1

Images Steganography using Pixel Value

Difference and Histogram Analysis

A PROJECT REPORT

Submitted in partial fulfillment of the

Requirement for the award of the

Degree of

BACHELOR OF TECHNOLOGY

In

ELECTRONICS AND COMMUNICATION ENGINEERING

By

SHIVAM NEGI (11BEC0240) AND RITIZ JAAT(11BEC0421)

Under the Guidance of

DR.THANIKAISELVAN V. (Associate professor, VIT)

SCHOOL OF ELECTRONICS ENGINEERING

VIT University

VELLORE. (TN) 632014

WINTER 2015

2

CERTIFICATE

This is to certify that the Project work titled “Images Steganography using Pixel Value

Difference and Histogram Analysis” that is being submitted by SHIVAM NEGI (11BEC0240)

AND RITIZ JAAT(11BEC0421) is in partial fulfillment of the requirements for the award of

Bachelor of Technology, is a record of bonafide work done under my guidance. The contents of this Project work, in full or in parts, have neither been taken from any other source nor have been

submitted to any other Institute or University for award of any degree or diploma and the same is certified.

Dr. Thanikaiselvan V.

Guide

The thesis is satisfactory / unsatisfactory

I n t e r n a l E x a m i n e r E x t e r n a l E x a m i n e r

Approved by

Program Chair

3

ACKNOWLEDGEMENTS

I express the deepest gratitude to our chancellor, Dr.G.Viswanathan for providing excellent

infrastructure and lab facilities.

I are very grateful to our dean, Dr. Ramachandra Reddy, SENSE for his unwavering support

during the entire course of this project. I are deeply indebted to him for providing virtuous lab

environment in Embedded and Networking division which was helpful to attain practical

knowledge.

I are very thankful to our Program chair, Assistant Dean, Dr. P. Arulmozhivarman , SENSE

for his kind support during the entire course of this project.

I wish to thank my guide Dr. Thanikaiselvan V. , Sr. Associate Professor for his exemplary

guidance, immense help, motivation and guidance throughout the tenure of my project in spite of

his hectic schedule, which truly remained driving spirit in my project and their experience helped

me in clarifying abstruse concepts and in understanding the objective of my project.

I would also like to thank VIT University for giving us the opportunity to do this thesis work.

Finally, I take this opportunity to extend our deep appreciation to our family members and

friends, for all that they meant to us during the crucial times of the course of our project .

4

Table of Contents

List of Figures

List of Tables

1. Introduction

1.1 Introduction to Steganography

1.2 Need of Information Security

1.3 Image Stegnography

1.4 Steaganalysis

1.5 Data Security and Its need

1.6 Advantages and Disadvantages

2. Literature Review

3. Related Work

5

Abstract

A new data hiding method is proposed in this project , which can increase the

steganographic security of a data hiding scheme .In this method a cover image is first

mapped into a 1D pixels sequence by Hilbert filling curve and then it has been divided

into non-overlapping embedding units .The division is made such that it gives two

consecutive pixel values .As human eye has limited tolerance when it comes to texture

and edge areas than in smooth areas , and as the difference between the pixel pairs in those

areas are larger , therefore the method exploites pixel value difference (PVD) to solve out

overflow underflow problem .

The digits in any bases can be embedded according to the local complexity of the cover

image and through this way it solves the problems which are faced by existent EMD

method . Overflow, Underflow or falling-off boundary problem can be reduced by this

proposed method henceforth solves the detectable artifacts caused by them . The

experimental results show our method not only to enhance embedding rate but also keep

stego image quality .

6

Chapter 1

Introduction

With the change in the world and advent of digital media , there are new methods which

are evolved and adopted for communication . The advancement in the digital world lead to

increase in the requirement of digital security and protection of digital data . As the time

progressed it has been observed that digital media and information/data transfer suffered a

lot from the lack of digital security through cyber attacks , hacking and became prey to

cyber crimes . Criminals and technicians developed softwares which can hack into other’s

system and can extract information through that .On the other hand many of the methods

are also invented to make the network secure and prevent these type of hacking .

