An Improved Visual Cryptography Scheme for Secret Hiding

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(IJCSIS) International Journal of Computer Science and Information Security,Vol. 9, No. 3, March 2011

AN IMPROVED VISUAL CRYPTOGRAPHY SCHEME

FOR SECRET HIDING G.Prasanna Lakshmi

Computer Science,IBSAR

Karjat,India

[email protected]

Dr. D.A.Chandulal

Professor and HOD, IBSAR

Computer Science

India

[email protected]

Dr.KTV Reddy )

Professor & Principal

Electronics & Telecommunications Dept.

Computer Science

India

[email protected]

Abstract — Cryptography is the practice and study

of hiding information. Cryptography, then, not only protects

data from theft or alteration, but can also be used for user

authentication. There are, in general, three types of

cryptographic schemes typically used to accomplish these

goals: secret key (or symmetric) cryptography, public-key (or

asymmetric) cryptography, and hash functions. In all cases,

the initial unencrypted data is referred to as plaintext . It is

encrypted into ciphertext , which will in turn (usually) be

decrypted into usable plaintext.

Visual Cryptography is a type of cryptography which

encodes a number of images in the way that when the images

on transparencies are stacked together, the hidden messageappears without a trace of original images. The decryption is

done directly by the human visual system with no special

cryptographic calculations. This project presents a system

which takes three pictures as an input and generates two

images which correspond to two of the three input pictures.

The third picture is reconstructed by printing the two output

images onto transparencies and stacking them together.

While the previous researches basically handle only

binary images, this project establishes the extended visual

cryptography scheme suitable for natural images. Generally,

visual cryptography suffers from the deterioration of the image

quality. This project also describes the method to improve the

quality of the output images. The trade-off between the imagequality and the security are discussed and assessed by

observing the actual results of this method. Furthermore, the

optimization of the image quality is discussed.

Keywords- Visual Cryptography, Digital Image, Mat lab

INTRODUCTION

Image:

A digital image is a computer file that

contains graphical information instead of text or a program.

Pixels are the basic building blocks of all digital images.

Pixels are small adjoining squares in a matrix across the length

and width of your digital image. They are so small that you

don’t see the actual pixels when the image is on your

computer monitor.Pixels are monochromatic. Each pixel is a single solid

color that is blended from some combination of the 3 primary

colors of Red, Green, and Blue. So, every pixel has a RED

component, a GREEN component and BLUE component. The

physical dimensions of a digital image are measured in pixels

and commonly called pixel or image resolution. Pixels are

scalable to different physical sizes on your computer monitor

or on a photo print. However, all of the pixels in any particular

digital image are the same size. Pixels as represented in a

printed photo become round slightly overlapping dots.

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Pixel Values: As shown in this bitonal image, each

pixel is assigned a tonal value, in this example 0 for black and1 for white.

PIXEL DIMENSIONS are the horizontal and

vertical measurements of an image expressed in pixels. The

pixel dimensions may be determined by multiplying both the

width and the height by the dpi. A digital camera will also

have pixel dimensions, expressed as the number of pixels

horizontally and vertically that define its resolution (e.g.,

2,048 by 3,072). Calculate the dpi achieved by dividing a

document's dimension into the corresponding pixel dimension

against which it is aligned.

Example:

Fig: An 8" x 10" document that

is scanned at 300 dpi has the pixel dimensions of 2,400 pixels

(8" x 300 dpi) by 3,000 pixels (10" x 300 dpi).

Images in MATLAB:

The basic data structure in MATLAB is the array, an

ordered set of real or complex elements. This object is

naturally suited to the representation of images, real-valued

ordered sets of color or intensity data.

MATLAB stores most images as two-dimensionalarrays (i.e., matrices), in which each element of the matrix

corresponds to a single pixel in the displayed image. (Pixel is

derived from picture element and usually denotes a single dot

on a computer display.)

For example, an image composed of 200 rows and

300 columns of different colored dots would be stored in

MATLAB as a 200-by-300 matrix. Some images, such as

color images, require a three-dimensional array, where the first

plane in the third dimension represents the red pixel

intensities, the second plane represents the green pixel

intensities, and the third plane represents the blue pixel

intensities. This convention makes working with images in

MATLAB similar to working with any other type of matrix

data, and makes the full power of MATLAB available for

image processing applications.

