

step is to assign the values to all the SIPs. This procedure

only depends on the underlying VC scheme. Under the k-out-

of-n VC scheme, the basis matrices S0 and S1 are

constructed first. Then we construct the matrix collections C0

and C1from the basis matrices

39

To produce the halftone share , a greyscale image

is provided. Let f(m,n) be the (m,n) pixel of the grayscale

image, then the input to the threshold block is

The generation of the complementary share in

method 1 is as

follows. The SIPs in the complementary share are decided by

the VSS scheme, as in other shares, but the non-SIPs are

obtained by reversing the non-SIPs in a primary share. The

resultant share is still a structured halftone.

8.1.2 Secret Image Transmission

This is the main page for the transmission for the secret image.

After transmission in the secret image the following process is to do. Click

the error diffusion command button the picture is resized to 512*512. If the

error diffusion button is clicked the resized image is converted to halftone

shares using error diffusion method. In this process the image is encrypted

and it has to save in the Location D:\HVC and this to be send. The browse

command button is clicked, and then the output image is click to send.

40

BEFORE ERROR DIFFUSION FOR ERROR DIFFUSION ENCRYPT THE SECRET IMAGE

8.1.3 Receiving of Secret Image

The image that is to be sent will be received here. The image is

displayed when the output button is clicked. The image that send to be

received here,this form is used to view the output image.

8.2 Result

8.2.1 Screen Shots

8.2.1 Transmission side form

41

This is the main page for the transmission for the secret image. After transmission in the secret image the following process is to do.

BEFORE ERROR DIFFUSION FOR ERROR DIFFUSION ENCRYPT THE SECRET IMAGE

After this process send the secret image.

8.2.2 Reception side form

42

The image that is to be sent will be received here. The image is

displayed when the output button is clicked.

8.2.3 Before error diffusion

43

Click the error diffusion command button the picture is resized

to 512*512.

8.2.4 Error diffusion

44

If the error diffusion button is clicked the resized image is

converted to halftone shares using error diffusion method.

8.2.5 Encrypt the secret image

45

In this process the image is encrypted and it have to save in the

Location D:\HVC and this to be send.

8.2.6 Sending of the image

46

The browse command button is clicked, then the output image is click

to send.

8.2.7 Receiving of output image47

The image that send to be received here,this form is used to view

the output image.

48

8.2.8View of the secret image

Finally the output image is displayed and it stored in the

Location D:\HVC

49

CHAPTER 9

CONCLUSION AND FUTURE ENHANCEMENT

9.1 Conclusion

This method is better to send the image in very secure manner,

because it based on error diffusion that can generate shares taking pleasing

visual information.

Error diffusion is used to construct the shares such

that the noise introduced by the preset pixels is diffused

away when halftone shares are generated.

The homogeneous and isotropic distribution of the

preset pixels imposes the least noise in the error diffusion,

thus leading to shares with high image quality.

9.2 Future Enhancement

We are going to implement in the military in order to provide security

in sharing of the maps.

We are also going to implement in the color image.

50

REFERENCE

�

� [1] M. Naor and A. Shamir, (1995) ,‘Visual cryptography,’ Advances in

� cryptography: EUROCRYPT’94, LNCS, vol. 950, pp. 1–12.

�

� [2] Z. Zhou, G. R. Arce, and G. Di Crescenzo, (Aug. 2006), ‘Halftone visual

cryptography,’ IEEE Trans. Image Process., vol. 15, no. 8, pp. 2441–2453,

�

� [3] L. A. MacPherson, (2002), ‘Grey level visual cryptography for general

access structures,’ M.S. thesis, University of Waterloo, Ontario, Canada.

�

� [4] T. C. Chang and J. P. Allebach, (Nov.2003) ‘Memory efficient error

diffusion,’ IEEE Trans. Image Process., vol. 12, no. 11, pp. 1352–1366.

51

52