Among many of the things which are invented , it has been been observed that

stegnography and cryptography are one of the finest and easiest method which can be used

for protection of digital data .It has also been detected that , both of them work together to

maximize the protection level or security of the data compared to the cases when they are

used solely .Cryptography , basically scramble the data and twist the data in such a way

that it gets turned into a cryptic message . While stegnography do the hiding of digital data

, the cryptography make it impossible to decrypt and get the original value of the message

.

A detailed description of steganography , cryptography and steganalysis is given and their

necessity for security purposes are given below :

7

1.1 Introduction to Steganography :

In greek , the word “ Stego “ stands for “ roof “ or “ covered/hidden “ and “graphia “

means writing . So the literal meaning of steganography is hiding a data in another data ,

message or information . The cover may be in the form of any other digital image , video

or audio and this is an ancient method which is dated back to ancient roman times where a

message is concealed in several other cover equipments just to hide that and make it

undetectable .In some of the cases the messages are tattooed on the heads of the

messenger and then the hairs of the messenger are allowed to grow so as to conceal the

message .

Over the years vast range of stegnography methods have been used which have been

proven successful and failure according to their ability to hold and concele the message .

A lot of research is still going on to develop more security and reduce the detectability of

the concealed message .In the same direction , the computers and networks use several

methods to hide data . Some of these include , hiding text within the web pages , hiding

data in plain pages , using ciphers and encrypted messages to conceal more complicated

single message which may contain the direction to find out the real hidden message in a

cryptic way .Therefore in modern times , a large amount of data is stored in images and a

lot of efforts are put in to increase the capacity of data hiding in various available covers .

Protection against detection can be needed if someone wants to ensure that the embedded

message is not detected by a third unauthorized party. For example, a user wants to

prevent others from finding out that an image contains a secret message hidden by the

"Least-bit insertion method". This aim of data hiding is achieved by using schemes that do

not modify the original object in a visible way; all changes should be indiscernible to the

human eye or the computer. Protection against removal, on the other hand, tries to prevent

the removal of hidden data without making it useless or degrading its quality. There are

8

many types of steganography methods that support almost all the digital file formats. The

high degrees of redundancy formats are most suitable for this type of techniques.

Redundancy can be defined as the bits of an object that provide accuracy far greater than

necessary for the object’s use and display .

In this project we are focusing on the image steganography where a secret message is

concealed in the cover image . The secret message is generally converted into its numeric

form and then its then embedded into the pixels of cover image via various existing

methods like LSB substitution , histogram shifting , pixel differentiation etc .

At the present time, the Internet has turn out to be a public communication channel.

Exchange of data / information in public system means that some complications need to

be confronted, which include data security and protection of copy writes. Ciphering is a

well-known method for security protection, but it has the drawbacks of making a message

incomprehensible and therefore attracts the attention of observers and hackers . And this is

one of the biggest difference which helps steganography to become one of the best

method for data transfer secretly and securely.

1.2 Need Of Information Security

In modern times , technology security is one of the biggest concern . As the cyber crime is

on rise , not only a secured network is needed but also it is necessary to provide security to

images like blue print of company projects and their product designs , confidential data

and images related to army or country’s security , using image stegnography .As the

messeges are encrypted by several algorithms , its difficult to find them in the image .

9

1.3 Image Steganography

When the principles of steganography are applied over the images for getting an output

which have data/ information concealed in them , then its called image steganography

.Here the data is hidden in the form of text in the images which generally camouflage the

secret data .

10

1.4 Data Security and Its need

Data security means protecting data and any important information, from negative forces

and from the undesirable actions of unauthorized users and hackers .As the society

evolved and advanced , we started depending more and more on the digital way of

communication and transfer of data . As this methods are really prevalent in our technical

and modern ecosystem , it calls for a more secure and accountable way for these

communication methods . Cryptography is generally used for protection of password ,

banking information , emails and other information of sensitive nature .And henceforth ,

cryptography helps the government to protect these types of information from getting in

the hands of enemies .

Many of the terrorist organizations as well as security agencies use this method to hide the

data to communicate confidentially .This include use of local media or internet to send the

information in cryptic manner .Steagnography can be misused and therefore the process

of steaganalysis has been brought to curb the growing use of such methods for detecting

information which has been sent by such terrorist organizations .

