Secret Communication Using Digital Image Steganography

CHAPTER 1

INTRODUCTION

1.1 Cryptography:

The earliest forms of information hiding can actually be considered to be highly

crude forms of private-key cryptography; the “key” in this case being the knowledge of

the method being employed (security through obscurity). Steganography books are filled

with examples of such methods used throughout history. Greek messengers had messages

tattooed into their shave head, concealing the message when their hair finally grew back.

Wax tables were scraped down to bare wood were a message was scratched. Once the

tablets were re-waxed, the hidden message was secure. Over time these primitive

cryptographic techniques improved, increasing speed, capacity and security of the

transmitted message.

Today, crypto-graphical techniques have reached a level of sophistication such

that properly encrypted communications can be assumed secure well beyond the useful

life of the information transmitted. In fact, it’s projected that the most powerful

algorithms using multi kilobit key lengths could not be comprised through brute force,

even if all the computing power worldwide for the next 20 years was focused on the

attack. Of course the possibility exists that vulnerabilities could be found, or computing

power breakthroughs could occur, but for most users in most applications, current

cryptographic techniques are generally sufficient.

Why then pursue the field of information hiding? Several good reasons exist, the

first being that “security through obscurity” isn’t necessarily a bad thing, provided that it

isn’t the only security mechanism employed. Steganography for instance allows us to

hide encrypted messages in mediums less likely to attract attention. A garble of random

characters being transmitted between two users may tip off a watchful 3 rd party that

sensitive information is being transmitted; whereas baby pictures with some additional

noise present may not. The underlying information in the pictures is still encrypted, but

attracts far less attention being distributed in the picture then it would otherwise.

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This becomes particularly important as the technological disparity between

individuals and organizations grows. Governments and businesses typically have access

to more powerful systems and better encryption algorithms then individuals. Hence, the

chance of individual’s messages being broken increases which each passing year.

Reducing the number of messages intercepted by the organizations as suspect will

certainly help to improve privacy.

Another advantage hinted at is that information hiding can fundamentally change

the way that we think about information security. Cryptographic techniques generally

rely on the metaphor of a piece of information being placed in a secure “box” and locked

with a “key”. The information itself is not disturbed and anyone with the proper key can

gain access. Once the box is open, all of the information security is lost. Compare this to

information hiding techniques where the key is embedded into he information itself.

Cryptography is the science of using mathematics to encrypt and decrypt data.

Cryptography enables you to store sensitive information or transmit it across insecure

networks (like the Internet) so that it cannot be read by anyone except the intended

recipient. While cryptography is the science of securing data, cryptanalysis is the science

of analyzing and breaking secure communication. Classical cryptanalysis involves an

interesting combination of analytical reasoning, application of mathematical tools, pattern

finding, patience, determination, and luck. Cryptanalysts are also called attackers.

Cryptology embraces both cryptography and cryptanalysis.

First we start with a few definitions. Cryptography can be defined as the

processing of information into an unintelligible (encrypted) form for the purposes of

secure transmission. Through the use of a “key” the receiver can decode the encrypted

message (decrypting) to retrieve the original message.

Stenography improves on this by hiding the fact that a communication even

occurred. The message m is imbedded into a harmless message c which is defined as the

cover-object. The message m is then embedded into c, generally with use of a key k that

is defined as the stego-key. The resulting message is then embedded into the cover-object

c, which results in stego-object s.

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1.2 Steganography:

Steganography means to hide secret information into innocent data. Digital

images are ideal for hiding secret information. An image containing a secret message is

called a cover image. First, the difference of the cover image and the stego image should

be visually unnoticeable. The embedding itself should draw no extra attention to the stego

image so that no hackers would try to extract the hidden message illegally. Second, the

message hiding method should be reliable. It is impossible for someone to extract the

hidden message if she/he does not have a special extracting method and a proper secret

key. Third, the maximum length of the secret message that can be hidden should be as

long as possible.

“Steganography is the art of hiding information in ways that prevent the detection of

hidden messages,”

1.2.1 Steganography in History:

Steganography comes from Greek and means “covered writing”. The ancient

Greeks wrote text on wax-covered tablets. To pass a hidden message, a person would

scrape off the wax and write the message on the underlying wood. He/she would then

once again cover the wood with wax so it appeared unused. Many developments in

steganography occurred during World War II. This included the development of

invisible inks, microdots, and encoded messages.

1.2.2 Steganography in the Digital Age:

Steganography is the art of secret communication. Its purpose is to hide the very

presence of communication as opposed to cryptography whose goal is to make

communication unintelligible to those who do not possess the right keys. Digital images,

videos, sound files, and other computer files that contain perceptually irrelevant or

redundant information can be used as “covers” or carriers to hide secret messages. After

embedding a secret message into the cover-image, a so-called stego-image is obtained. It

is important that the stego-image does not contain any easily detectable artifacts due to

message embedding. A third party could use such artifacts as an indication that a secret

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message is present. Once this message detection can be reliably achieved, the

steganographic tool becomes useless.

Obviously, the less information is embedded into the cover-image, the smaller the

probability of introducing detectable artifacts by the embedding process. Another

important factor is the choice of the cover-image. The selection is at the discretion of the

person who sends the message. The sender should avoid using cover-images that would

be easy to analyze for presence of secret messages. For example, one should not use

computer art, charts, images with large areas of uniform color, images with only a few

colors, and images with a unique semantic content, such as fonts. Although computer-

generated fractal images may seem as good covers6 because of their complexity and

irregularity, they are generated by strict deterministic rules that may be easily violated by

message embedding.

1.3 Cryptography VS Steganography:

Cryptography is the science of encrypting data in such a way that nobody can

understand the encrypted message, whereas in steganography the existence of data is

conceived means its presence cannot be noticed. The information to be hidden is

embedded into the cover object which can be text, image, audio or video so that the

appearance of cover object doesn’t vary even after the information is hidden.

Information to be hidden + cover object = stego object.

To add more security the data to be hidden is encrypted with a key before

embedding. To extract the hidden information one should have the key. A stego object is

one, which looks exactly same as cover object with hidden information.

1.4 Steganography VS Watermarking:

Watermarking is another branch of steganography it is mainly used to restrict the

piracy in digital media. In steganography the data to be hidden is not at all related to the

cover object, here our main intention is secret communication. In watermarking the data

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to be hidden is related to the cover object it is extended data or attribute of the cover

object, here our main intention is to stop piracy of digital data. Steganography is a very

powerful tool because, as the stated above, it can be very difficult to detect.

 1.5 Stegosystem:

The stegosystem is conceptually similar to the cryptosystem.

Figure 1.1 Basic block diagram of Stegosystem

Emb: The message to be embedded. It is anything that can be represented as a bit stream

(an image or text).

Cover: Data/Medium in which emb will be embedded.

Stego: Modified version of the cover that contains the embedded message,

Emb.key: Additional data that is needed for embedding & extracting.

fE: Steganographic function that has cover, emb & key as parameters.

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Figure 1.2 Graphical version of the Stegosystem

Steganography refers to the science of “invisible” communication. Unlike

cryptography, where the goal is to secure communications from an eavesdropper,

steganographic techniques strive to hide the very presence of the message itself from an

observer. Although steganography is an ancient subject, the modern formulation of it is

often given in terms of the prisoner’s problem where Alice and Bob are two inmates who

wish to communicate in order to hatch an escape plan. However, all communication

between them is examined by the warden, Wendy, who will put them in solitary

confinement at the slightest suspicion of covert communication. Specifically, in the

general model for steganography, we have Alice wishing to send a secret message to

Bob. In order to do so, she” embeds” into a cover-object, to obtain the stego-object.

The stego-object is then sent through the public channel. The warden, Wendy,

who is free to examine all messages exchanged between Alice and Bob, can be passive or

active. A passive warden simply examines the message and tries to determine if it

potentially contains a hidden message. If it appears that it does, she then takes appropriate

action else she lets the message through without any action. An active warden, on the

other hand, can alter messages deliberately, even though she may not see any trace of a

hidden message, in order to foil any secret communication that can nevertheless be

occurring between Alice and Bob. The amount of change the warden is allowed to make

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depends on the model being used and the cover objects being employed. For example,

with images, it would make sense that the warden is allowed to make changes as long as

she does not alter significantly the subjective visual quality of a suspected stego-image.

