Report

IMAGE STEGANOGRAPHY

1. INTRODUCTION

Steganography from the Greek means secret or coveted writing and is a long

practiced form of hiding information. It is a very old method of passing messages in secret. The

historian Herodotus wrote about how an agent wrote a message warning of an invasion on the wood

part of a wax tablet. Since messages were normally inscribed in the wax and not the wood, the tablet

appeared blank to a common observer. There is also the story of a messenger during the Persian Wars

who shaved his head and had a message tattooed on it. He waited until his hair grew back to make his

journey. When he arrived at his destination, he shaved his head to reveal the message. During WWII

spies on both sides used “invisible” inks. These inks were fluids such as milk, fruit juice, or urine that

would darken when heated. They also sent messages with very small punctures above characters in a

document that formed a message when combined.

Steganography improved with the development of new technologies that

could pass off more information and be less conspicuous. It can be defined as the art and science of

writing hidden messages in such a way that no one apart from the sender and intended recipient even

realizes there is a hidden message. It includes a vast array of secret communication methods that

conceals the message’s very existence. These methods include microdots, character arrangement,

digital signature and covert channels. According to Dictoinary.com, steganography is:”Hiding a secret

message within a larger one in such a way that others cannot discern the presence or contents of the

hidden message” and cryptography is:”The process or skill of communication in, or deciphering secret

writing or ciphers.”

Although related to cryptography, they are not the same. The advantage

of steganography over cryptography alone is that messages do not attract attention to themselves, to

messengers or to recipients. An unhidden coded message, no matter how unbreakable it is, will arouse

suspicion and may in itself be incriminating, as in countries where encryption is illegal. Often,

steganography and cryptography are used together to ensure security of the covert message. However

judicial limits must be implemented on cryptography and steganography or else if terrorists use a good

stego-tool and a solid encryption algorithm it would be very difficult to discover their plans before they

are executed. Of course, encryption should not be mitigated as it is an academic pursuit and helps

preserve privacy.

DEPARTMENT OF ELECTRONICS, CUSAT

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STEGANOGRAPHY V/S CRYPTOGRAPHY

Steganogarphy Cryptography

Hides the existence of message.Scrambles a message so that it cannot be

understood.

Message looks like normal graphic, video or

sound file.

Message looks like meaningless jumble of

characters.

A collection of graphic images, video files, or

sound files on a disk may not look suspicious.

A collection of random characters on a disk

may look suspicious.

A smart eavesdropper can detect something

suspicious from a sudden change of message

format (i.e., text to graphic images).

A smart eavesdropper can detect a secret

communication from a message that has been

cryptographically encoded.

Requires caution when reusing pictures or sound

files.Requires caution when reusing keys.

There are no laws associated There are some laws that ban cryptography.

Generally a steganographic message will appear to be something else: a

picture, an article, a shopping list, or some other message. This apparent message is the covert picture.

A steganographic message (the plain text) is often first encrypted by some traditional means, producing

a cipher text. Then a covertext is modified in some way to contain the cipher text, resulting in

stegotext. For example, the letter size, spacing, type face, or other characteristics of a cover text can be


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manipulated to carry the hidden message; only the recipient(who must know the technique used) can

recover the message and then decrypt it. Open coded messages, which are plain text passages ,

“sound” innocent because they purport to be about ordinary occurrences. Because many open-coded

messages don’t seem to be cause for suspicion, and therefore “sound” normal and innocent, the suspect

communications can be detected

By mail filters while “innocent” messages are allowed to flow through.

Digital technique gave us new ways to apply steganographic techniques, including one of the most

intriguing – that of hiding information in digital images. Today, the term steganography includes the

concealment of digital information within computer files. When embedding data it is important to

remember the following restrictions and features:

The cover data should not be significantly degraded by the embedded data, and the embedded

data should be as imperceptible as possible.

The embedded data should be directly encoded into the media, rather than into a header or

wrapper, to maintain data consistency across formats.

The embedded data should be as immune as possible to modifications from intelligent attacks

or anticipated manipulations such as filtering and resampling.

Some distortion or degradation of the embedded data can be expected when the cover data is

modified. To minimize this, error correcting codes should be used.


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2. HISTORY

The first description of the use of steganography dates back to the

Greeks. Herodotus tells how a message was passed to the Greeks about Xerses’ hostile intentions

underneath the wax of a writing tablet. The Chinese used a slightly different form of steganography.

Like the Greeks, the Chinese would transport secret information via messengers. The object was called

a La wan, a thin piece of silk that had a message written on it.

Gaspar Schott, in his book Schola Steganographica, described a method

of encoding secret information by matching letters to specific musical notes. This "music" would never

be pleasing to listen to if played, and to the untrained eye it would appear to be normal sheets of music

when, in fact, it was an encoded message.