11

1.5 Steganalysis

It has been observed that , digital watermarking and the steganography are one of the most

secure method for digital data communication . This has led to development of methods

which can find out the digital data which is hidden in the audio or image or video

.Steganalysis is one of the method which has played an important role in detection if such

hidden data which has been sent using steganography .As terrorists or other criminal

organization use this method to have their covert exchange of data and even for

communication purpose , the method of steganalysis is used to detect and extract the

hidden messages from the cover .

In the method of steganalysis it is priory assumed that , the data is hidden in the images

and the entire method is designed on the basis of theory of statistics .The process works in

such a way that it narrows down to file which has hidden data from the original set of files

. Due to abundant amount of images and a large variety of encoding algorithms ,

steaganalysis is a challenge .An algorithm named general steaganalysis method don’t

depend on cover image or the encoding algorithm type , thus many obstacles incurred in

obtaining the original cover image and the method used are avoided .

1.6 Data hiding techniques

Lossless data hiding techniques is a method of compressing data in a way such that the

original data is retrieved without any data loss. The image obtained is the exact replica of

the original image. The quality of the data that is compressed is not degraded and the

exact data is obtained without any loss. Lossless compression methods may be categorized

according to the type of data they are designed to compress. Some main types of targets

for compression algorithms are text, executables, images, and sound. Whilst, in principle,

any general-purpose lossless compression algorithm (general-purpose means that they can

handle all binary input) can be used on any type of data, many are unable to achieve

12

significant compression on data that is not of the form that they are designed to deal with.

Sound data, for instance, cannot be compressed well with conventional text compression

algorithms. Most lossless compression programs use two different kinds of algorithms:

one which generates a statistical model for the input data, and another which maps the

input data to bit strings using this model in such a way that "probable" (e.g. frequently

encountered) data will produce shorter output than "improbable" data. Often, only the

former algorithm is named, while the second is implied (through common use,

standardization etc.) or unspecified.

1.6.1 Advantages of Steganography :

1. It is robust method .

2. It provide a secret and safe method for transmission of data .

3. As the file show no physical change after steganography , it go undetected .

4. The method is also used in holding the digital data in images , not discreetly but

purposely and is being used in medical technology to

5. It has larger data hiding capacity .

13

1.6.2 Disadvantages of Steagnography :

1. Staganalysis can be used to extract the data and it gets easy when the image is

subjected to staganalysis attacks .

2. The extracted data can be easily be understood as the data is not be in the cryptic

form and the important information which is supposed to be hidden can be easily

perceived by the person who checks that .

3. Message is hard to recover if image is subject to attack such as translation and

rotation.

4. Significant damage to picture appearance. Message difficult to recover.

5. Image is distorted. Message easily lost if picture subject to compression such as

JPEG.

HUMAN VISUAL SYSTEM MODEL

To balance transparency and robustness, an effective watermarking scheme should exploit

HVS masking characteristics. Here, the luminance masking, texture masking and edge

masking features of the cover image was used to develop a spatial HVS model in a better

way.

PEAK SIGNAL TO NOISE RATIO

The word peak signal-to-noise ratio (PSNR) is an expression for the ratio between the

maximum possible value (power) of a signal and the power of distorting noise that affects

the quality of its representation. As many signals have a very wide dynamic range, (ratio

between the largest and smallest possible values of a changeable quantity) the PSNR is

usually expressed in terms of the logarithmic decibel scale.

14

Improving the visual value of a digital image can be subjective and telling that one

particular method provides a better quality image could vary from person to person. For

this reason, it is necessary to establish quantitative/empirical measures to compare the

effects of image enhancement techniques on image quality.

Different image enhancement algorithms can be compared systematically to identify

whether a particular algorithm produces better results. If we can show that an algorithm

or set of algorithms can enhance a degraded known image to more closely resemble the

original, then we can more accurately conclude that it is a better algorithm.