It should be noted that the main goal of steganography is to communicate securely

in a completely undetectable manner. That is, Wendy should not be able to distinguish in

any sense between cover-objects (objects not containing any secret message) and stego-

objects (objects containing a secret message). In this context, “steganalysis” refers to the

body of techniques that are designed to distinguish between cover-objects and stego-

objects. It should be noted that nothing might be gleaned about the contents of the secret

message. When the existence of hidden message is known, revealing its content is not

always necessary. Just disabling and rendering it useless will defeat the very purpose of

steganography. In this paper, we present a steganalysis technique for detecting stego-

images, i.e., still images containing hidden messages, using image quality metrics.

Although we focus on images, the general techniques we discuss would also be

applicable to audio and video media. Given the proliferation of digital images, and given

the high degree of redundancy present in a digital representation of an image (despite

compression), there has been an increased interest in using digital images as cover-

objects for the purpose of steganography. The simplest of such techniques essentially

embeds the message in a subset of the LSB (least significant bit) plane of the image,

possibly after encryption. It is well known that an image is generally not visually affected

when its least significant bit plane is changed. Popular steganographic tools based on

LSB like embedding vary in their approach for hiding information. For example Steganos

and Stools use LSB embedding in the spatial domain, while Jsteg embeds in the

frequency domain. Other more sophisticated techniques include the use of quantization

and dithering for a good survey of steganography techniques. What is common to these

techniques is that they assume a passive warden framework. That is they assume the

warden Wendy will not alter the image. We collectively refer to these techniques as

passive warden steganography techniques.

Conventional passive warden steganography techniques like LSB embedding are

not useful in the presence of an active warden as the warden can simply randomize the

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LSB plane to thwart communication. In order to deal with an active warden Alice must

embed her message in a robust manner. That is, Bob should be able to accurately recover

the secret message despite operations like LSB randomizing, compression, filtering, and

rotation by small degrees, etc. performed by the active warden Wendy. Indeed, the

problem of embedding messages in a robust manner has been the subject of intense

research in the image processing community, albeit for applications other than

steganography, under the name of robust digital watermarking A robust digital watermark

is an imperceptible signal added to digital content that can be later detected or extracted n

order to make some assertion about the content. For example, the presence of her

watermark can be used by Alice to assert ownership of the content. Recent years have

seen an increasing interest in digital watermarking with many different applications,

ranging from copyright protection and digital rights management, to secret

communication.

Essentially robust digital watermarks provide a means of image-based

steganography in the presence of an active warden since modifications made by the

warden will not affect the embedded watermark as long as the visual appearance of the

image is not significantly degraded. However, despite this obvious and commonly

observed connection to steganography, there has been very little effort aimed at analyzing

or evaluating the effectiveness of common robust watermarking techniques for

steganographic applications. Instead, most work has focused on analyzing or evaluating

the watermarking algorithms for their robustness against various kinds of attacks that try

to remove or destroy them. However, if robust digital watermarks are to be used in active

warden steganography applications, detection of their presence by an unauthorized agent

defeats their very purpose. Even in applications that do not require hidden

communication, but only robustness, we note that it would be desirable to first detect the

possible presence of a watermark before trying to remove or manipulate it. This means

that a given signal would have to be first analyzed for the presence of a watermark. In

this project, we develop steganalysis techniques both for conventional LSB-like

embedding used in the context of a passive warden model and for watermarking which

can be used to embed secret messages in the context of an active warden. In order to

distinguish between these two models, we will be using the terms watermark and

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message when the embedded signal is in the context of an active warden and a passive

warden, respectively.

Furthermore, we simply use the terms marking or embedding when the context of

discussion is general to include both active and passive warden steganography. The

techniques we present are novel and to the best of our knowledge, the first attempt at

designing general purpose tools for steganalysis. General detection techniques as applied

to steganography have not been devised and methods beyond visual inspection and

specific statistical tests for individual techniques like LSB embedding are not present in

the literature. Since too many images have to be inspected visually to sense hidden

messages, the development of a technique to automate the detection process will be very

valuable to the steganalyst. Our approach is based on the fact that hiding information in

digital media requires alterations of the signal properties that introduce some form of

degradation, no matter how small. These degradations can act as signatures that could be

used to reveal the existence of a hidden message. For example, in the context of digital

watermarking, the general underlying idea is to create a watermarked signal that is

perceptually identical but statistically different from the host signal. A decoder uses this

statistical difference in order to detect the watermark. However, the very same statistical

difference that is created could potentially be exploited to determine if a given image is

watermarked or not.

1.6 Statement of Problem:

The goals for this Project have been the following.

One goal has been to compile an introduction to the subject of steganography and

steganalysis. There exist a number of studies on various algorithms, but complete

treatments on a technical level are not as common. Material from papers, journals, and

conference proceedings are used that best describe the various parts.

Another goal has been to search for algorithms that can be used to implement for

the detection of steganographic techniques.

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A third goal is to evaluate their performance of with different image quality

metrics. These properties were chosen because they have the greatest impact on the

detection of steganography algorithms

A final goal has been to design and implement the steganalysis detector in

MATLAB.

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CHAPTER 2

WAVELET TRANSFORM

2.1 Overview:

Wavelets are mathematical functions defined over a finite interval and having an

average value of zero that transform data into different frequency components,

representing each component with a resolution matched to its scale.

The basic idea of the wavelet transform is to represent any arbitrary function as a

superposition of a set of such wavelets or basis functions. These basis functions or baby

wavelets are obtained from a single prototype wavelet called the mother wavelet, by

dilations or contractions (scaling) and translations (shifts). They have advantages over

traditional Fourier methods in analyzing physical situations where the signal contains

discontinuities and sharp spikes. Many new wavelet applications such as image

compression, turbulence, human vision, radar, and earthquake prediction are developed in

recent years. In wavelet transform the basis functions are wavelets. Wavelets tend to be

irregular and symmetric. All wavelet functions, w(2kt - m), are derived from a single

mother wavelet, w(t). This wavelet is a small wave or pulse like the one shown in Figure

2.1.

Normally it starts at time t = 0 and ends at t = T. The shifted wavelet w(t - m)

starts at t = m and ends at t = m + T. The scaled wavelets w(2kt) start at t = 0 and end at t

= T/2k. Their graphs are w(t) compressed by the factor of 2k as shown in Figure 2.2. For

example, when k = 1, the wavelet is shown in Figure 2.2(a). If k = 2 and 4, they are

shown in (b) and (c), respectively.

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Figure 2.1 Mother wavelet w(t)

(a)w(2t) (b)w(4t) (c)w(8t)

Figure 2.2 Scaled wavelets

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The wavelets are called orthogonal when their inner products are zero. The

smaller the scaling factor is, the wider the wavelet is. Wide wavelets are comparable to

low-frequency sinusoids and narrow wavelets are comparable to high-frequency

sinusoids.

2.2 Scaling:

Wavelet analysis produces a time-scale view of a signal. Scaling a wavelet simply

means stretching (or compressing) it. The scale factor is used to express the compression

of wavelets and often denoted by the letter a. The smaller the scale factor, the more

“compressed” the wavelet. The scale is inversely related to the frequency of the signal in

wavelet analysis.

2.3 Shifting:

Shifting a wavelet simply means delaying (or) hastening its onset.

Mathematically, delaying a function f(t) by k is represented by: f(t-k) and the schematic

is shown in Figure 2.3.

(a) Wavelet function (t) (b) Shifted wavelet function (t-k)

Figure 2.3 Shifting of Wavelets

2.4 Scale and Frequency:

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The higher scales correspond to the most “stretched” wavelets. The more

stretched the wavelet, the longer the portion of the signal with which it is being

compared, and thus the coarser the signal features being measured by the wavelet

coefficients. The relation between the scale and the frequency is shown in Figure 2.4.

Low scale High scale

Figure 2.4 Scale and frequency of Wavelets

2.5 Discrete Wave Transform:

Calculating wavelet coefficients at every possible scale is a fair amount of work,

and it generates an awful lot of data. If the scales and positions are chosen based on

powers of two, the so-called dyadic scales and positions, then calculating wavelet

coefficients are efficient and just as accurate. This is obtained from discrete wavelet

transform (DWT).

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2.5.1 One-Stage Filtering:

For many signals, the low-frequency content is the most important part. It is the

identity of the signal. The high-frequency content, on the other hand, imparts details to

the signal. In wavelet analysis, the approximations and details are obtained after filtering.

The approximations are the high-scale, low frequency components of the signal. The

details are the low-scale, high frequency components. The filtering process is

schematically represented as in Figure2.5.

Figure 2.5 Single stage filtering

The original signal, S, passes through two complementary filters and emerges as

two signals. Unfortunately, it may result in doubling of samples and hence to avoid this,

downsampling is introduced. The process on the right, which includes downsampling,

produces DWT coefficients. The schematic diagram with real signals inserted is as shown

in Figure 2.6.