Figure 1

Pirate legends tell of the practice of tattooing secret information, such

as a map, on the head of someone, so that the hair would conceal it. Invisible ink offered a common

form of invisible writing. Early in World War II, steganographic technology consisted almost

exclusively of these inks. With invisible ink, a seemingly innocent letter could contain a very

different message written between the lines. Margaret Thatcher, the former British Prime Minister,

used a method of invisible watermarking in the 1980s. After several cabinet documents had been

leaked to the press, Thatcher ordered that the word processors being used by government employees


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mk:@MSITStore:C:%5CDOCUME~1%5CXP%5CLOCALS~1%5CTemp%5CRar$DI02.000%5C0849324335.chm::/7366final/LiB0046.html#147

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encode their identity in the word spacing of the document. This allowed for disloyal ministers to be

quickly found out.

3. DIGITAL STEGANOGRAPHY TECHNIQUES

When it is all said and done, there are only three ways to hide a digital message in a digital cover: injection, substitution, and generation of new files.

1. INJECTION

Data injection embeds the secret message directly in the host medium.

The problem with this kind of embedding is that it usually makes the host file larger, and therefore the

alteration is easier to detect.

2. SUBSTITUTION

Normal data is replaced or substituted with the secret data. This usually

results in very little size change for the host file. However, depending on the type of host file and the

amount of hidden data, the substitution method can degrade the quality of the original host file.

Figure

3. GENERATION OF NEW FILES (FIGURE)

A cover is generated for the sole purpose of concealing a secret message.

The generated cover is innocent that can be passed over, and the cover provides the mechanism for

conveying the message.

Another, more modern form of generation of new files is the Spam

Mimic program. Spam Mimic embeds a text message within a rather daunting piece of spam, which

can be e-mailed to an intended recipient. While this generated spam does not make a whole lot of

sense, it makes enough to be believable, and that is enough.


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4. STEGANOGRAPHY IN IMAGES

In recent years, enormous research efforts have been invested in the

development of digital image steganographic techniques. The major goal of steganography is to

enhance communication security by inserting secret message into the digital image, modifying the

nonessential pixels of the image.

4.1. TERMS USED

Steganography literally means "covered writing" and is the art of hiding

the very existence of a message. In the field of steganography, some terminology has developed. The

adjectives cover, embedded and stego were defined at the Information Hiding Workshop held in

Cambridge, England. The term ``cover'' is used to describe the original, innocent message, data, audio,

still, video and so on. When referring to audio signal steganography, the cover signal is sometimes

called the ``host'' signal.

The information to be hidden in the cover data is known as the

``embedded'' data. The ``stego'' data is the data containing both the cover signal and the ``embedded''

information. Logically, the processing of putting the hidden or embedded data, into the cover data, is

sometimes known as embedding. Occasionally, especially when referring to image steganography, the

cover image is known as the container.

We can sort these definitions common to the steganography field as follows:

Cover medium: This is the medium in which we intent to hide data, it can be an innocent looking

piece of information for steganography, or some important medium that must be protected for

copyright or integrity reasons.

Embedded message: This is the hidden message we want to put in the cover. It can be some data

for steganography and some copyright information or added content for digital watermarking.


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Stegokey: This is represented by some secret information, which is needed in order to extract the

embedded message from the stego-medium.

Stego-medium: This is the final piece of information that the casual observer can see. A possible

formula of the process may be represented as:

Cover medium+ embedded message +stegokey = stego-medium.

The formula can be diagrammatically represented as follows:

4.2 PROPOSED METHOD

The framework of proposed Steganography Based Information

Protection method is shown in Fig 1. Its description is presented in the following steps.


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CRYPTO PART

SCANNING CODING ENCRYPTION

COVER

PROCESSIN

G

EMBEDDING RESHAPING

CRYPTO KEYS

STEGO PART

STEGO IMAGESTEGO KEYS

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Scanning

The message is mostly connected with the neighbourhood elements, i.e.

pixels in an image are varying smoothly and letter in the text are related to those on the right and the

left. Scanning process minimizes these relations by suitably created random arrangement of message

elements. We consider a randomization scheme in which a scan function, defined on different scan

patterns, controls the randomization. A function defined for getting a scan path for randomizing the

block is known as key for this process.

Coding

It contains some redundant space due to smooth variation in images and

language characteristics in text. The distribution of message elements shows that it can be represented

with lesser number of bits. In error free coding, the most frequent elements are represented by shorter

codes and least frequent letters by longer codes. These codes change the statistical properties of the

message. Huffman codes are error free and can be used for increasing the security. We use Huffman

codes for text coding and Modified Huffman codes for binary images or Fax data. These codes are used

for achieving additional security.