The mathematical representation of the PSNR is as follows:

where the MSE (Mean Squared Error) is:

This can also be represented in a text based format as:

MSE = (1/(m*n))*sum(sum((f-g).^2))

PSNR = 20*log(max(max(f)))/((MSE)^0.5)

Legend:

f represents the matrix data of our original image g represents the matrix data of our degraded image in question

m represents the numbers of rows of pixels of the images and i represents the index of that row

n represents the number of columns of pixels of the image and j represents the index of that column

MAXf is the maximum signal value that exists in our original “known to be good” image

15

Basically , the MSE represents the average of the squares of the "errors" between actual image and the noisy image. The mean squared error (MSE) for our practical purposes

allows us to compare the “true” pixel values of our original image to our degraded image. The error is the amount by which the values of the original image differ from the degraded

image.

The target is to achieve higher PSNR, for that , the better degraded image has been reconstructed to match the original . This would occur because the objective is to

minimize the MSE between images with respect the maximum signal value of the image.

It has been derived that:

• a lower value for MSE means less error, and as considered in Eq , the inverse

relationship between the MSE and PSNR translates to a high value of PSNR.

• higher value of PSNR is good and signifies that the ratio of signal-to-noise is higher

.

16

Literature Review

As explained above , steganography is the art of hiding digital data into an image , various

techniques have been employed to achieve the same . Logically , it can be said that ,

embedding of data may lead to noise in the output results and may distort the pixel value

of existing image .As this slight change will not be perceivable by human eye , it makes

steganography one of the most powerful tool for this purpose.

1.Chin-Chen Chang, The Duc Kieu and Wen-Chuan Wu implemented a lossless data

embedding technique by joint neighboring coding.The proposed method presents new

reversible information hiding method for vector quantization (VQ) and used joint

neighboring coding (JNC) technique over a compressed grey scale image . This way ,in

this method , the data is successfully embedded secretly by using the difference of current

VQ- Compressed index and upper neighboring indices .It has been found that , the

proposed method achieves the best visual quality reconstructed image when it is compared

with the two related work .

The time duration for execution of this method is also limited to make it one of the fastest

method and only Yang et Al’s method is above it when it comes to speed , followed by

the Lin and Chang’s method . But this method is having the higher bit rate as compared to

the other method . The experimental fallouts show that the proposed method of Chin-Chen

Chang, The Duc Kieu and Wen-Chuan Wu achieves the best visual quality of

reconstructed images (i.e. highest PSNR) and very less distortion . In addition, the

proposed method has as high embedding capacity as Lin and Chang's method, followed by

Yang et al.'s method.

17

Therefore the method proposed is applicable to some steganographic applications such as

secret communications where the network bandwidth and the execution speed are areas of

which demanded more research at that time.

The tradeoff among image distortion, embedding capacity, and efficiency varies from

application to application. As, different techniques are employed for different

applications. Three embedding classes are information hiding techniques in spatial

domain, in transformed domain (e.g. using DCT, DFT, or DWT) , and in compressed

domain .

2.Chin-Chen Chan , The Duc Kieu ,Yung-Chen Chou later came up with a new

research in the field of Reversible information hiding for Vector Quantization (VQ)

indices based on locally adaptive coding . They proposed a different reversible

information hiding technique for vector quantization (VQ) compressed images built on

locally adaptive coding method. The proposed steganographic method embeds the

message into Vector quantization indices in an already present index table during the

process of compressing the index table in the block-by-block manner.

The suggested method by this paper is superior to the one proposed by Yang et al.’s and

Lin and Chang’s when it comes to visual quality of reconstructed images .The embedding

rate is also found out to be better tgen Yang Et al’s method .With regard to the

compression rate and the encoding execution time, Yang et al.’s method is the best,

followed by the proposed method, and then Lin and Chang’s method. Thus, we can

conclude that method proposed by them is applicable to stenographic applications such as

online content distribution systems.

Designing a novel steganography method is mostly concentrated on achieving good visual

quality, high hiding capacity, robustness, and steganographic security . An information

18

hiding scheme with low image distortion is more secure than that with high distortion

because it does not raise any suspicions. Therefore, image quality factor is the first

important one. The second important factor is embedding capacity (called capacity for

short). An information hiding scheme with high payload is preferred because more secret

data can be transferred. In nature, embedding capacity is inversely proportional to visual

quality. That is, if capacity is increased then visual quality is reduced. The tradeoff

between the two factors above is made by users, depending on users' purposes and

application fields.