IMAGE REPRESENTATION

An image is stored as a matrix using standard Mat lab matrix

conventions. There are four basic types of images supported

by Mat lab:

1. Binary images

2. Intensity images

3. RGB images

4. Indexed images

Binary Images:

In a binary image, each pixel assumes one of only two discrete

values: 1 or 0. A binary image is stored as a logical array. By

convention, this documentation uses the variable name BW to

refer to binary images.

The following figure shows a binary image with a close-up

view of some of the pixel values.

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Fig: Pixel Values in a Binary Image

Grayscale Images:

A grayscale image (also called gray-scale, gray scale,

or gray-level) is a data matrix whose values represent

intensities within some range. MATLAB stores a grayscale

image as an individual matrix, with each element of the matrix

corresponding to one image pixel. By convention, this

documentation uses the variable name I to refer to grayscale

images.

The matrix can be of class uint8, uint16, int16, single,

or double. While grayscale images are rarely saved with a

color map, MATLAB uses a color map to display them.

For a matrix of class single or double, using the

default grayscale color map, the intensity 0 represents black and the intensity 1 represents white. For a matrix of type uint8,

uint16, or int16, the intensity intmin (class (I)) represents

black and the intensity intmax (class (I)) represents white.

The figure below depicts a grayscale image of class double.

Fig: Pixel Values in a Grayscale Image Define Gray Levels

1) Color Images:

A color image is an image in which each pixel is

specified by three values — one each for the red, blue, and

green components of the pixel's color. MATLAB store color

images as an m-by-n-by-3 data array that defines red, green,

and blue color components for each individual pixel. Color

images do not use a color map. The color of each pixel is

determined by the combination of the red, green, and blueintensities stored in each color plane at the pixel's location.

Graphics file formats store color images as 24-bit

images, where the red, green, and blue components are 8 bits

each. This yields a potential of 16 million colors. The

precision with which a real-life image can be replicated has

led to the commonly used term color image.

A color array can be of class uint8, uint16,

single, or double. In a color array of class single or

double, each color component is a value between 0 and 1. A

pixel whose color components are (0, 0, 0) is displayed as

black, and a pixel whose color components are (1, 1, 1) isdisplayed as white. The three color components for each pixel

are stored along the third dimension of the data array. For example, the red, green, and blue color components

of the pixel (10,5) are stored in RGB(10,5,1),

RGB(10,5,2), and RGB(10,5,3),

respectively.


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The following figure depicts a color image of class

double.

Fig: Color Planes of a True color Image

2) Indexed Images:

An indexed image consists of an array and a

colormap matrix. The pixel values in the array are direct

indices into a colormap. By convention, this documentation

uses the variable name X to refer to the array and map to refer

to the colormap.

The colormap matrix is an m-by-3 array of class

double containing floating-point values in the range [0, 1].

Each row of map specifies the red, green, and blue

components of a single color. An indexed image uses direct

mapping of pixel values to colormap values. The color of each

image pixel is determined by using the corresponding value of

X as an index into map.

A colormap is often stored with an indexed image

and is automatically loaded with the image when you use the

imread function. After you read the image and the color map

into the MATLAB workspace as separate variables, you must

keep track of the association between the image and color map.

However, you are not limited to using the default color map--you can use any color map that you choose.

The relationship between the values in the image

matrix and the color map depends on the class of the image

matrix. If the image matrix is of class single or double, it

normally contains integer values 1 through p, where p is the

length of the color map. The value 1 points to the first row in

the color map, the value 2 points to the second row, and so on.

If the image matrix is of class logical, uint8 or uint16,

the value 0 points to the first row in the color map, the value 1

points to the second row, and so on.

The following figure illustrates the structure of an indexedimage. In the figure, the image matrix is of class double, so

the value 5 points to the fifth row of the color map.

Fig: Pixel Values Index to Color map Entries in

Indexed Images


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Digital Image File Types:

The 5 most common digital image file types are as follows:

1. JPEG is a compressed file format that supports 24 bit color

(millions of colors). This is the best format for photographs to

be shown on the web or as email attachments. This is because

the color informational bits in the computer file arecompressed (reduced) and download times are minimized.

2. GIF is an uncompressed file format that supports only 256

distinct colors. Best used with web clip art and logo type

images. GIF is not suitable for photographs because of its

limited color support.