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Figure 2.6 Decomposition and decimation

2.5.2 Multiple-Level Decomposition:

The decomposition process can be iterated, with successive approximations being

decomposed in turn, so that one signal is broken down into many lower resolution

components. This is called the wavelet decomposition tree and is depicted as in Figure

2.7.

Figure 2.7 Multilevel decomposition

2.5.3 Wavelet Reconstruction:

The reconstruction of the image is achieved by the inverse discrete wavelet

transform (IDWT). The values are first upsampled and then passed to the filters. This is

represented as shown in Figure 2.8.

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Figure 2.8 Wavelet Reconstruction

The wavelet analysis involves filtering and downsampling, whereas the wavelet

reconstruction process consists of upsampling and filtering. Upsampling is the process of

lengthening a signal component by inserting zeros between samples as shown in Figure

2.9.

Figure 2.9 Reconstruction using up sampling

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2.5.4 Reconstructing Approximations and Details:

It is possible to reconstruct the original signal from the coefficients of the

approximations and details. The process yields a reconstructed approximation which has

the same length as the original signal and which is a real approximation of it.

The reconstructed details and approximations are true constituents of the original

signal. Since details and approximations are produced by downsampling and are only half

the length of the original signal they cannot be directly combined to reproduce the signal.

It is necessary to reconstruct the approximations and details before combining them. The

reconstructed signal is schematically represented as in Figure 2.10.

Figure 2.10 Reconstructed signal components

2.6 1-D Wavelet Transform:

The generic form for a one-dimensional (1-D) wavelet transform is shown in

Figure 2.11. Here a signal is passed through a low pass and high pass filter, h and g,

respectively, then down sampled by a factor of two, constituting one level of transform.

Figure 2.11 1-D Wavelet Decomposition

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Repeating the filtering and decimation process on the lowpass branch outputs

make multiple levels or “scales” of the wavelet transform only. The process is typically

carried out for a finite number of levels K, and the resulting coefficients are called

wavelet coefficients.

The one-dimensional forward wavelet transform is defined by a pair of filters s

and t that are convolved with the data at either the even or odd locations. The filters s and

t used for the forward transform are called analysis filters.

nL nH

li = ∑ sjx2i+j and hi = ∑ tjx2i+1+j

j=-nl j=-nH

Although l and h are two separate output streams, together they have the same

total number of coefficients as the original data. The output stream l, which is commonly

referred to as the low-pass data may then have the identical process applied again

repeatedly. The other output stream, h (or high-pass data), generally remains untouched.

The inverse process expands the two separate low- and high-pass data streams by

inserting zeros between every other sample, convolves the resulting data streams with

two new synthesis filters s’ and t’, and adds them together to regenerate the original

double size data stream.

nH nl

yi = t’jl’

i+j + s’j h’

i+j where l’2i = li, l’ 2i+1 = 0

j= -nH j= -nH h’2i+1 = hi, h’2i = 0

To meet the definition of a wavelet transform, the analysis and synthesis filters s,

t, s’ and t’ must be chosen so that the inverse transform perfectly reconstructs the original

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data. Since the wavelet transform maintains the same number of coefficients as the

original data, the transform itself does not provide any compression. However, the

structure provided by the transform and the expected values of the coefficients give a

form that is much more amenable to compression than the original data. Since the filters

s, t, s’ and t’ are chosen to be perfectly invertible, the wavelet transform itself is lossless.

Later application of the quantization step will cause some data loss and can be used to

control the degree of compression. The forward wavelet-based transform uses a 1-D sub-

band decomposition process; here a 1-D set of samples is converted into the low-pass

sub-band (Li) and high-pass sub-band (Hi). The low-pass sub-band represents a down

sampled low-resolution version of the original image. The high-pass sub-band represents

residual information of the original image, needed for the perfect reconstruction of the

original image from the low-pass sub-band

2.7 2-D Transform Hierarchy:

The 1-D wavelet transform can be extended to a two-dimensional (2-D) wavelet

transform using separable wavelet filters. With separable filters the 2-D transform can be

computed by applying a 1-D transform to all the rows of the input, and then repeating on all of

the columns.

LL1 HL1

LH1 HH1

Figure 2.12 Sub-band Labeling Scheme for a one level, 2-D Wavelet Transform

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The original image of a one-level (K=1), 2-D wavelet transform, with

corresponding notation is shown in Figure 2.12. The example is repeated for a three-level

(K =3) wavelet expansion in Figure 2.13. In all of the discussion K represents the highest

level of the decomposition of the wavelet transform.

LL1 HL1

HL2

HL3

LH1 HH1

LH2 HH2

LH3 HH3

Figure 2.13 Sub-band labeling Scheme for a Three Level, 2-D Wavelet Transform

The 2-D sub-band decomposition is just an extension of 1-D sub-band

decomposition. The entire process is carried out by executing 1-D sub-band

decomposition twice, first in one direction (horizontal), then in the orthogonal (vertical)

direction. For example, the low-pass sub-bands (Li) resulting from the horizontal

direction is further decomposed in the vertical direction, leading to LLi and LHi sub-

bands.

Similarly, the high pass sub-band (Hi) is further decomposed into HLi and HHi.

After one level of transform, the image can be further decomposed by applying the 2-D

sub-band decomposition to the existing LLi sub-band. This iterative process results in

multiple “transform levels”. In Figure 2.13 the first level of transform results in LH 1,

HL1, and HH1, in addition to LL1, which is further decomposed into LH2, HL2, HH2, LL2

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at the second level, and the information of LL2 is used for the third level transform. The

sub-band LLi is a low-resolution sub-band and high-pass sub-bands LHi, HLi, HHi are

horizontal, vertical, and diagonal sub-band respectively since they represent the

horizontal, vertical, and diagonal residual information of the original image. An example

of three-level decomposition into sub-bands of the image CASTLE is illustrated in Figure

2.14.

Figure 2.14 The process of 2-D wavelet transform applied through three transform levels

To obtain a two-dimensional wavelet transform, the one-dimensional transform is

applied first along the rows and then along the columns to produce four sub-bands: low-

resolution, horizontal, vertical, and diagonal. (The vertical sub-band is created by

applying a horizontal high-pass, which yields vertical edges.) At each level, the wavelet

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transform can be reapplied to the low-resolution sub-band to further decorrelate the

image. Figure 2.14 illustrates the image decomposition, defining level and sub-band

conventions used in the AWIC algorithm. The final configuration contains a small low-

resolution sub-band. In addition to the various transform levels, the phrase level 0 is used

to refer to the original image data.

2.8 Wavelet Computation:

In order to obtain an efficient wavelet computation, it is important to eliminate as

many unnecessary computations as possible. A careful examination of the forward and

reverse transforms shows that about half the operations either lead to data which are

destroyed or are null operations (as in multiplication by 0).

The one-dimensional wavelet transform is computed by separately applying two

analysis filters at alternating even and odd locations. The inverse process first doubles the

length of each signal by inserting zeros in every other position, then applies the

appropriate synthesis filter to each signal and adds the filtered signals to get the final

reverse transform.

2.9 Algorithms and Transformations:

Another steganography method is to hide data in mathematical functions that are

in compression algorithms. Two functions are Discrete Cosine Transformation (DCT)

and Wavelet Transformation. The DCT and wavelet functions transform data from one

domain into another. The DCT function transforms that data from a spatial domain to a

frequency domain.

Figure 2.15 Discrete Cosine Transformation (DCT)

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The DCT function:

. The idea behind it in regard to steganography is to hide the data bits in the least

significant coefficients.

2.10 To Encode the Hidden Data:

Figure 2.16 Graphical representation to encode the hidden data

Take the DCT or wavelet transform of the cover image

Find the coefficients below a certain threshold

Replace these bits with bits to be hidden (can use LSB insertion)

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Take the inverse transform

Store as regular image

2.11 To Decode the Hidden Data:

Figure 2.17 Graphical representation to encode the hidden data

Take the transform of the modified image

Find the coefficients below a certain threshold

Extract bits of data from these coefficients

Combine the bits into an actual message

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CHAPTER 3

METHODS OF STEGANOGRAPHY

3.1 Adaptive Steganography Using Filtering:

Adaptive Steganography reduces modifications to the image, and adapts the

message embedding technique to the actual content and features of the image. In general,

to keep a good degree of stealth-ness, Adaptive methods embed message bits into certain

random clusters of pixels (avoiding areas of uniform color) selecting pixels with large

local standard deviation or image blocks containing a number of different colors. The

main advantage of adaptive steganography is that the changes made to the cover image

take into account the sensitivity of the human visual system and also various statistical

parameters generally being used by steg-analysis algorithms. The main challenge posed

to existing adaptive steganography techniques is that the methods so far developed

doesn't seem to have a way to control the amount of information that is to be hidden, for a

given cover image. This problem is overcome in the method presented in this paper.