Encryption

This process conceals the message by transforming it into unintelligible

form. Mostly, shift register based schemes are being used in present – day cryptography due to their

simplicity and ease of hardware implementation. In shift register based schemes, the message bits are

added under modulo two with binary random sequences. Linear feedback shift registers, feedback

polynomials, state filter function and combining function are known as key parameters of this process.

Reshaping

In applications, the method is required to be made robust so that

embedded message can be deducted easily, even when stego images are slightly modified. In digital

communications, information is transmitted bit-by-bit, i.e. as binary signaling. Larger the pulse size of

the symbol higher is the probability of detection. Improvements of performance is due to the fact that

for fewer symbols to hide we use more locations per symbol. Each symbol is represented by a pattern

of binary bits.


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Cover Processing

Mostly LSB are highly variable in cover images and some minor changes

in this region do not affect its quality and visual appearance. The highly variable region can be used for

hiding secret information in invisible manner. Depth of hiding of cover image used for information

hiding can be measured by an entropy measure. To make steganography secure against known cover

image attack, it is necessary to make cover image suitable for information hiding so that it is not

vulnerable to known cover – stego attack. The parameters used for generating random binary sequence

and depth of hiding chosen ones are considered as key parameters.

Embedding Process

Process proposed is based on substitution method where message bits,

after above processing steps, are embedded in cover image in randomly selected pixels at random

places in LSB region within decided depth. Cover image to be used for embedding is processed first by

modifying LSB of pixels. Embedding of information does not effect the quality and visual appearance

of stego images. Embedding is based on the theory of shift registers.

This embedding method provides greater flexibility of hiding information

and makes detection of embedded message more difficult. Even if we know that there exits an

embedded message, its extraction is very difficult without knowing the key used. An attack who has no

knowledge of key parameters cannot extract the embedded message.

Method of restoring clear message is reverse of the above steps, i.e., to

detect, decrypt, decode and reconstruct the message. If reshaping is used then it is required to deshape

prior to decryption.

Steganography includes a vast variety of techniques for hidden messages

in a variety of media. Today thanks to modern technology, steganography is used on text, images,

sound, signals, and more. In the following sections we look at how steganography can and is being

used through the media of images. In this section we deal with data encoding in still digital images. In

essence, image steganography is about exploiting the limited powers of the human visual system

(HVS). Within reason, any plain text, cipher text, other images, or anything that can be embedded in a

bit stream can be hidden in an image. Image steganography has come quite far in recent years with the


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development of fast, powerful graphical computers, and steganographic software is now readily

available over the internet for everyday users.

An example of stegangraphy in images is shown below:

Image of a tree. By removing all but the last 2 bits of each color

component, an almost completely black image results. Making the resulting image 85 times brighter

results in the image below.

Image extracted from above image.

In a computer, images are represented as arrays of values. These values

represent the intensities of the three colors R(ed) G(reen) and B(lue), where a value for each of the

three colors describes a pixel. Through varying the intensity of the RGB values, a finite set of colors

spanning the full visible spectrum can be created. Each primary color is represented by 1 byte; 24-bit

images use 3 bytes per pixel to represent a color value. These 3 bytes can be represented as

hexadecimal, decimal, and binary values. In many Web pages, the background color is represented by a

six-digit hexadecimal number—actually three pairs representing red, green, and blue. A white

background would have the value FF, FF, FF: 100 percent red (FF), 100 percent green (FF), and 100

percent blue (FF). Its decimal value is 255, 255, 255, and its binary value is 11111111, 11111111,


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11111111, which are the three bytes making up white. In an 8-bit gif image, there can be 2^8 = 256

colors and in a 24-bit image, there can be 2^24 = 16777216 colors.

An image size of 640 by 480 pixels, utilizing 256 colors (8 bits per pixel)

is fairly common. Such an image would contain around 300 kilobits of data. Digital images are

typically stored in either 24-bit or 8-bit per pixel file. A 24-bit image sometimes called a true color

image provides the most space for hiding information; however, it can be quite large. The best quality

hidden image is normally produced using a 24-bit bitmap as a cover image. Pixel representation

contributes to file size. For example, suppose we have a 24-bit image 1,024 pixels wide by 768 pixels

high—a common resolution for high- resolution graphics. Such an image has more than two million

pixels, each having such a definition, which would produce a file exceeding 2 Mbytes. Because such

24-bit images are still relatively uncommon on the Internet, their size would attract attention during

transmission. File compression would thus be beneficial, if not necessary, to transmit such a file.


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5. IMAGE FILES

To a computer, an image is an array of numbers that represent an

array of numbers that represent light intensities at various points or pixels. These pixels make up the

image’s raster data. A common image size is 640 * 480 and 256 colors (or 8 bits per pixel). Such an

image could contain about 300 kb of data.