3. TIFF is an uncompressed file format with 24 or 48 bit

color support. Uncompressed means that all of the color

information from your scanner or digital camera for each

individual pixel is preserved when you save as TIFF. TIFF is

the best format for saving digital images that you will want to

print. Tiff supports embedded file information, including exact

color space, output profile information and EXIF data. Thereis a lossless compression for TIFF called LZW. LZW is much

like 'zipping' the image file because there is no quality loss.

An LZW TIFF decompresses (opens) with all of the original

pixel information unaltered.

4. BMP is a Windows (only) operating system uncompressed

file format that supports 24 bit color. BMP does not support

embedded information like EXIF, calibrated color space and

output profiles. Avoid using BMP for photographs because it

produces approximately the same file sizes as TIFF without

any of the advantages of TIFF.

5. Camera RAW is a lossless compressed file format that is

proprietary for each digital camera manufacturer and model. A

camera RAW file contains the 'raw' data from the camera's

imaging sensor. Some image editing programs have their own

version of RAW too. However, camera RAW is the most

common type of RAW file. The advantage of camera RAW is

that it contains the full range of color information from the

sensor. This means the RAW file contains 12 to 14 bits of

color information for each pixel. If you shoot JPEG, you only

get 8 bits of color for each pixel. These extra color bits make

shooting camera RAW much like shooting negative film. You

have a little more latitude in setting your exposure and a

slightly wider dynamic range.

Image Coordinate Systems:3) Pixel Coordinates

Generally, the most convenient method for

expressing locations in an image is to use pixel coordinates. In

this coordinate system, the image is treated as a grid of

discrete elements, ordered from top to bottom and left to right,

as illustrated by the following figure.

Fig: The Pixel Coordinate System

For pixel coordinates, the first component r (the row)

increases downward, while the second component c (the

column) increases to the right. Pixel coordinates are integer

values and range between 1 and the length of the row or

column.

There is a one-to-one correspondence between pixel

coordinates and the coordinates MATLAB uses for matrixsubscripting. This correspondence makes the relationship

between an image's data matrix and the way the image is

displayed easy to understand. For example, the data for the

pixel in the fifth row, second column is stored in the matrix

element (5, 2). You use normal MATLAB matrix subscripting

to access values of individual pixels.

For example, the MATLAB code

I (2, 15)

Returns the value of the pixel at row 2, column 15 of the

image I.

4) Spatial Coordinates:

In the pixel coordinate system, a pixel is treated as a

discrete unit, uniquely identified by a single coordinate pair,

such as (5, 2). From this perspective, a location such as (5.3,

2.2) is not meaningful.

At times, however, it is useful to think of a pixel as a

square patch. From this perspective, a location such as (5.3,

2.2) is meaningful, and is distinct from (5, 2). In this spatialcoordinate system, locations in an image are positions on a

plane, and they are described in terms of x and y (not r and c

as in the pixel coordinate system).

The following figure illustrates the spatial coordinate system

used for images. Notice that y increases downward.


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Fig: The Spatial Coordinate System

This spatial coordinate system corresponds closely to

the pixel coordinate system in many ways. For example, the

spatial coordinates of the center point of any pixel are identicalto the pixel coordinates for that pixel.

There are some important differences, however. In

pixel coordinates, the upper left corner of an image is (1,1),

while in spatial coordinates, this location by default is

(0.5,0.5). This difference is due to the pixel coordinate

system's being discrete, while the spatial coordinate system is

continuous. Also, the upper left corner is always (1,1) in pixel

coordinates, but you can specify a non default origin for the

spatial coordinate system.

Another potentially confusing difference is largely a

matter of convention: the order of the horizontal and verticalcomponents is reversed in the notation for these two systems.

As mentioned earlier, pixel coordinates are expressed as (r, c),

while spatial coordinates are expressed as (x, y). In the

reference pages, when the syntax for a function uses r and c, it

refers to the pixel coordinate system. When the syntax uses x

and y, it refers to the spatial coordinate system.

Digital image processing:

Digital image processing is the use of computer

algorithms to perform image processing on digital images. As

a subfield of digital signal processing, digital image

processing has many advantages over analog image

processing; it allows a much wider range of algorithms to be

applied to the input data, and can avoid problems such as the

build-up of noise and signal distortion during processing.

IMAGE DIGITIZATION:

An image captured by a sensor is expressed as a

continuous function f(x,y) of two co-ordinates in the plane.