The proposed approach utilizes the sensitivity of the human visual system to

adaptively modify the intensities of some pixels in a high frequency components spatial

image (HFSI) of the cover image. The modification of pixel intensities depends on the

magnitude of the pixels in HFSI and also on the local features of the cover image. If the

contrast of the image is large (e.g., an edge), the intensities can be changed greatly

without introducing any distortion to human eyes. On the other hand, if the contrast is

small (e.g. a smooth), the intensities can only be tuned slightly. In this method, first the

cover image is passed through a filter to separate the high and low frequency components

of the image. The inverse transform of both the images is computed. Now the pixels

values of HFSI are modified depending on the magnitude of the pixel i.e. more the

magnitude more the Least Significant Bits (LSB's) of that pixel are changed and also the

local features of cover image are considered. Now both the LFSI (Low Frequency

components spatial image of cover image) and HFSI are added to form the stego - image.

At the receiver the reverse process is to be done to recover the message.

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There are four different methods of Steganography:

Least Significant Bit Insertion in Transformation Domain

Least Significant Bit Insertion in Spatial Domain

Reversible Data Embedding using Difference Expansion

Reversible Data Hiding

3.2 Introduction to LSB:

Maintaining the secrecy of digital information when being communicated over the

Internet is presently a challenge. Given the amount of cheap computation power available

and certain known limitations of the encryption methods it is not too difficult to launch

attacks on cipher-text. An ideal steganographic technique embeds message information

into a carrier image with virtually imperceptible modification of the image. Adaptive

steganography comes closer to this ideal since it exploits the natural variations in the

pixel intensities of a cover image to hide the secret message. The objective of

steganography is a method of embedding additional information into the digital contents

that is undetectable to listeners. We are investigating its embedding, detecting, and

coding techniques.

The idea behind the LSB algorithm is to insert the bits of the hidden message into

the least significant bits of the pixels. As the application domain of embedding data in

digital multimedia sources becomes broaden, several terms are used by various groups of

researchers, including steganography, digital watermarking, and data hiding. This paper

introduces a new, principled approach to detecting least significant bit (LSB)

steganography in digital signals such as images and audio. It is shown that the length of

hidden messages embedded in the least significant bits of signal samples can be estimated

with relatively high precision. The new steganalytic approach is based on some statistical

measures of sample pairs that are highly sensitive to LSB embedding operations. The

resulting detection algorithm is simple and fast. To evaluate the robustness of the

proposed steganalytic approach, bounds on estimation errors are developed. Furthermore,

the vulnerability of the new approach to possible attacks is also assessed, and counter

27

measures are suggested A detailed algorithm is presented along with results of its

application on some sample images.

3.3 Advantages of LSB Insertion:

A major advantage of the LSB algorithm is it is quick and easy. There has also

been steganography software developed which work around LSB color alterations via

palette manipulation. LSB insertion also works well with gray-scale images.

3.4 LSB Insertion in Transformation Domain:

The idea behind the LSB algorithm is to insert the bits of the hidden message into

the least significant bits of the pixels.

 Simplified Example with a 24 bit pixel:

  1 pixel:

(00100111 11101001 11001000)

  Insert 101:

(00100111 11101000 11001001)

Red green blue

 Simplified Example with an 8 bit pixel:

1 pixel:

(00 01 10 11)

White red green blue

  Insert 0011:

(00 00 11 11)

white white blue blue

28

3.5 LSB Insertion in Spatial Domain:

Usually 24-bit or 8-bit files are used to store digital images. The former one

provides more space for information hiding however, it can be quite large. The colored

representations of the pixels are derived from three primary colors: red, green and blue.

24-bit images use 3 bytes for each pixel, where each primary color is represented by 1

byte. Using 24-bit images each pixel can represent 16,777,216 color values. We can use

the lower two bits of these color channels to hide data, then the maximum color change in

a pixel could be of 64-color values, but this causes so little change that is undetectable for

the human vision system. This simple method is known as Least Significant Bit insertion

Using this method it is possible to embed a significant amount of information with no

visible degradation of the cover image. Figure 3.11 shows the process.

Several versions of LSB insertion exist. It is possible to use a random number

generator initialized with a stego-key and its output is combined with the input data, and

this is embedded to a cover image. For example in the presence of an active warden it is

not enough to embed a message in a known place (or in a known sequence of bits)

because the warden is able to modify these bits, even if he can’t decide whether there is a

secret message or not, o r he can’t read it because it is encrypted. The usage of a stego-

key is important, because the security of a protection system should not be based on the

secrecy of the algorithm itself, instead of the choice of a secret key. Figure 3.2 shows this

process. The LSB inserting usually operates on bitmap images. ‘Steganos for Windows’

and ‘Wbstego’ are LSB inserting software products which are able to embed data (in

clear or encrypted format) in a bitmap image. The embedded data cannot be considered

as a watermark, because even if a small change occurs in a picture (cropping, lossy

compression, color degradation) the embedded information will be lost – although the

change which is occurred during the embedding process is invisible.

29

Figure 3.1 Graphical representation of LSB insertion

Figure 3.2 Stego Image formation through LSB insertion

30

3.5.1 Algorithm:

Step 1: Let the cover image is represented by c(x,y). It is then passed through a filter

with transfer function h(x,y) to separate high and low frequency components.

F[c(x, y)] = C(X, Y)

Where C(X, Y) represents Fourier Transform of the cover image. In this paper capital

letters representation for pixel is used for frequency domain and small letters for spatial

Representation.

C(X, Y)H(X, Y) = LO(X, Y) + HI(X, Y)

Where LO(X, Y), HI(X, Y) represent low frequency and high frequency components of

cover image respectively, obtained after passing through the filter with cut off as stated

above.

Step 2: Inverse transform of both the frequency components is found out, known as HFSI

(High Frequency components Spatial Image) and LFSI (Low Frequency components

Spatial Image) separately.

F1[LO(X, Y)]= lo(x, y) and F-1 [HI(X, Y)] = hi(x, y)

Where lo(xy) and hi(x,y) are the spatial components of low and high frequencies in the

cover image respectively.

Step 3: Now message is embedded into HFSI image. The number of bits modified in a

pixel is made to depend up on its magnitude and also on the local features of the cover

image. Let the message is represented as m(x,y) and the embedding function as M[].

mlo(x, y) = M[lo(x, y) + m(x, y)]

Step 4: Both the modified HFSI and unmodified LFSI are added to form stego - image.

Steg(x, y) = mlo(x, y) + lo(x, y)

Step5: At the receiver LFSI is subtracted from stego - image leaving modified HFSI

image.

31

mhi(x, y) = steg(x, y) - hi(x, y)

Step 6: Now the message is decoded from the Modified HTSI image using the stego –

key.

m(x, y) + lo(x, y) = M '[mhi(x, y)]

3.6 Reversible Data Embedding:

This type of reversible steganography directly modifies image pixels in the spatial

domain to achieve reversibility. Since this technique is easy to implement, offer a

relatively high hiding capacity, and the quality of the cover image can be easily

controlled, it has become a popular method for reversible steganography.

Reversible data embedding, which is also called lossless data embedding, embeds

invisible data (which is called a payload) into a digital image in a reversible fashion. As a

basic requirement, the quality degradation on the image after data embedding should be

low. An intriguing feature of reversible data embedding is the reversibility, that is, one

can remove the embedded data to restore the original image. From the information hiding

point of view, reversible data embedding hides some information in a digital image in

such a way that an authorized party could decode the hidden information and also restore

the image to its original, pristine state. The performance of a reversible data-embedding

algorithm can be measured by the following.

1) Payload capacity limit: what is the maximal amount of information can be embedded?

2) Visual quality: how is the visual quality on the embedded image?

3) Complexity: what is the algorithm complexity?

The motivation of reversible data embedding is distortion- free data embedding.

Though imperceptible, embedding some data will inevitably change the original content.

Even a very slight change in pixel values may not be desirable, especially in sensitive

imagery, such as military data and medical data. In such a scenario, every bit of

information is important. Any change will affect the intelligence of the image, and the

access to the original, raw data is always required. From the application point of view,

32

reversible data embedding can be used as an information carrier. Since the difference

between the embedded image and original image is almost imperceptible from human

eyes, reversible data embedding could be thought as a covert communication channel. By

embedding its message authentication code, reversible data embedding provides a true

self authentication scheme, without the use of metadata. In this paper, we present a high-

capacity, high visual quality, reversible data-embedding method for digital images.