Digital images ate typically stored as either 24-bit or 8-bit files. A

24-bit image provides the most space for hiding information, however, it can be quite large except

for the JPEG images. A 24-bit image of 1,024 pixels width and 768 pixels height has more than two

million pixels, each having 24-bits, which would produce a file exceeding 2 Mega bytes. Such a file

would attract attention during transmission. File compression would thus be beneficial, if not

necessary, to transmit such a file.

5.1 IMAGE COMPRESSION

Image compression offers a solution to large image files. Two kinds of

image compression are lossless and lossy compression. Both methods save storage space but have

differing results interfering with the hidden information, when the information is uncompressed

Lossy compression, as typified by JPEG (Joint Photographic Experts

Group) format files, offers high compression, but may not maintain the original image's iintegrity.

This can impact negatively on any hidden data in the image. This is due to the lossy compression

algorithm, which may ``lose'' unnecessary image data, providing a close approximation to high-quality

digital images, but not an exact duplicate. Hence, the term``lossy'' compression. Lossy compression is

frequently used on true-colour images, as it offers high compression rates.

Lossless compression maintains the original image data exactly; hence it

is preferred when the original information must remain intact. It is thus more favoured by

steganographic techniques. Unfortunately, lossless compression does not offer such high compression

rates as lossy compression. Typical examples of lossless compression formats are GIF (Graphics

Interchange Format) and Microsoft's BMP (Bitmap) format.


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5.2 PALETTE AND IMAGE COMPOSITION

The palette and composition of the image also contribute to how well the

stegotool does its job. An images with gradual color gradients or in grayscale is the best for

stenography because it is easier to insert small “errors” in. The changes also appear moregradually and

as a result are less likely to be detected. Observe the different color palettes below and how the one on

the left changes gradually and is more suitable for a cover image than the one on the right. (see Figure

below)

Figure – Palette Gradients

It is also important to use images that do not contain large blocks of a solid color, as the changed bits in

the solid area are easier to detect.


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6. TECHNIQUES FOR IMAGE STEGANOGRAPHY

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. There have

been a number of image steganography algorithm proposed, these algorithm could be categorized in a

number of ways:

SPATIAL OR TRANSFORM

It is depending on redundancies used from either domain for the

embedding process.

MODEL BASED OR AD-HOC

It is used if the algorithm models statistical properties before embedding

and preserves them, or otherwise.

ACTIVE OR PASSIVE WARDEN

It is based on whether the design of embedder-detector pair takes into

account the presence of an active attacker.

6.1. SPATIAL DOMAIN EMBEDDING

Information can be hidden/concealed in many different ways in images.

Straight message insertion can be done, which will simply encode every bit of information in the

image. More complex encoding can be done to embed the message only in ``noisy'' areas of the image

that will attract less attention. The message may also be scattered randomly throughout the cover

image. The most common approaches to information hiding in images are:

Least significant bit (LSB) insertion

Masking and filtering techniques

Redundant and pattern encoding technique

Encrypt and scatter technique

Each of these can be applied to various images, with varying degrees of success. Each of them suffers

to varying degrees from operations performed on images, such as cropping, or resolution decrementing,

or decreases in the colour depth.


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6.1.1 LEAST SIGNIFICANT BIT INSERTION

First we will investigate least significant bit insertion, where you

literally put the information in the least significant bits of an image. This is one of the most common

techniques used today in steganography where you pack the hidden messages into insignificant bits in

image data. This is a simple technique but the down side is that the message is very susceptible to

information loss when using lossy compression techniques. A 1,024 by 768 image has the potential to

hide a total of 2,359,296 bits (294,912 bytes) of information. If you compress the message to be hidden

before you embed it, you can hide a large amount of information. To the human eye, the resulting

stego-image will look identical to the cover image.

LSB insertion requires on average that only half the bits in an image be

changed. For example since the 8-bit letter A only requires eight bytes to hide it in, the ninth byte of

the three pixels can be used to hide the next character of the hidden message. Here is the original raster

data:

(00100111 11101001 11001000)

(00100111 11001000 11101001)

(11001000 00100111 11101001)

The binary value of A is 10000011 and encoding A into the last bits of this 3 pixel sequence will

change the above sequence to:

(00100111 11101000 11001000)

(00100110 11001000 11101000)

(11001000 00100111 11101001).

Notice that only the underlined bits had to be changed in order to create

the A. A slight variation of this technique allows for embedding the message in two or more of the

least significant bits per byte. This increases the hidden information capacity of the cover-object, but

the cover-object degrades more statistically, and it is more detectable.