Image digitization means that the function f(x,y) is sampled

into a matrix with M rows and N columns. The image

quantization assigns to each continuous sample an integer

value. The continuous range of the image function f(x,y) is

split into K intervals. The finer the sampling (i.e., the larger M

and N) and quantization (the larger K) the better the

approximation of the continuous image function f(x,y).

IMAGE PRE-PROCESSING:Pre-processing is a common name for operations with

images at the lowest level of abstraction -- both input and

output are intensity images. These iconic images are of the

same kind as the original data captured by the sensor, with an

intensity image usually represented by a matrix of image

function values (brightness). The aim of pre-processing is an

improvement of the image data that suppresses unwanted

distortions or enhances some image features important for

further processing. Four categories of image pre-processing

methods according to the size of the pixel neighborhood that is

used for the calculation of new pixel brightness:

o Pixel brightness transformations.

o Geometric transformations.

o Pre-processing methods that use a local

neighborhood of the processed pixel.

o Image restoration that requires knowledge

about the entire image.

Image Segmentation:

Image segmentation is one of the most important stepsleading to the analysis of processed image data. Its main goal

is to divide an image into parts that have a strong correlation

with objects or areas of the real world contained in the image.

Two kinds of segmentation

1. Complete segmentation: This results in set of

disjoint regions uniquely corresponding with

objects in the input image. Cooperation with

higher processing levels which use specific

knowledge of the problem domain is necessary.

2. Partial segmentation: in which regions do not

correspond directly with image objects. Image is

divided into separate regions that are

homogeneous with respect to a chosen property

such as brightness, color, reflectivity, texture, etc.

In a complex scene, a set of possibly overlapping

homogeneous regions may result. The partially

segmented image must then be subjected to further

processing, and the final image segmentation may

be found with the help of higher level information.


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Segmentation methods can be divided into three groups

according to the dominant features they employ

1. First is global knowledge about an image or

its part; the knowledge is usually

represented by a histogram of image

features.

2. Edge-based segmentations form the secondgroup; and

3. Region-based segmentations

II. IMAGE ENHANCEMENT

The aim of image enhancement is to improve the

interpretability or perception of information in images for

human viewers, or to provide `better' input for other automated

image processing techniques. Image enhancement techniques

can be divided into two broad categories:

1. Spatial domain methods, which operate directly on pixels,and

2. Frequency domain methods, which operate on the Fourier

transform of an image.

Unfortunately, there is no general theory for determining what

`good’ image enhancement is when it comes to human

perception. If it looks good, it is good! However, when image

enhancement techniques are used as pre-processing tools for

other image processing techniques, then quantitative measures

can determine which techniques are most appropriate.

VISUAL CRYPTOGRAPHY

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.

Visual cryptography was pioneered by Moni Naor

and Adi Shamir in 1994. They 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.

Using a similar idea, transparencies can be used to implement

a one-time pad encryption, where one transparency is a shared

random pad, and another transparency acts as the cipher text

Differences between Cryptography & Steganography:

Cryptography and steganography are well known and

widely used techniques that manipulate information

(messages) in order to cipher or hide their existence. These

techniques have many applications in computer science and

other related fields: they are used to protect e-mail messages,

credit card information, corporate data, etc. More specifically,

steganography is the art and science of communicating in a

way which hides the existence of the communication (Johnsonand Jajodia, 1998). A steganographic system thus embeds

hidden content in unremarkable cover media so as not to

arouse an eavesdropper’s suspicion. As an example, it is

possible to embed a text inside an image or an audio file. On

the other hand, cryptography is the study of mathematical

techniques related to aspects of information security such as

confidentiality, data integrity, entity authentication, and data

origin authentication. In this project we will focus only on

confidentiality, i.e., the service used to keep the content of

information from all but those authorized to have it.

Cryptography protects information by transforming it intoan unreadable format. It is useful to achieve confidential

transmission over a public network. The original text, or

plaintext , is converted into a coded equivalent called cipher

text via an encryption algorithm. Only those who possess a

secret key can decipher (decrypt ) the ciphertext into plaintext.

Cryptography systems can be broadly classified into

symmetric-key systems that use a single key (i.e., a password )

that both the sender and the receiver have, and public-key

systems that use two keys, a public key known to everyone

and a private key that only the recipient of messages uses.

The purpose of both is to provide secret communication.