Our method can be applied to digital audio and video as well. We calculate the

differences of neighboring pixel values, and select some difference values for the

difference expansion (DE). The original content restoration information, a message

authentication code, and additional data (which could be any data, such as date/time

information, auxiliary data, etc.) will all be embedded into the difference values. In this

paper we will consider grayscale images only. For color images, there are several options.

One can decorrelate the dependence among different color components by a reversible

color conversion transform, and then reversibly embed the data in the decorrelated

components. Or one can reversibly embed each color component individually. Please

note that reversible data embedding is a fragile technique When the embedded image is

manipulated and/or lossy compressed, the decoder will find out it is not authentic and

thus there will be no original content restoration.

3.7 Reversible Data Hiding:

Data Hiding is referred to as a process to hide data (representing some

information) into cover media. That is, the data hiding process links two sets of data, a set

of the embedded data and another set of the cover media data. The relationship between

these two sets of data characterizes different applications. For instance, in covert

communications, the hidden data may often be irrelevant to the cover media. In

authentication, however, the embedded data are closely related to the cover media. In

these two types of applications, invisibility of hidden data is an important requirement. In

most cases of data hiding, the cover media will experience some distortion due to data

hiding and cannot be inverted back to the original media. That is, some permanent

distortion has occurred to the cover media even after the hidden data have been extracted

out. In some applications, such as medical diagnosis and law enforcement, it is critical to

33

reverse the marked media back to the original cover media after the hidden data are

retrieved for some legal considerations.

In other applications, such as remote sensing and high-energy particle physical

experimental investigation, it is also desired that the original cover media can be

recovered because of the required high-precision nature. The marking techniques

satisfying this requirement are referred to as reversible, lossless, distortion-free, or

invertible data hiding techniques. Reversible data hiding facilitates immense possibility

of applications to link two sets of data in such a way that the cover media can be

losslessly recovered after the hidden data have been extracted out, thus providing an

additional avenue of handling two different sets of data.

Obviously, most of the existing data hiding techniques are not reversible. For

instance, the widely utilized spread-spectrum based data hiding methods are not

invertible owing to truncation (for the purpose to prevent over/underflow) error and

round-off error. The well-known least significant bit plane (LSB) based schemes and are

not lossless owing to bit replacement without “memory.” Another category of data

hiding techniques, quantization-index-modulation (QIM) based schemes and , are not

distortion-free owing to quantization error.

Recently, some reversible marking techniques have been reported in the literature.

The first method is carried out in the spatial domain. It uses modulo 256 addition

(assuming here that eight-bit grayscale images are considered) to embed the hash value of

the original image for authentication. The embedding formula is Iω =(I+W)mod(256), in

which I denotes the original image, Iω the marked image, and W = W(H(I),K) the

watermark, where H(I) denotes the hash function operated on the original image I, and K

the secret key. Because of using modulo 256 additions, the over/underflow is prevented

and the reversibility is achieved. Some annoying salt-and-pepper noise, however, is

generated owing to possible grayscale value flipping over between 0 and 255 in either

direction during the modulo 256 addition.

The second reversible marking technique was developed in the transform domain,

which is based on a lossless multi resolution transform and the idea of patchwork. It also

34

uses modulo 256 addition. Note that no experimental results about this technique have

been reported. Another spatial domain technique was reported in that losslessly

compresses some selected bit plane(s) to leave space for data embedding. Because the

necessary bookkeeping data are also embedded in the cover media as an overhead, the

method is reversible.

Since these techniques aim at authentication, the amount of hidden data is limited.

The capacity of method, which is based on the idea of patchwork and modulo 256

addition, is also limited except that the hidden data exhibit some robustness against high

quality JPEG compression. Since it uses modulo 256 addition, it also suffers from salt-

and-pepper noise. As a result, the technique cannot be utilized in many applications. This

observation is valid to all lossless data hiding algorithms that use modulo 256 addition to

achieve reversibility.

The first reversible marking technique that is suitable for a large amount of data

hiding was presented. This technique first segments an image into non-overlapping

blocks, and then introduces a discriminating function to classify these blocks into three

groups: R(egular), S(ingular), and U(nusable). It further introduces a flipping operation,

which can convert an R-block to an S-block and vice versa. A U-block remains intact

after the flipping operation. By assigning, say, binary 1 to an R-block and binary 0 to an

S-block, all R- and S-blocks are scanned in a chosen sequential order, resulting in a

biased (meaning that the binary numbers of 1 and 0 are not balanced) binary sequence.

This biased binary sequence is losslessly compressed to leave space for data embedding

and the compressed bit sequence is embedded into the cover media as an overhead for

later reconstruction of the original image. In data embedding, the R- and S-blocks are

scanned once again and the flipping operation is applied whenever necessary to make the

changed R- and S-block sequence coincident with the to-be-embedded data followed by

the overhead data mentioned above. While it is novel and successful in reversible data

hiding, the payload is still not large enough for some applications. Specifically, the

embedding capacity estimated by authors ranges from 3 to 41 kb for a 512 *512 *8 cover

grayscale image when the embedding amplitude is 4 (the estimated average PSNR of the

marked image versus the original image is 39 dB) .

35

Another problem with the method is that when the embedding strength increases

in order to increase the payload, the visual quality of the marked image will drop severely

due to annoying artifacts. To increase the payload dramatically, a new lossless data

hiding technique based on integer wavelet transform (IWT) (a second generation wavelet

transform, which has avoided round-off errors) was developed recently. Because of the

superior decorrelation capability of wavelet transform, the selected bit plane compression

of IWT coefficients in high frequency sub-bands creates more space for data hiding,

resulting in a two to five times payload as large. Specifically, its payload ranges from 15

to 94 kb for a 512 512 8 grayscale image at the same (39 dB) PSNR of the marked

images compared with the original images.

To achieve reversible data hiding, a histogram modification is applied in its pre-

processing to prevent over/underflow. This histogram modification causes, however, a

relatively low PSNR of the marked image versus the original image though there are no

annoying artifacts. It is noted that reversible data hiding has attracted increasing attention

recently, and more algorithms are being developed. Then the authors adopt the CALIC

lossless image compression algorithm, with the quantized values as side information, to

efficiently compress the quantization residuals to create high capacity for the payload

data.

The compressed residual and the payload data are concatenated and embedded

into the host signal via generalized-LSB modification method. The payload of this

technique is from 15 to 143 kb for a 512 512 8 grayscale image while the PSNR is

38 dB. Even though the payload is high, the PSNR is still not high enough. In this paper,

we propose a new reversible data embedding technique, which can embed a large amount

of data (5–80 kb for a 512 512 8 grayscale image) while keeping a very high visual

quality for all natural images, specifically, the PSNR of the marked image versus the

original image is guaranteed to be higher than 48 dB. It utilizes the zero or the minimum

point of the histogram (defined below) and slightly modifies the pixel

36

Figure 3.3 Histogram of an image

grayscale values to embed data. This technique can be applied to virtually all types of

images. Up to now, it has been successfully tested on different types of images, including

some commonly used images, medical images, texture images, aerial images, and all of

the 1096 images in CorelDraw database. The computation of our proposed technique is

quite simple and the execution time is rather short. Although the proposed lossless data

hiding technique is applied to still images, it is also applicable to videos which consist of

a sequence of images.

3.8 Drawbacks of Existing Techniques:

1. In LSB Technique only you are going to hide in least significant only. So, anyone can

easily retrieve the secret information.

2. Compressed domain reversible steganography often suffers from high computational

cost, low hiding capacity, and low stego-image quality.

3. After hiding the data in image by using LSB algorithm, if someone takes the Image

and compress the image means definitely lsb value will be change.

4. So we can’t get the original data what we hided.

37

CHAPTER 4

LOSSLESS STEGANOGRAPHY BASED ON AMBTC

COMPRESSION

4.1 Introduction:

Information hiding is a technique that inserts secret messages into a cover file, so

that the existence of the messages is not apparent. Research in information hiding has

tremendous increased during the past decade with commercial interests. Information

hiding techniques that are used today include watermarking and steganography. The

major concern of watermarking is to protect the ownership of a digital content, while

steganography is to embed secret messages into digital content so that the secret

messages are not detectable. All digital media, such as digital images, videos and audio

files, can be used to hide secret messages. However, digital images are often used for

steganography. This is because nature images usually have higher degree of redundancy,

which are suitable to embed information without degrading the visual quality of the

images. Moreover, images are widely used throughout the internet, which usually arouse

little suspicion than other digital media. Although many steganography techniques have

been developed for digital images, most of them are irreversible. That is, the original

image cannot be recovered to its original state after the extraction of the secret data.