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For example, using LSB (Least Significant Bit) technique, in the image

in figure 2 (Saint Olga planting Christianity in Russia symbolized as a tree) on the left below each

pixel is encoded in 24 bits, 8 each for the red, green, and blue intensity of that pixel. In the image on

the right the least significant bit for each color of some number of pixels was used to encode a hidden

message which is the Gospel of Judas in its English translation by Kasser, Meyer, and Wurst.. The

original image size was 500x320pixels, giving 961,000 bits or about 120,000 bytes for a hidden

message and quite more than enough for the short text of 17,845 bytes. The human eye cannot detect

any difference between the full 24-bit color of the image on the left from the slightly modified image

on the right.

Figure - A message is embedded in the right image using LSB technique in steganography

Steganography software processes LSB insertion to make the hidden

information less detectable. For example, the EzStego tool arranges the palette to reduce the occurrence

of adjacent index colors that contrast too much—before it inserts the message. This approach works

quite well in gray-scale images and in images with related colors.S-Tools, another steganography tool,

takes a different approach by closely approximating the cover image,which may mean radical palette

changes. As with 24-bit images, hanging the pixels’ LSB may create new colors. (New colors may not


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be added to an 8-bit image due to the palette limit.) Instead, S-Tools reduces the number of colors

while maintaining the image quality, so that the LSB changes do not drastically change color values.

For example, eight color values are required for each color if values 000

through 111 are to be stored. Reducing the number of unique colors to 32 ensures that these values can

be used and that the number of colors will not exceed 256 (256/8 = 32). Each of the 32 unique colors in

the palette may be expanded to eight colors having LSB values of the red, green, blue (RGB) triples

ranging from 000 to 111. This results in multiple colors in the palette that look the same visually but

that may vary by one bit. Some of the advantages of this technique of least significant bit insertion are

given below:

Major advantage of LSB algorithm is that it’s quick and easy.

LSB insertion also works well with grey scale images.

On the average only half of the bits would have to be changed in an LSB

encoding scheme.With such small variation in the colors it would be very

difficult for the human eye to discern the difference.

6.1.2 MASKING AND FILTERING

Masking and filtering techniques are mostly used on 24 bit and greyscale

images. They hide nfo in a way simliar to watermarks on actual paper and are sometimes used as

digital watermarks. Masking images entails changing the luminance of the masked area. The smaller

the luminance change, the less of a chance it can be detected. Observe that luminance in figure.3 is at

15% in the mask region if it was decreased then it would nearly be invisible.


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Figure – Masking

Because watermarking techniques are more integrated into the image,

they may be applied without fear of image destruction from lossy compression. By covering, or

masking a faint but perceptible signal with another to make the first non-perceptible, we exploit the

fact that the human visual system cannot detect slight changes in certain temporal domains of the

image.

Technically, watermarking is not a steganographic form. Strictly,

steganography conceals data in the image; watermarking extends the image information and becomes

an attribute of the cover image, providing license, ownership or copyright details. Masking techniques

are more suitable for use in lossy JPEG images than LSB insertion because of their relative immunity

to image operations such as compression cropping.

Stego-images (images that have been manipulated by steganographic

methods) that are masked will keep a higher fidelity than LSB through compression, cropping and


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some image processing. The reason that a stego image encoded with masking, degrades less under

JPEG compression is that the secret message is hid in the significant areas of the picture. There is a tool

called JPEG – J steg that takes advantage of the compression of JPEG while trying to keep a high

message fidelity. The program takes a secret message and a lossless cover image as input and outputs a

stego image in JPEG format.

6.1.3. PVD METHOD FOR GRAY-LEVEL IMAGE

The alteration of edge areas in the human visual system cannot be

distinguished well, but the alteration of smooth areas can be distinguished well. That is, an edge area

can hide more secret data than a smooth area. With this concept, Wu and Tsai proposed a novel

steganography technique using the pixel-value differencing (PVD) method to distinguish edge and

smooth areas. The pixel-value differencing (PVD) method segments the cover image into non

overlapping blocks containing two connecting pixels and modifies the pixel difference in each block

(pair) for data embedding. A larger difference in the original pixel values allows a greater modification.

The PVD technique can embed more data in the edge area which guarantees high imperceptibility.

In Wu and Tsai’s scheme, which is also known as the PVD method, the difference value

between two consecutive pixels is regarded as a feature for recording the secret message. When the

original difference value is unequal to the secret message, the two consecutive pixels will be directly

adjusted so that their difference value can stand for the secret data. However, considerable stego-image

distortion can happen when the PVD method adjusts the two consecutive pixels to hide the secret data

in the difference value.