Cryptography hides the contents of the message from an

attacker, but not the existence of the message.Steganography/watermarking even hide the very existence of

the message in the communicating data. Consequently, the

concept of breaking the system is different for cryptosystems

and stego systems (watermarking systems).

• A cryptographic system is broken when the attacker

can read the secrete message.

• Breaking of a steganographic/watermarking system

has two stages:

- The attacker can detect that

Steganography/watermarking has been used;

- The attacker is able to read, modify or remove the

hidden message.

A steganography/watermarking system is considered

as insecure already if the detection of

steganography/watermarking is possible.


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Extended Visual Cryptography:

Extended Visual Cryptography is a type of

cryptography which encodes a number of images in the way

that when the images on transparencies are stacked together,

the hidden message appears without a trace of original images.

The decryption is done directly by the human visual system

with no special cryptographic calculations. Generally, visualcryptography suffers from the deterioration of the image

quality. This project also describes the method to improve the

quality of the output images. The trade-off between the image

quality and the security are discussed and assessed by

observing the actual results of this method. Furthermore, the

optimization of the image quality is discussed.

INTRODUCTION

Visual cryptography is a kind of cryptography that can bedecoded directly by the human visual system without any

special calculation for decryption. As shown in below figure,

our visual cryptography system takes three pictures as an input

and generates two images which correspond to two of the

three input pictures. The third picture is reconstructed by

printing the two output images onto transparencies and

stacking them together. This type of visual cryptography,

which reconstructs the image by stacking some meaningful

images together, is especially called Extended Visual

Cryptography. In this project, the pictures shown on the output

images are called sheets and the resulting image reconstructed

by stacking the two sheets together is called the target .

Previous works on the extended visual cryptography deal with

binary images such as text images, but natural images such as

photographs are difficult to handle in such scheme. This

project establishes the extended visual cryptography scheme

for natural images. Generally, visual cryptography suffers

from the deterioration of the image quality. This project also

describes the method to improve the quality of the output

image.

Visual Secret Sharing Scheme:

The basic model of the visual cryptography consists

of a several number of transparency sheets. On each

transparency a cipher text is printed which is indistinguishable

from random noise. The hidden message is reconstructed bystacking a certain number of the transparencies and viewing

them. The system can be used by anyone without any

knowledge of cryptography and without performing any

cryptographic computations. Naor and Shamir have developed

the Visual Secret Sharing Scheme (VSSS) to implement this

model. In k out of n VSSS (which is also called (k, n) scheme),

a binary image (picture or text) is transformed into n sheets of

transparencies of random images. The original image becomes

visible when any k sheets of the n transparencies are put

together, but any combination of less than k sheets cannot

reveal the original binary image.

In the scheme, one pixel of the original image is

reproduced by m sub pixels on the sheets. The pixel is

considered “on” (transparent) if the number of transparent sub

pixels is more than a constant threshold, and “off” if the

transparent sub pixels is less than a constant lower threshold,

when the sheets are stacked together. The contrast α is thedifference between the on and off threshold number of

transparent pixels. Ateniese et al. has extended the (k, n) VSSS

to general access structures where senders can specify all

qualified and forbidden subsets of n participants. Droste

considered the problem of sharing more than one secret image

among a set of participants and proposed a method to

reconstruct different images with different Combination of

sheets.

Extended Visual Cryptography:

Naor and Shamir have mentioned an extension of the

model which conceals the very existence of the secret

message. That is, each sheet carries some meaningful imagesrather than random dots. They referred to the (2 , 2) example

with the number of sub pixels m = 4. Ateniese has formalized

this framework as the Extended Visual Cryptography and

developed a Scheme for general access structures

[Ateni01].They also discuss the trade-off between the contrast

of the each images on the sheets and that of the resulting

image when stacked together in(k, k ) cases.


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Application to the Grayscale and Color

Images:

A few researches have discussed the visual

cryptography for grayscale and color images. Naor and Shamir

mentioned the extension of their scheme to grayscale images.That is, to represent the gray levels of the hidden image by

controlling the way how the opaque sub pixels of the sheets

are stacked together. The grayscale version of the visual

cryptography is fundamentally proposed in the project. There

are some researches that deal with color images. Naor and

Shamir discussed the visual cryptography Scheme which

reconstructs a message with two colors, by arranging the

colored or transparent sub pixels [Naor96]. Koga et al. devised

a lattice-based (k, n) scheme [Koga98]. The approach by

Verheul and van Tilborg [E.R.V97] is basically similar to

Koga’s. Both approaches assign a color to a sub pixel at a

certain position, which means that displaying m colors uses

m−1 Sub pixels. The resulting pixels contain one colored sub

pixel and the rest of the sub pixels are black.