3. Fridrich J and Goljan M, Du R came up with a method of reliable detection of

LSB steganography in color and grayscale Images. A large number of

steganographic projects use the method of embedding of Least Significant Bit (LSB)

for message hiding in colored cover images and grayscale cover images. In the method

proposed by Fridrich and Goljan , a very accurate and reliable algorithm is designed

that can detect LSB embedding in randomly scattered pixels in both 24-bit color

images and 8-bit grayscale .

Least-significant bit (LSB) based approach has become one of the most popular approach

for application of steganographic algorithms in the spatial domain. However, before the

proposition of paper by Weiqi Luo ( Member, IEEE ) , Fangjun Huang ( Member, IEEE) ,

and Jiwu Huang ( Senior Member, IEEE) , in most existing approaches, the choice of

embedding positions contained by a cover image mainly be contingent on a pseudo

random number generator without considering the correlation between the image content

itself and the size of the secret message.

The paper proposed to expand the LSB matching revisited image steganography and

came up with an edge adaptive scheme which can select the embedding regions based on

19

the size of the secret message and the difference between two consecutive pixels in the

cover image. When it comes to the lower embedding rates, only sharper edge regions are

used while keeping the other smoother regions as they are. When the embedding rate

increases, more edge regions can be released adaptively for data hiding by adjusting just a

few parameters. In this method the experimental results evaluated on 6000 natural images

with three specific and four universal steganalytic algorithms show that the new scheme

can enhance the security significantly compared with typical LSB-based approaches as

well as their edge adaptive ones while preserving higher visual quality of stego images .

Firstly the secret message is embedded into the sharper edge regions adaptively according

to a threshold determined by the size of the secret message and the gradients of the content

edges. It is expected that this adaptive idea can be extended to other steganographic

methods such as audio/video steganography in the spatial or frequency domains when the

embedding rate is less than the maximal amount.

Pixel-value differencing (PVD) scheme

The pixel-value differencing (PVD) scheme uses the difference value between two

consecutive pixels in a block to determine how many secret bits should be embedded.

There are two types of the quantization range table given by Wu and Tasi . The first one

was based on selecting the range widths of [8, 8, 16, 32, 64, 128], to provide large

capacity. The second was based on selecting the range widths of [2, 2, 4, 4, 4, 8, 8, 16, 16,

32, 32, 64, 64], to provide high imperceptibility. This work designs a new quantization

range table based on the perfect square number to decide the payload by the difference

value between the consecutive pixels.

The data hiding techniques is carried out in three domains , namely spatial domain,

compress domain, and frequency domain. The domains specified has their own

advantages amd disadvantages when it comes to the hiding capacity , execution time and

20

the payload capacity. Recently in the research field in this domain , many researchers are

more enthusiastic to improve the embedding efficiency and decrease the possibility of

detection. Least-significant-bit matching is the conventional effective steganography

method, and it is proved much more problematic to detect than simple LSB replacement.

4. Zhang and Wang (2006) proposed to fully exploit different modification directions for

secret data embedding . The proposed method can achieve various hiding capacities and

good visual qualities compared to two recently published works, namely Mie-likainen's

method (Mielikainen, 2006), Zhang and Wang's method (Zhang and Wang, 2006).

5.Hong, W. and Chen, T. S. came up with a new data-hiding method based on pixel pair

matching (PPM). Pixel Pair Matching used the idea of the use of the values of pixel pair

as a reference coordinate, and search a coordinate in the neighborhood (vicinity) set of this

pixel pair according to a given message bit. The pixel pair is then replaced by the searched

coordinate to embed the secret message bit. Based on PPM, two data-hiding methods have

been proposed recently that is Exploiting modification direction (EMD) and diamond

encoding (DE) . The maximum capacity of EMD is 1.161 bpp and DE has higher capacity

than EMD. The proposed method offers lower distortion than DE by providing more

compact neighborhood sets and allowing embedded digits in any notational system.

Compared with the optimal pixel adjustment process (OPAP) method, the proposed

method always has lower distortion for various payloads. Experimental result shows that

the proposed method provides better performance and better security than those of OPAP

and DE.