6.1.4 IMAGE STEGANOGRAPHY BASED ON FIRST COMPONENT ALTERATION

In a computer, images are represented as arrays of values. These values

represent the intensities of the three colors R(Red), G (Green) and B (Blue), where a value for each of

three colors describes a pixel. Each pixel is combination of three components(R, G, and B).In this

scheme, the bits of first component (blue component) of pixels of image have been replaced with data

bits, which are applied only when valid key is used. Blue channel is selected because a research was

conducted by Hecht, which reveals that the visual perception of intensely blue objects is less distinct


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that the perception of objects of red and green. For example, suppose one can hide a message in three

pixels of an image (24-bit colors). Suppose the original 3 pixels are:

(00100111 11101001 11001000) (00100111 11001000

11101001)

(11001000 00100111 11101001)

A steganographic program could hide the letter "A" which has a position 65 into ASCII character set

and have a binary representation "01000001", by altering the blue channel bits of pixels.

(01000001 11101001 11001000) (00100111 11001000

11101000)

(11001000 00100111 11101001)

Procedure:

Step 1: Extract all the pixels in the given image and store it in the array called Pixel-Array.

Step 2: Extract all the characters in the given text file and store it in the array called Character- Array.

Step 3: Extract all the characters from the Stego key and store it in the array called Key- Array.

Step 4: Choose first pixel and pick characters from Key-Array and place it in first component of pixel.

If there are more characters in Key- Array, then place rest in the first component of next pixels,

otherwise follow Step (e).

Step 5: Place some terminating symbol to indicate end of the key. ‘0’ has been used as a terminating

symbol in this algorithm.

Step 6: Place characters of Character- Array in each first component (blue channel) of next pixels by

replacing it.

Step 7: Repeat step 6 till all the characters has been embedded.

Step 8: Again place some terminating symbol to indicate end of data.

Step 9: Obtained image will hide all the characters that we input.


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6.1.5 STOCHASTIC MODULATION

Stochastic modulation is a high-capacity steganographic method that

embeds message bits into individual pixels by adding to the cover image a noise signal with a

probabilistic distribution that is symmetrical about zero.

First, note that if {si} is a normally distributed Gaussian sequence N(0,σ)

and if zi is a random variable uniformly distributed in {–1, 1}, then {zisi} is also N(0,σ). In other

words, a Gaussian sequence with randomized signs stays Gaussian. This statement is true for any

random variable with a distribution symmetrical about zero.

Suppose the message mi consists of a random sequence of 1's and –1's

(mi has zero mean). Consider a naïve steganographic scheme in which we add the signal {misi} to the

image. Unfortunately, in order to recover the message, the original image or at least its approximation

(e.g., using low-pass filtering) is necessary. Errors in estimating the original image necessitate

employment of error-correction schemes, which in turn may dramatically decrease the steganographic

capacity. Below, we show a simple idea how a class of parameterized parity functions can be used to

make this scheme oblivious.

Defining a parity function P on pixel values, P(x, s) ∈ {–1,1}, for x∈{0,

…, 255} and s > 0, where s is an integer parameter, and P(x, s) = 0 for s = 0. This function applied to

the stego image pixel values will produce message bits.

The parity function is required to satisfy the following “anti-symmetric” property for all x

P(x+s, s) = – P(x–s, s) for s ≠ 0 .

For example, for s = 1, we can define P(x, 1), x = 0, 1, 2, … as P(x, 1) = 1, 1, –1, –1, 1, 1, –1, –1, …. In

general, for

s > 0, the first segment of 2s parities can be arbitrary, but every next segment of 2s values must be the

negative copy of the previous segment. Thus, it is enough to define P on the set [1, 2s]. A good choice

for the parity function is

P(x, s) = (–1)x+s, x∈[1, 2s] .

This parity function ensures that P changes its sign as often as possible.

We will find this property useful when x+s or x–s should get outside of their dynamic range during


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embedding. Notice that besides the pixel value x, the parity function depends on the second parameter

s. This is important because otherwise we could not find a function P(x) satisfying P(x+s) = – P(x–s)

for all pixel values x and all positive integers s.

Embedding method

Having defined the parity function, we can now continue with the

description of the embedding method. The image pixels can be visited either sequentially or along a

pseudo-random walk generated from the stego-key. A pseudorandom number generator (PRNG) is

seeded with a secret seed derived from the stego-key. The PRNG should produce numbers with a

distribution that matches the distribution of the noise that will be superimposed on the cover image

during embedding. We will call the noise generated by the PRNG the stego noise.

For each pixel x along the random walk, we generate one sample of the

stego noise rounded to an integer s. If s = 0, we do not modify x and move to the next pixel. If s ≠ 0, we

check if P(x+s, s) = m, where m is the message bit to be embedded. In this case, we modify x to x + s

and move to the next pixel and embed the next message bit. If P(x+s, s) = –m, we modify x to x – s.

Denoting the pixel values of the stego image as xi’, the embedding process can be expressed using the

formula

xi’ = xi + miP(xi + si , si) si .