Therefore the more colors are used; the worse the

contrast of the images becomes significantly. Their approaches

cannot be applied to the extended visual cryptography, either.

Rijmen and Preneel talked about enabling multicolor with

relatively less sub pixels (24 colors with m= 4) [Rijme96].

However each sheet must contain color random images, which

means applying this approach to the extended visual

cryptography is impossible. This project focuses on the (2 , 2)

scheme and discusses the method to deal with the natural

images with intermediate gray levels. It also shows how to

enhance the contrast.

About this Program:This program can encode the text images (6 images

max) in a single color image. And also can easily recover the

texts already being hided. It is very simple one, but very useful

Major advantage of this encoded image is like a original image

as before encoding. The image has no visible changes. So no

one can easily read the hidden texts by sight. The decoding

logic only can recover the texts. Thus these special features of

encoded image make it used to save the confidential data. Also

can used for web applications, mail services, media etc.,.

Tips to use program:

• For better results images should be in 24-Bit bit map

(bmp) format.

• Use larger size image (> 512 X 512)

• Avoid small size Fonts.

• All images should be in the same size.

• Text data should be converted to image format (bmp)

before execution.

• Text data should be monochrome (Black letters)

,color text leads to undesired results.

• Change the address of the image mentioned in this

program as per your location in Local drive.

• No need to specify the dimension of the image.

Initialize the Input

In this part obtained the input (1 Color image + 6 Text

images) from local drive. Collect all images as a separate

variable. Change the location of the image mentioned in

imread () function as per your image.

Part Two: Image Encoding

In this part the desire text images (max 6 images) are

going to encoded with the Color image. At first pair of textimages are combined into a single image. Simple logic is used

to achieve this. Pixels are selected from alternate location of

pair of text images. As a result of this, image having details

(but alternate locations) about pair of two text images. Further

explained that if a location (n,m) have the pixel of Image 1

next location (n+1,m+1) have the pixel taken from Image 2.

This task is done by alterim() function. Two arguments of this

function are two text images.

Next step is to encode the text image which was

already obtained from the result of alterim() function. We

obtained 3 single text images from this function. Each image

is encoded in each color frames R, G and B respectively. To

do this I used a simple logic. i.e. encode the text image as a bit

of color image LSB. Because LSB bit is very less significant,

so changing this bit won't affect the entire image. Further the

text image is having only black and white area. So I fix a

threshold as 128. If a pixel value of a text image is >128, LSB

of Color image is set as '1' otherwise '0'. Simply says if text is

present set that LSB as 1 otherwise 0. Similar way I do it for

other two color frames. All the above operations are

performed by imhide () function for each frame respectively.

Arguments of this function are a color frame and a text image

obtained from alterim() function. Finally each frames are

added to form a Encoded Water mark image.

NOTE: In bmp format each color frame bits are represented by8-bits. We took the LSB of that 8-bit.

Part Three: Recovery of Text image

In this part two functions are used to recover the text

image from Encoded image. First step is to extract the text

image. Remember that text image encoded in the color image

in each frame is the alternate combination of two images


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(Text). The function txtxtract () just extract the text image

which is the combination of two text images, not by separate.

As we encode our data in LSB of each color frame that bit

alone sufficient to reconstruct our text image. So logical AND

operation is used to recover the LSB. Reverse operation of

function imhide () is used to reconstruct the text. Next step is

to separate the pair of two text images from the result of

function txtxtract (). Alternate selection used in previous

Encoding section is used again for all pair of images.

Part Four: Edge tappering

This part is the final step in my program. The image

obtained from previous step is not a tappered. So finally all

text images are smoothen by using fspecial() function

available in MATLAB.

INTRODUCTION TO MATLAB

What Is MATLAB?

MATLAB® is a high-performance language for

technical computing. It integrates computation, visualization,

and programming in an easy-to-use environment where

problems and solutions are expressed in familiar mathematical

notation. Typical uses include

1. Math and computation

2. Algorithm development

3. Data acquisition

4. Modeling, simulation, and prototyping

5. Data analysis, exploration, and visualization

6. Scientific and engineering graphics

7. Application development, including graphical user

interface building.