6. Chao et al. (2009) , got inspired by EMD method and proposed a diamond encoding

(DE) method to greatly improve the payload of EMD. Extraction function is used to

generate diamond characteristic values (DCV) in DE method , and the payload is

controlled by using the embedding parameter . Despite of the advantages of DE method

21

, it still does not allow embedding digits in multiple bases , which is the essential

requirement for an embedding method with the consideration of human visual system

(HVS) . If pixel values are exceeding 0 or 255 then it will be added or subtracted by B to

retain the pixel values within the range [0, 255] so as to prevent the overflow or underflow

condition . This adjustment may cause a large alteration, and may lead to generation of

image noise when the embedding parameter is large.

The other groups which evolved took embedding strategies like HVS into consideration,

namely these methods embed more data in edge areas to acquire more payload whereas

embed less data in smooth areas to preserve the visual quality.

7.Wuand Tsai (2003) , worked in the same direction and recommended a data embedding

method based on pixel value differencing (PVD). Due to pixel pairs with larger difference

are often located in complex regions, PVD embeds more data into pixel pairs with larger

differences. But the PVD method may lead to considerable distortion and digression in the

image quality due to presence of noise.

8. Kazem Qazanfari, Reza Safabakhsh , proposed a method to prevent the cover image

from steganalysis methods . He found out that LSB and outguess are two steganography

approaches which preserve the cover histogram to a large extent and are highly successful

. In these methods , some extra bits are embedded to retain the cover histogram. But

these techniques adversely affect the statistical and perceptual attributes of the cover

image. LSB was proposed to improve LSB by prohibiting some pixels from changing,

resulting in the reduction of the extra bits. In this method , the LSB is improved. The

method talks about distinguishing sensitive pixels and protecting them from extra bit

embedding, which causes lower distortion in the co-occurrence matrices. In addition, it

also extend LSB to preserve the DCT coefficients histogram of jpeg images and

generalize the method to the case where more than one bit of the cover elements are used.

22

The experimental results show that the improved LSB method produces fewer traces in

the co-occurrence matrices than the LSB method. Furthermore, the histogram based

attacks cannot detect stego images produced by their method with or without extra bits

embedding.

By providing an embedding secret to improve on these short comings, in this paper, two

pixels are adopted as an embedding unit and PVD method is employed on them . 1D pixel

sequence is mapped by using the Hilbert Filling curve .The proposed method not only

consider human visual system (HVS) and the correlation of local consecutive pixels but

also avoid storing the search matrix data and therefore adds value to the existing research

going on the improvement of the data hiding schemes .

23

Chapter Three

Related Work

LSB Matching and Replacement

In a binary number system ,the bit at the one’s place , the right most bit is called LSB or

the least significant bit .Least significant bit encoding is one of the most utilized

technique which is quite famously used for steganography. The method focuses on

altering the least significant bit and therefore embed the data in the same way . To defeat

the histogram based steganalysis methods, many efforts have been made by researchers to

protect the histograms of images. One of the first solutions to defeat these attacks was

LSB matching. LSB matching increases or decreases the pixel values with the same

probabilities when the least significant bit of the pixel value is not equal to the message

bit.

This method can be used in embedding of fixed length of message .But only three bits are

embedded by this method as embedding more bits may cause distortion of the image and

cause failure. Different adaptive methods are used to minimize the distortion.

When we apply an optimal pixel adjustment process to the stego image obtained by the

simple LSB substitution technique, the image quality of the stegoimage can be improved

by a large extent with low extra computational difficulty. The worst case mean-square-

error between the stego image and the cover image is derived. Experimental results show

that the stego image is visually indistinguishable from the original cover image. The

obtained results also show a significant improvement with respect to a previous work.