In this formula, the message bits mi are duplicated as necessary to

account for the cases when si = 0. We can say that instead of adding the signal {misi} to the cover

image as we did in the beginning of this section, we add {visi}, where vi = miP(xi + si, si). According

to our assumption, the message bits mi form a pseudo-random sequence of 1’s and – 1’s. Because the

image and the stego noise sequence si are independent of the message, the variable vi is also a pseudo-

random sequence of 1’s and –1’s. Thus, the signal vi has the same statistical properties as the stego

noise.

There is a slight complication at the boundaries of the pixels’ dynamic

range at 0 and 255. The amplitude of the noise that is added to the image should be truncated as it

would happen during the image acquisition process.


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Whenever xi + si > 255 the xi’ will be the nearest value less or equal to 255 with the desired parity mi.

A similar measure is applied when xi + si < 0.

6.2 TRANSFORM TECHNIQUES

There are three main types of transform techniques used when

embedding a message in steganography: (1) discrete cosine transform (DCT), (2) discrete Fourier

transform, and (3) wavelet transform.

6.2.1. DISCRETE COSINE TRANSFORM (DCT)

The discrete cosine transform, simply put, helps to separate the image

into parts of differing importance with respect to the image's visual quality. Discrete cosine transform-

based image compression relies on two techniques to reduce the data required to represent the image:

1. Quantization of the image's DCT coefficients. Quantization is the process of reducing the

number of possible values of a quantity, thereby reducing the number of bits needed to

represent it.

2. Entropy coding of the quantized coefficients. Entropy coding is a technique for representing the

quantized data as compactly as possible.

A simple example of quantization is the rounding of real numbers into

integers. To represent a real number between 0 and 7 to some specified precision takes many bits.

Rounding the number to the nearest integer gives a quantity that can be represented by just three bits.

For example, 2.765423 rounded to 3 takes up fewer bits. By doing this,

we can reduce the number of possible values of the quantity, and along with it the number of bits

needed to represent it, at the cost of losing information. A "finer" quantization allows for more values

and loses less information.

In the JPEG image-compression standard, each cosine transform

coefficient is quantized using a weight that depends on the frequencies for that coefficient. The

coefficients in each 8 × 8 block are divided by a corresponding entry of an 8 × 8 quantization matrix,


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and the result is rounded to the nearest integer.To shed some more light on the DCT, we will take a

closer look at how JPEG compression works.

JPEG divides up the image into 8 × 8 pixel blocks, and then calculates the DCT of each block.

The DCT helps separate the image into parts (or spectral subbands) of differing importance

(with respect to the image's visual quality). In other words, some parts of the image are more

important to the overall picture than other parts.

A quantizer rounds off the DCT coefficients according to the quantization matrix. At this point

it is important to reemphasize that there is a trade-off between image quality and the degree of

quantization. A large quantization change can produce unacceptably large image distortion. On

the opposite end, finer quantization leads to lower compression ratios. With this said, the

question now is how to quantize the DCT coefficients most efficiently. Because of human

eyesight's natural high-frequency roll-off, these high frequencies play a less important role than

low frequencies. This lets JPEGs make larger modifications to the high frequencies with little

noticeable image deterioration. If steganographic data is being loaded into the JPEG image, the

loading occurs after this step.

This next step produces the "lossy" nature of a JPEG, but this also allows for large compression

ratios.

JPEG's compression technique uses a variable length code and then writes the compressed data

stream to the output file, with the commonly recognized .jpg suffix. During decompression,

JPEG recovers the quantized DCT coefficients from the compressed data stream, takes the

inverse, and displays the image (Figure).


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IMAGE STEGANOGRAPHY

The JPEG encoding procedure divides an image into 8 × 8 blocks of pixels. Then they are run through

a DCT and the resulting visual frequencies, high and low, are scaled to remove the ones that human

viewers would not detect under normal conditions. If steganographic data is going to be loaded into the

JPEG, it happens after this step. When this happens, the lowest-order bits of all nonzero frequency

coefficients are replaced with the bits from the steganographic source file. These modified coefficients

are then sent to the Huffmann coder, which changes colour frequencies to a numeric value.

Here is an example showing how steganographic data is encoded:

The steganographic encoding format (the format of data inserted into the lowest-order bits of

the image) is as follows:

+ — — -+ — — — — — - — — -+ — — — — — — — — — — — — — — —

| A | B B B… B | C C C C C C C C C C…

+ — — -+ — — — — — - — — -+ — — — — — — — — — — — — — — —

"A" is 5 bits. It expresses the length (in bits) of field B. Order is most-significant bit first.

"B" is some number of bits from 0 to 31. It expresses the length (in bytes) of the injection file.

Order is again most-significant bit first. The range of values for "B" is 0 to 1 billion.