At present, there is only a small amount of literature dealing with reversible data

hiding. However, most of them are focused on spatial domain; rare of them addressed the

reversibility on the compressed domain. In 2006, Chang and Lin proposed a reversible

data embedding mechanism for VQ compressed images.

They used side matching and relocation techniques to achieve reversibility

without using the location map. However, the computational cost for their method is

high, and is not suitable for real-time applications. In 2007, Chang et al. proposed a new

reversible data hiding technique in the VQ-compressed domain further, and the newly

proposed method has the benefit of high efficiency of the embedding and extraction

38

process. However, the quality of the stego-image depends largely on a specially designed

codebook, and distortions of the stego-image may become unacceptable if a poor

codebook is selected.

In this paper, we propose a new lossless steganography scheme that embeds secret

data into AMBCT-compressed images. AMBTC is a well known lossy, block based

compression scheme. Each image block is compressed by using two quantization levels

and one bit plane. Due to its low complexity, low computational cost, and easy to

implement, AMBTC has gained wide interest in its further development, including image

compression and steganography. In our scheme, we will take the advantage of the order

of two quantization levels to hide secret data and achieve reversibility.

4.2 Block Diagram:

Figure 4.1 Block diagram of Secret Communication using Steganography

4.3 AMBTC Compression Technique:

The concept of absolute moment block truncation coding (AMBTC) was

introduced by Lema and Mitchell in 1984, and the goal of AMBTC is to preserve the

mean and the first absolute central moment of image blocks. The AMBTC encoding and

decoding procedures are briefly described in the following. In the encoding procedure of

AMBTC, the original host image O is first partitioned into a set of non-overlapping

39

Cover Image

Secret

Data

AMBTC

Compression

Embedding in Trio

Stego

Image

Extraction from Stego

Secret

Data

blocks of n× n pixels. These image blocks can be viewed as k-dimensional vectors, where

k = n× n. Each image block is then compressed by using two quantization levels a i and bi,

and one bit plane B. Let oi be image block i, oi1,oi2,oi3,….oik be the pixels of image block

i, the two quantization levels ai and bi of AMBTC for block i can be calculated as follow:

Here qi denotes the number of pixels having a value higher than or equal to the

block mean oi. Once the two quantization levels, i.e., lower mean ai and higher mean bi

have been obtained, the bit plane for image

block i can be constructed by investigating pixel values within block i. If a pixel value ij

o is greater or equal to the mean value ci, then bij is set to 1. Otherwise, bij is set to 0. Each

image block is encoded in the same manner to construct a sequence of compressed code

ai, bi, Bi, i =1, 2…..z, where z is the total number of blocks to be encoded. Hence the

output of AMBTC for each block includes two quantization levels a and b; and one bit

plane B which specifies the state, a or b, for each output pixel.

An example for AMBTC encoding procedures are shown in Figure 4.2.

Figure 4.2(a) shows an image block of 4× 4 pixels. To encode this block, the mean of this

block is calculate as o = 108. The value o is then taken as a threshold to generate a bit

plane B, as shown in Figure 4.2(b). Pixels with values higher than or equal to o have a

corresponding bit valued 1 stored in the bit plane. Otherwise, bit valued 0 is stored. Two

rounded quantization levels a = 84 and b = 132 can be calculated using Equations (1) and

(2).

Once the quantization levels and the bit plane have been calculated, the

compressed code of this image block comes out as a trio of [84,132, (1000 1100 1100

40

1110)] . All image blocks are encoded with the same manner, until all the blocks are

processed.

Figure 4.2 Example of AMBTC encoding and decoding procedures

To decode an AMBTC-compressed image, the decoder reconstructs image blocks

from the compressed code. To recover an image block, if a value i b in B is 0, then the

corresponding pixel is reconstructed by the quantization level a, Otherwise, reconstructed

by the quantization level b. By recovering pixels in all image blocks, the whole

compressed image can be reconstructed. Figure 4.2(c) shows an example of AMBTC

decoded image block using the trio [84,132, (1000 1100 1100 1110)].

4.4 Formation of Trio:

In this section, a lossless steganography for AMBTC-compressed images is

introduced. For convenience, we defined the notations used in this paper first. The

original host image O is a grayscale image. The AMBTC-compressed code for O is

denoted by C, and the reconstructed AMBTC compressed image is denoted by E.

Embedding is done by modifying C, and the result is an AMBTC compressed stego-code

C’. The receiver then uses the restoring process to obtain the AMBTC-compressed stego-

image E’.

41

The AMBTC compressed code C consists of a sequence of trios (two quantization

level a and b, and a bit plane B). Each trio (a, b, B) represents the compressed code for an

image block. Note that if we interchange two quantization levels a and b, and perform

Logical NOT operation on the bit plane B, the reconstructed AMBTC image blocks will

remain the same. Let R() denotes the reconstruction function for AMBTC compressed

image blocks with 3 parameters a, b and B, then the following equation always hold true

for every trio (a, b, B) :

R(a,b, B) ≡ R(b,a, )

where B is the resultant of the Logical NOT operation on the bit plane B. Equation

3 implies that the reconstructed image E and the recovered stego-image E’ are exactly the

same, i.e. E ≡ E′ .

Since the interchange of the quantization levels together with logical NOT

operation on bit the plane B does not change the value of decoded image blocks, we may

adopt this property to embedded one bit into each trio without losing any image quality.

The embedding procedures are described in the following section.

4.5 Data embedding:

The process of producing a stego-image is described below. Suppose an AMBTC

compressed code C, to be embedded, is composed of N × N trios. The embedding

algorithm of the proposed method follows the steps listed below:

Step 1. The m-bits secret data S ( m ≤ N × N ) is first appended by 0’s of size N × N − m ,

and then XORed with a random binary sequence generated by a secret key k . The

encrypted bit stream is denoted by

.

Step 2. Sequentially embed secret data into AMBTC encoded blocks. For each AMBTC

encoded block I with trio , if the corresponding embedded bit ei =1 , then the

42

trio is changed to . Otherwise, leave the trio remain

unchanged.

Step 3. Repeat step 1 and step 2 until the entire encrypted bit stream E is embedded.

4.6 Data extraction:

The data extraction procedures are similar to that of the embedding procedures.

For each AMBTC compressed block i, if i i a < b, then the embedded secret bit ei =0 is

extracted. Otherwise, ei =1 is extracted. This procedure is repeated until all the encrypted

secret data Ebs have been extracted, and then decrypted using the secret key k to obtain

the original secret data S.

4.7 Quality Measurements:

The peak signal to noise ratio (PSNR) is used to measure the distortion between the

original image and the stego image. The computation of PSNR is defined as

Here ij x denotes the original pixel value, and x ij denotes the processed pixel value. To

investigate the performance of our scheme, we compare the stegoimage quality,

recovered image quality.

43

CHAPTER 5

DATA FLOW DIAGRAM

5.1 Flow Chart:

44

Selection of Cover Image

Block Separation

Trio Formation

Embedding

Stego Image Formation

Secret Data

Sender (User)

Figure 5.1 Formation of a stego image

45

Receiving of Stego Image

Block Separation

Trio Formation

Extraction

Original Image

Secret Data

Sender (User)

Figure 5.2 Extraction of a stego image

CHAPTER 6

MATLAB

6.1 Introduction:

The past decade has been a tremendous escalation in the use of computers. There

is a wealth of software packages available for analysis, design and manufacture of

devices, equipment, machinery and systems. There is no doubt that we have familiar with

computing techniques and have an awareness of these software packages. MATLAB is

such a software package that serves a vehicle for analysis and performance of a system.

MATLAB is a software package for high performance numeric computation and

data visualization. Fundamentally, it is built upon a foundation of sophisticated matrix

software for analyzing linear system of equations.

6.2 Features:

A very strong programming environment, which is reasonably interactive.

A high level matrix language with control flow statements, functions, data

structures, I/O, OOP features.

Good plotting features and graphics handling techniques (3D) used for image

processing, animation and in visual interfacing.

Rich of libraries such a as Application Program that interface (API) that allows

you to write C and FORTRAN Programs that interact with MATLAB.

Rich of computational algorithms for simple functions like sum, since to complex

functions like FET, Bessel’s function etc.

46

There are several ‘Tool boxes’ available for users of MATLAB. These tool boxes

are collections of functions written for special applications such as statistics,

communications, and control system design, signal processing and neural

networks.