MATLAB is an interactive system whose basic data

element is an array that does not require dimensioning. This

allows you to solve many technical computing problems,

especially those with matrix and vector formulations, in a

fraction of the time it would take to write a program in a scalar

non interactive language such as C or FORTRAN.

The name MATLAB stands for matrix laboratory. MATLAB

was originally written to provide easy access to matrix

software developed by the LINPACK and EISPACK projects.

Today, MATLAB engines incorporate the LAPACK and

BLAS libraries, embedding the state of the art in software for

matrix computation.

MATLAB has evolved over a period of years with input

from many users. In university environments, it is the standard

instructional tool for introductory and advanced courses in

mathematics, engineering, and science. In industry, MATLAB

is the tool of choice for high-productivity research,

development, and analysis.

MATLAB features a family of add-on application-

specific solutions called toolboxes. Very important to most

users of MATLAB, toolboxes allow you to learn and apply

specialized technology. Toolboxes are comprehensive

collections of MATLAB functions (M-files) that extend the

MATLAB environment to solve particular classes of problems.

Areas in which toolboxes are available include signal

processing, control systems, neural networks, fuzzy logic,

wavelets, simulation, and many others.

The MATLAB System:

The MATLAB system consists of five main parts:

Development Environment:

This is the set of tools and facilities that help you use

MATLAB functions and files. Many of these tools are

graphical user interfaces. It includes the MATLAB desktop

and Command Window, a command history, an editor and

debugger, and browsers for viewing help, the workspace, files,

and the search path.

The MATLAB Mathematical Function:

This is a vast collection of computational algorithms

ranging from elementary functions like sum, sine, cosine, and

complex arithmetic, to more sophisticated functions like

matrix inverse, matrix eigen values, Bessel functions, and fast

Fourier transforms.

The MATLAB Language:

This is a high-level matrix/array language with control

flow statements, functions, data structures, input/output, and

object-oriented programming features. It allows both

"programming in the small" to rapidly create quick and dirty


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throw-away programs, and "programming in the large" to

create complete large and complex application programs.

Graphics:

MATLAB has extensive facilities for displaying

vectors and matrices as graphs, as well as annotating and

printing these graphs. It includes high-level functions for two-dimensional and three-dimensional data visualization, image

processing, animation, and presentation graphics. It also

includes low-level functions that allow you to fully customize

the appearance of graphics as well as to build complete

graphical user interfaces on your MATLAB applications.

The MATLAB Application Program Interface

(API):

This is a library that allows you to write C and Fortranprograms that interact with MATLAB. It includes facilities for

calling routines from MATLAB (dynamic linking), calling

MATLAB as a computational engine, and for reading and

writing MAT-files.

MATLAB WORKING

ENVIRONMENT:

A. MATLAB DESKTOP:-

Mat lab Desktop is the main Mat lab application

window. The desktop contains five sub windows, the

command window, the workspace browser, the current

directory window, the command history window, and one or

more figure windows, which are shown only when the user

displays a graphic.

The command window is where the user types

MATLAB commands and expressions at the prompt (>>) and

where the output of those commands is displayed. MATLAB

defines the workspace as the set of variables that the user

creates in a work session. The workspace browser shows these

variables and some information about them. Double clicking

on a variable in the workspace browser launches the Array

Editor, which can be used to obtain information and income

instances edit certain properties of the variable.

The current Directory tab above the workspace tab

shows the contents of the current directory, whose path is

shown in the current directory window. For example, in the

windows operating system the path might be as follows:

C:\MATLAB\Work, indicating that directory “work” is a

subdirectory of the main directory “MATLAB”; WHICH IS

INSTALLED IN DRIVE C. clicking on the arrow in the

current directory window shows a list of recently used paths.

Clicking on the button to the right of the window allows the

user to change the current directory.

MATLAB uses a search path to find M-files and other

MATLAB related files, which are organize in directories inthe computer file system. Any file run in MATLAB must

reside in the current directory or in a directory that is on search

path. By default, the files supplied with MATLAB and math

works toolboxes are included in the search path. The easiest

way to see which directories are on the search path. The

easiest way to see which directories are soon the search paths,

or to add or modify a search path, is to select set path from the

File menu the desktop, and then use the set path dialog box. It

is good practice to add any commonly used directories to the

search path to avoid repeatedly having the change the current

directory.