24

Exploiting modification direction (EMD)

Least-significant-bit (LSB) matching is one of the most conventional as well as efficient

steganography method, and after extreme experimentations , it is examined that it is

much more difficult to detect than simple LSB replacement . To fully exploit different

modification directions for secret data embedding - Zhang and Wang in year 2006 ,

proposed the exploiting modification direction (EMD) method. In this method , ‘n’

pixels are taken as embedding unit , and the digits are embedded in (2n+1) base. Its

maximum payload is 1=2 log2 5 = 1:161 bpp when n=2. Zhang and Wang claimed that

the modification directions of Mielikainen's scheme are not explored fully. Specifically,

the PSNR value of the EMD method is slightly smaller than that of Mielikainen's scheme

at hiding capacity of 1 bpp. In addition, forn¼2, Zhang and Wang's method only utilizes

four modification directions.Kieu and Chang (2011) (FEMD) proposed a novel extraction

function (also called the modified extraction function) by modifying the extraction

function proposed by Zhang and Wang (2006). The modified extraction function allows

the proposed method to exploit eight modification directions for embedding secret data,

restrict the embedding distortion into a square of various sizes (e.g. 22, 33, and so on) and

use the minimum distortion embedding (MDE) process. By this way, the proposed method

can achieve various hiding capacities and good visual quali-ties compared to two recently

published works, namely Mie-likainen's method (Mielikainen, 2006), Zhang and Wang's

method (Zhang and Wang, 2006). To solve the irreversibility of the EMD method in

(Zhang and Wang, 2006; Qin et al. 2014) proposed a novel data hiding scheme based on

EMD with reversibility by using two steganographic images, which can also achieve

satisfactory performances of the hiding capacity and the stego image quality.

25

Chapter 4

Proposed Methodology

A new data hiding method is proposed in this project , which can increase the

steganographic security of a data hiding scheme .In this method a cover image is first

mapped into a 1D pixels sequence by Hilbert filling curve and then it has been divided

into non-overlapping embedding units .The division is made such that it gives two

consecutive pixel values .As human eye has limited tolerance when it comes to texture

and edge areas than in smooth areas , and as the difference between the pixel pairs in those

areas are larger , therefore the method exploites pixel value difference (PVD) to solve out

overflow underflow problem .

The digits in any bases can be embedded according to the local complexity of the cover

image and through this way it solves the problems which are faced by existent EMD

method . Overflow, Underflow or falling-off boundary problem can be reduced by this

proposed method hensforth solves the detectable artifacts caused by them . The

experimental results show our method not only to enhance embedding rate but also keep

stego image quality.

26

Embedding

• An image with size MxN is taken, and the image is brought to Hilbert 1D sequence.

• Partition the sequence into non-overlapping (M x N) /2 embedding units with two

consecutive pixels .Calculate the differences of the adjacent pixel values .

• Design a range table which consists of the contiguous sub-ranges Wj=(j=1,2,3…, 6)

W={Wj=[lj,uj]}={[0, 7],[8, 15],[16,31],[32, 63],[64, 127],[128, 255]}

For wj=uj-lj+1

• If di belong the range of Wj calculate the value of si and ki :Compute si and ki

• Read the Next Ki seperate bit from the binary secret data . Convert them into

decimal number . Now compute extraction function .

• Now compare, extraction function with M (I), based on the comparison we will

modify pixel pair and compute the difference between the modified pixel value

and determine if it falls in the same range of Wj.

• If the range is same as older calculation , the new pixel value replaces the older one

.But if its not then we will select the candidate pixel pair such that it falls in the

range and meets the conditions .

Block Diagram Embedding

Cover Image Partitioning Get secret bits.

Convert Bin to

Decimal

Embedding

Process

Optimization

Problem –

Overflow /

Underflow

Stego Image

27

Generation of Secret Data

Using the random bit generator , 4 lakhs binary random bits are generated . Those bits are

later stored in an array format so that it can be used . Each set of generated bits are used

only once , and are called secret message . The secret message bits are converted to its

decimal value (mi) . The secret message bits are used for embedding and helps in

determining the payload capacity of the cover image.

Data Extraction

• To extract the embedded message digits, pixel pairs are scanned in the same order

as in the embedding procedure. The embedded message digits are the values of

extraction function of the scanned pixel pair.Do exactly the same things as Step 1 in

data embedding.

• Calculate difference of adjacent pixels.From the wj division, find out the range in

which the difference belongs.Now calculate mi* exactly the same way as in data

embedding

•

28