MATLAB Includes Tools For:

1. Data acquisition.

2. Data analysis and Exploration.

3. Visualization and Image Processing.

4. Algorithm Programming and Development.

5. Modeling and simulation.

6. Programming and Application Development.

These tools allow you to solve the problems in Applied Maths, Physics,

Chemistry, Engineering and Finance – almost any application area that deals with

complex numerical calculations.

When MATLAB is invoked the following windows are present:-

Command Window: This is the main window characterized by the prompt ‘>>’.

All commands, including those for running user written programs are typed here.

Command history: All commands typed on MATLAB prompt in command

window get recorded, even across multiple sessions.

Workspace: It lists all variable that have been generated so far and shows there

type and size.

Edit Window: This where we write, edit, create and save our own programs in

files called ‘M-Files’.

MATLAB for Speech Recognition:

For implementing a speech recognition algorithm, MATLAB is a good choice.

For our application we use the following tool boxes:

1. Signal processing tool box.

47

2. Symbolic math tool box.

3. General MATLAB instructions.

Signal Processing Toolbox:

The signal processing tool box is a specialized collection of M-files built

specifically for signal processing operations, from wave form generation to filter design

and implementation, parametric modeling, and spectral analysis.

The Toolbox provides 2 categories of tools:

Command Line Functions:

1. Analog and Digital filter analysis.

2. Digital filter implementation.

3. FIR and IIR digital filter design.

4. Analog filter design.

5. Filter Discretisation.

6. Spectral Windows transform.

7. Spectral analysis.

8. Statistical signal processing and Spectral analysis.

9. Parametric Modeling.

10. Linear prediction.

11. Waveform Generation.

Interactive Graphical User Interfaces for:

1. Filter design and analysis-FDA tool.

2. Filter visualization.

3. Signal plotting and analysis-WIN tool.

4. Window visualization.

48

5. Signal plotting and analysis, Spectral analysis and filtering signals-SP tool.

6.3 Symbolic MATH Toolbox:

The symbolic math tool box integrates powerful symbolic and variable precision

computing into MATLAB environment. This tool box supplement MATLAB numeric

and graphic facilities with several other types of mathematical computation.

6.4 Image Matrix:

MATLAB handles images as matrices. This involves breaking each pixel

of an image down into the elements of a matrix. MATLAB distinguishes between color

and grayscale images and therefore their resulting image matrices differ slightly.

6.5 Introduction to Programming in MATLAB:

In this worksheet we will continue to learn Matlab programming. The main goal

is that you by completing the worksheet can write your own Runge Kutta 4 ODE-solver.

The worksheet centers around a few examples, and it is important to implement these

examples, run them and carefully compare the output with the code. The prerequisites for

this worksheets are the worksheets Introduction to Matlab and Introduction to

programming in Matlab.

6.6 Functions in MAT LAB:

You have already encountered functions that are built in to Matlab. The sin() is a

function that takes an argument and returns an output. As you start to write your own

programs in Matlab you will need to create your own functions that takes one or more

arguments and does something with these arguments.

Example:

Function y = function name(arguments list)

commands

49

The code above must be written in a separate m-file! The name of the file should coincide

with the name of the function. Remember to save the file with a .m after the file name.

6.7 Source Code:

function varargout = gui(varargin)

gui_Singleton = 1;

gui_State = struct('gui_Name', mfilename, ...

'gui_Singleton', gui_Singleton, ...

'gui_OpeningFcn', @gui_OpeningFcn, ...

'gui_OutputFcn', @gui_OutputFcn, ...

'gui_LayoutFcn', [] , ...

'gui_Callback', []);

if nargin & isstr(varargin{1})

gui_State.gui_Callback = str2func(varargin{1});

end

if nargout

[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else

gui_mainfcn(gui_State, varargin{:});

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end

function gui_OpeningFcn(hObject, eventdata, handles, varargin)

handles.output = hObject;

guidata(hObject, handles);

a=ones(256,256);

axes(handles.axes1);

imshow(a);

axes(handles.axes2);

imshow(a);

axes(handles.axes3);

imshow(a);

function varargout = gui_OutputFcn(hObject, eventdata, handles)

varargout{1} = handles.output;

function browse_Callback(hObject, eventdata, handles)

[filename, pathname] = uigetfile('*.bmp;*.png;*.jpg', 'Pick an Image');

if isequal(filename,0) | isequal(pathname,0)

warndlg('User pressed cancel')

else

a=imread(filename);

[r c p]=size(a);

if p==3

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a=rgb2gray(a);

end

axes(handles.axes1);

imshow(a);

handles.input=a;

guidata(hObject, handles);

end

function encoding_Callback(hObject, eventdata, handles)

input=handles.input;

input=double(input);

[row col] = size(input);

len=row*col;

block=0;

Q1={};

Q2={};

z=1;

pop=get(handles.popupmenu1,'value');

switch pop

case 1

k=16;

case 2

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k=64;

end

mid=sqrt(k);

handles.blokval=k;

bit=zeros(1,len);

bitcount=1;

for x=1:mid:row

for y=1:mid:col

count=0;

t=0;

s=0;

a = input(x:x+(mid-1),y:y+(mid-1));

block=block+1;

m=mean(mean(a));

m=round(m);

for i=1:mid

for j=1:mid

if a(i,j)>=m

bit(1,bitcount)=1;

bitcount=bitcount+1;

count=count+1;

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p=a(i,j);

s=s+p;

else

bit(1,bitcount)=0;

bitcount=bitcount+1;

c=a(i,j);

t=t+c;

end

end

end

q1=floor(inv((k-count)) *t);

Q1{z}=q1;

q2=floor(inv((count))*s);

Q2{z}=q2;

z=z+1;

clear a;

end

end

lev=size(Q2);

levv=lev(2)*(k+2);

tt=1;

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zz=1;

ww=1;

trio=zeros(1,levv);

while(zz<=lev(2))

trio(1,tt)=Q1{zz};

tt=tt+1;

trio(1,tt)=Q2{zz};

tt=tt+1;

for kk=1:k

trio(1,tt)=bit(1,ww);

tt=tt+1;

ww=ww+1;

end

zz=zz+1;

end

bitcount=bitcount-1;

save bit bit;

save bitcount bitcount;

save Q1 Q1;

save Q2 Q2;

save row row;

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save col col;

save block;

save trio trio;

warndlg('Encoding completed');

guidata(hObject, handles);

function embedd_Callback(hObject, eventdata, handles)

load bit;

load q1;

load q2;

load count;

load row;

load col;

load block;

bit1=reshape(bit,[row,col]);

N=sqrt(block);

load trio;

trionew=trio;

Input=bit1;

original=Input;

target=Input;

fid = fopen('message.txt','r');

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F = fread(fid);

s = char(F');

fclose(fid);

len=length(F);

handles.lend=len;

sz1=size(Input);

segblock=sz1(1)/N;

size1=sz1(1)*sz1(2);

sz2=size(F);

size2=sz2(1);

if (len*8)<=(N*N)

val=(N*N)-(len*8);

j=1;

for i=1:len

k=8 ;

while(k>=1)

x(1,j)=bitget(F(i),k);

k=k-1;

j=j+1;

end

end

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for v=(len*8)+1:N*N

x(1,v)=0;

end

Mc=size(sz1,1);

Nc=size(sz1,2);

Mm=size(sz2,1);

Nm=size(sz2,2);

message_vector=round(F./256);

rand('state',34);

for (kk=1:100)

pn_sequence_h=round(rand(sz1/segblock,sz1/segblock));

end

size1=N*N;

sequence=reshape(abs(pn_sequence_h),[1,size1]);

for z=1:size1

xored(1,z)=xor(x(z),sequence(z));

end

mm=1;

gg=1;

segblock1=segblock^2;

for z=1:size1

58

if xored(1,z)==1

a = not(bit(gg:gg+(segblock1-1)));

trionew(mm:mm+(segblock1+1))=[Q2{z} Q1{z} a];

end

mm=mm+(segblock1+2);

gg = gg +segblock1;

end

embedtrio=trionew;

kn=segblock1;

k1n=sqrt(kn);

blocksn=[];

[r c]=size(embedtrio);

len=r*c;

pp=1;

saf=1;

sa=k1n;

sbf=1;

sb=k1n;

while(pp<=len)

a=embedtrio(pp);

pp=pp+1;

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b=embedtrio(pp);

pp=pp+1;

mn=1;

kj=1;

for jj=1:kn

if kj>k1n

mn=mn+1;

kj=1;

end

if embedtrio(pp)==0

output1(mn,kj)=a;

pp=pp+1;

kj=kj+1;

else

output1(mn,kj)=b;

pp=pp+1;

kj=kj+1;

end

end

blocksn(saf:sa,sbf:sb)=output1;

sbf=sb+1;