The Command History Window contains a record of thecommands a user has entered in the command window,

including both current and previous MATLAB sessions.

Previously entered MATLAB commands can be selected and

re-executed from the command history window by right

clicking on a command or sequence of commands. This

action launches a menu from which to select various options in

addition to executing the commands. This is useful to select

various options in addition to executing the commands. This is

a useful feature when experimenting with various commands

in a work session.

Using the MATLAB Editor to create M-Files:

The MATLAB editor is both a text editor specialized forcreating M-files and a graphical MATLAB debugger. The

editor can appear in a window by itself, or it can be a sub

window in the desktop. M-files are denoted by the extension

.m, as in pixelup.m. The MATLAB editor window has

numerous pull-down menus for tasks such as saving, viewing,

and debugging files. Because it performs some simple checks

and also uses color to differentiate between various elements

of code, this text editor is recommended as the tool of choice

for writing and editing M-functions. To open the editor, type

edit at the prompt opens the M-file filename.m in an editor

window, ready for editing. As noted earlier, the file must be in

the current directory, or in a directory in the search path.

Getting Help:

The principal way to get help online is to use the

MATLAB help browser, opened as a separate window either

by clicking on the question mark symbol (?) on the desktop

toolbar, or by typing help browser at the prompt in the

command window. The help Browser is a web browser

integrated into the MATLAB desktop that displays a


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Hypertext Markup Language(HTML) documents. The Help

Browser consists of two panes, the help navigator pane, used

to find information, and the display pane, used to view the

information. Self-explanatory tabs other than navigator pane

are used to perform a search.

RESULTS

Fig 1: Text to be encodedFig 2: Color image before encoding


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Fig 3: Extracted Text images

CONCLUSION

This project proposed the extended visual

cryptography scheme for natural images. Next it showed a

method to improve the image quality of the output by

enhancing the image contrast beyond the constraints given by

the previous studies. The method enables the contrastenhancement by extending the concept of error and by

performing half toning and encryption simultaneously. The

trade-off between the image quality and the security are

assessed by observing the actual results of this method.

Furthermore, the optimization of the image quality at a given

contrast is discussed. Under an assumption that the occurrence

of the violations is stochastically even in the images, a CFR

function is introduced for the image quality optimization. The

validity of the assumption and the effect of image quality

improvement are also verified with the experiments. Fig.4

shows an example of the results created by proposed method.

BIBILOGRAPHY

1. G. Ateniese, C. Blundo, A. de Santis, and D. Stinson.

Visual cryptography for general access structures. Information

and Computation, 129(2):86–106, 1996.

2. Giuseppe Ateniese, Carlo Blundo, Alfredo De Santis, and

Douglas R. Stinson. Extended capabilities for visual

cryptography. Theoretical Computer Science, 250:143–161,

2001.

3. E.R.Verheul and H.C.A.van Tilborg. Constructions and

properties of k out of n visual secret sharing schemes. Design

Codes and Cryptography, 11(2):179–196, 1997.4. R.W. Floyd and L. Steinberg. An adaptive algorithm for

spatial greyscale. Proc.SID, 17/2:75–77, 1975.

[Gomes97] Jonas Gomes and Luiz Velho. Image Processing

for Computer Graphics. Springer, 1997.

5. [Hofme97] T. Hofmeister, M. Krause, and H.U.Simon.

Contrast-optimal k out of n secret sharing schemes in visual

cryptography. In COCCON ’97, Lecture Notes in Computer

Science, volume 1276, pages 176–185, Berlin, 1997. Springer.

6. [Koga98] Hiroki Koga and Hirosuke Yamamoto. Proposal

of a lattice-based visual secret sharing scheme for color and

gray-scale images. IEICE Transaction on Fundamentals, E81-

A(6):1262–1269, June 1998.

[Naor95] M. Naor and A. Shamir. Visual cryptography,

advances in cryptology. Eurocrypt ’94 Proceeding LNCS ,

950:1–12, 1995.

7. M. Naor and A. Shamir. Visual cryptography ii: Improving

the contrast via the cover base. Theory of Cryptography

Library, (96-07), 1996.

8. V. Rijmen and B. Preneel. Efficient colour visual

encryption or shared colors of benetton. presented at

EUROCRYPT’ 96 Rump Session, available as

http://www.iacr.org/conferences.


Documents

An Improved Visual Cryptography Scheme for Secret Hiding