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sb=sbf+(k1n-1);

if sb>row

saf=sa+1;

sa=saf+(k1n-1);

sbf=1;

sb=k1n;

end

end

axes(handles.axes2);

imshow(uint8(blocksn));

handles.out=blocksn;

guidata(hObject, handles);

save trionew trionew;

save sequence sequence;

save xored xored;

warndlg('Embedding completed');

else

warndlg('The secret data size is greater than Cover Image-Reduce the data');

end

function extraction_Callback(hObject, eventdata, handles)

load trionew;

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load Q1;

load Q2;

load sequence;

load xored;

load row;

load col;

len=handles.lend;

[r c] = size(trionew);

wid = r*c;

blok=handles.blokval;

e=1;

for y=1:(blok+2):wid-(blok+1)

if trionew(1,y)<trionew(1,y+1)

xr(e)=0;

else

xr(e)=1;

end

e=e+1;

end

i=1;

gg=1;

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mm=1;

rand('state',34);

blok1=sqrt(blok);

for (kk=1:100)

pn_sequence_h=round(rand(row/blok1,row/blok1));

end

tot=row/blok1;

size1=tot*tot;

sequence=reshape(abs(pn_sequence_h),[1,size1]);

for i=1:size1

if (isequal(sequence(i),0) & isequal(xr(i),0)) | (isequal(sequence(i),1) &

isequal(xr(i),1))

x(i)=0;

else

x(i)=1;

end

end

len2=len*8;

siz=size(x);

secret=x(1:len2);

place = [128 64 32 16 8 4 2 1];

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cc=1;

asc=[];

for se=1:8:len2

binary=secret(cc:cc+7);

uu=(place(1).*binary(1))+(place(2).*binary(2))+(place(3).*binary(3))+

(place(4).*binary(4))+(place(5).*binary(5))+(place(6).*binary(6))+(place(7).*binary(7))+

(place(8).*binary(8));

if (exist('Output.txt'))

delete 'Output.txt';

end

fid = fopen('Output.txt','a');

fwrite(fid,char(uu),'char');

cc=cc+8;

end

fclose(fid);

warndlg('Text is retreived in output text file');

function decoding_Callback(hObject, eventdata, handles)

output=zeros(1,65536);

load trionew;

load row;

k=handles.blokval

k1=sqrt(k);

64

blocks=[];

[r c]=size(trionew);

len=r*c;

pp=1;

saf=1;

sa=k1;

sbf=1;

sb=k1;

while(pp<=len)

a=trionew(pp);

pp=pp+1;

b=trionew(pp);

pp=pp+1;

mn=1;

kj=1;

for jj=1:k

if kj>k1

mn=mn+1;

kj=1;

end

if trionew(pp)==0

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output1(mn,kj)=a;

pp=pp+1;

kj=kj+1;

else

output1(mn,kj)=b;

pp=pp+1;

kj=kj+1;

end

end

blocks(saf:sa,sbf:sb)=output1;

sbf=sb+1;

sb=sbf+(k1-1);

if sb>row

saf=sa+1;

sa=saf+(k1-1);

sbf=1;

sb=k1;

end

end

axes(handles.axes3);

imshow(blocks,[]);

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handles.out=blocks;

warndlg('Decoding Completed');

guidata(hObject, handles);

function validate_Callback(hObject, eventdata, handles)

reconstruct=handles.out;

input=handles.input;

reconstruct=double(reconstruct);

input=double(input);

[M N]=size(input);

MSE = sum(sum((input-reconstruct).^2))/(M*N);

PSNR = 10*log10(255*255/MSE);

PSNR=num2str(PSNR);

set(handles.psnr1,'String',PSNR);

set(handles.mse1,'String',MSE);

function psnr1_Callback(hObject, eventdata, handles)

function psnr1_CreateFcn(hObject, eventdata, handles)

if ispc

set(hObject,'BackgroundColor','white');

else

set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));

end

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function mse1_Callback(hObject, eventdata, handles)

function mse1_CreateFcn(hObject, eventdata, handles)

if ispc

set(hObject,'BackgroundColor','white');

else

set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));

end

function clear_Callback(hObject, eventdata, handles)

delete('Output.txt');

a=ones(256,256);

axes(handles.axes1);

imshow(a);

axes(handles.axes2);

imshow(a);

axes(handles.axes3);

imshow(a);

set(handles.psnr1,'String',' ');

set(handles.mse1,'String',' ');

warndlg('Files erased succesfully');

function close_Callback(hObject, eventdata, handles)

close gui;

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function popupmenu1_Callback(hObject, eventdata, handles)

function popupmenu1_CreateFcn(hObject, eventdata, handles)

if ispc

set(hObject,'BackgroundColor','white');

else

set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));

end

69

CHAPTER 7

RESULTS AND CONCLUSION

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7.1 Results:

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Figure 7.1 New command window of MATLAB

72

Figure 7.2 Source code of our gui file

73

Figure 7.3 GUI window in the MATLAB

74

Figure 7.4 Message file which we are embedding

75

Figure 7.5 Selection of image for covering

76

Figure 7.6 Selected image shown in GUI

77

Figure 7.7 Completion of Decoding Process

78

Figure 7.8 Completion of Embedding Process

79

Figure 7.9 Text file extracted in file named output

80

Figure 7.10 Extracted output file

81

Figure 7.11 Completion of Decoding Process

82

Figure 7.12 Retrieving of Original Image

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Figure 7.13 File erased successfully

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7.2 Conclusion:

Steganography can be used for hidden communication. We have explored the

limits of steganography theory and practice. We pointed out the enhancement of the

image steganographic system using LSB approach to provide a means of secure

communication. A stego-key has been applied to the system during embedment of the

message into the cover-image. In our proposed approach, the message bits are embedded

randomly into the cover-image pixels instead of sequentially. Finally, we have shown that

steganography that uses a key has a better security than non-key steganography. This is

so because without the knowledge of the valid key, it is difficult for a third party or

malicious people to recover the embedded message. However there are still some issues

need to be tackled to implement LSB on a digital image as a cover-object using random

pixels. After testing the system and studied the recorded results from the experiments.

We recommend using this proposed system in hiding secure information in any digital

system, because this system collect the properties of both steganography and

cryptography sciences.

7.3 Future Scope:

The steganography method presented here can also be combined with some

cryptography method to keep the data non-decipherable even if it is detected. The reserve

bytes in the header could also be used to encode a password or a key, which would have

to be matched to decode the rest of the data. In this method we can only use a square

image it can also be changed to any type of image. In addition to being vulnerable to

detection techniques, LSB is extremely vulnerable to corruption. That is, the integrity of

the hidden message can easily be destroyed. All the attacker must do is to randomize the

LSBs of the image. The attacker may not even know that it is a stego-image, but such

actions would destroy the secret message. Let us hope to overcome these disadvantages

in near future.

85

REFERENCES

1. Digital Image Processing Second Edition Rafael C. Gonzalez University of

Tennessee Richard E. Woods MedData Interactive Prentice Hall, 2001.

2. Anderson R. J., 'Stretching the Limits of Steganography', 1st Information Hiding

Workshop, Springer Lecture Notes in Computer Science, Vol.1174, pp. 39–48,

1996.

3. Provos, N., Honeyman, P., 'Hide and seek: an introduction to steganography',

Security & Privacy Magazine, IEEE, Vol. 1, No. 3, pp. 32-44, May-June 2003.

4. Fridrich, J., Goljan, M., Du, R., ‘Detecting LSB steganography in color and gray-

scale images’, IEEE Multimedia Special Issue on Security, pp. 22-28, October-

November 2001.

5. Dumitrescu, S., Wu, X., Memon, N., 'On steganalysis of random lsb embedding in

continuoustone images', IEEE International Conference on Image Processing,

Rochester, New York, September 2002.

6. Chandramouli, R. and N. Memon, 2001. Analysis of LSB based image

steganography techniques. Proceedings of the International Conference on Image

Processing, Oct. 7-10, IEEE Computer

7. Society, Washington DC., USA., pp: 1019-1022. DOI:

10.1109/ICIP.2001.958299

8. Judge, J.C., 2001. Steganography: Past, present, future

http://www.sans.org/reading_room/whitepapers/steganography/steganography_pa

st_present_future_552

9. Johnson, N.F., 2000. Steganography. http://www.jjtc.com/stegdoc/index2.html

10. Anderson, R., R. Needham and A. Shamir, 1998. The steganographic file system.

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