"C" is the bits in the injection file. No ordering is implicit on the bit stream.


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This format, by design, makes the steganographic content as

inconspicuous as possible. But being inconspicuous is only part of the problem. The storage

effectiveness for this technique is decent but not outstanding. Tests have shown that compressing the

steganographic file before injecting the message does not greatly harm compression.

6.2.2 DISCRETE FOURIER TRANSFORM

The discrete Fourier transform transforms a signal or image from the

spatial domain to the frequency domain. Kun-Hung Lee, an engineering student at the University of

Bridgeport, creates an excellent analogy for the discrete Fourier transform by comparing how sound

frequencies are interpreted by the human ear.

If you used this hypothetical technology to film your eardrum while

listening to your best friend saying your name, then took the resulting movie and wrote down the

numeric position of your eardrum in every frame of the movie, you would have a digital PCM (pulse

code modulation) recording. If you could later make your eardrum move back and forth in accordance

with the thousands of numbers you had written down, you would hear your friend's voice saying your

name exactly as it sounded the first time. It really does not matter what the sound is — your friend, a

crowded party, a symphony. When you hear more than one thing at a time, all the distinct sounds are

physically mixed together in your ears as a single pattern of varying air pressure. Your ears and your

brain work together to analyze this signal back into separate auditory sensations.

FREQUENCY INFORMATION AS FUNCTION OF TIME

An organ in our inner ears called the cochlea enables us to detect tonality in the sounds we hear.

The cochlea is acoustically coupled to the eardrum by a series of three tiny bones. It consists of a spiral

of tissue filled with liquid and thousands of tiny hairs. The hairs on the outside of the spiral are longer

than the hairs on the inside of the spiral. Each hair is connected to a nerve that feeds into the auditory

nerve bundle going to the brain. The longer hairs resonate with lower frequency sounds, and the shorter

hairs with higher frequencies. Thus the cochlea serves to transform the air pressure signal experienced

by the eardrum into frequency information that can be interpreted by the brain as tonality and texture.

This way, we can tell the difference between adjacent notes on a piano, even if they are played equally

loud. The Fourier transform is another mathematical technique for doing a similar thing: resolving any


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IMAGE STEGANOGRAPHY

time-domain function into a frequency spectrum, much like a prism splitting light into a spectrum of

colors. This analogy is not perfect, but it gets the basic idea across.

There is another transform technique that you may come across if you delve deeper into the

mathematics of steganography called the wavelet transform, which is very similar in concept to the

Fourier transform.

6.2.3 SPREAD-SPECTRUM ENCODING

Spread-spectrum encoding is the method of hiding a small or narrow-

band signal, a message, in a larger cover signal. The foundation of this process begins with a spread-

spectrum encoder. The encoder works by modulating a narrowband signal over a carrier. The carrier

signal is continually shifted using a noise generator and a secret key that makes the noise seem random.

The message is embedded in the existing noise of the carrier signal, spreading the narrow signal over a

wide area. This decreases the density of the hidden signal and makes it much more difficult to detect

within the overall carrier signal.

Spread-spectrum encoding allows for very high data rates because

messages can be compressed before being encoded in the carrier signal. Redundant data can also be

added to the signal for error correction. Spread-spectrum is usually very robust because the addition of

noise does not usually destroy the message. However, it is possible to remove the message with noise

reduction filters, which would be used by the intended recipient to extract the message.

Spread-spectrum encoding is a very good method of steganography

because of its difficulty to detect; if it is detected, it is usually more difficult to decipher because the

attacker would also need the secret key used to encode the message.

Direct Sequence Spread Spectrum (DSSS)

Data to be transmitted is divided into small pieces and each piece is allocated to a frequency

channel across the spectrum. Transmitter utilizes a phase varying modulation technique to

modulate each piece of data with a higher data rate bit sequence.


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Frequency Hopped Spread Spectrum (FHSS)

Data carrier frequency is periodically modified (hopped) across a specific

range of frequencies (spreading).The shifting pattern is determined by the chosen code sequence (FSK

– Frequency Shift Key).

Time Hopped Spread Spectrum

Short information burst (chirp ) transmitted with pseudorandom pulse

duration, or transmitted in a random position. Spread Spectrum Steganography has significant potential

in secure communications – commercial and military

6.2.4 REDUNDANT PATTERN ENCODING

Patchwork and other similar tools do redundant pattern encoding, which

is a sort of spread spectrum technique. The Patchwork method is based on a pseudorandom, statistical

process that takes advantage of the human weaknesses to luminance variation. It works by scattering

the message throughout the cover image like a patchwork.This makes the image more resistant to

cropping and rotation and they hide information more thoroughly than by simple masking. They also

support image manipulation more readily than tools that rely on LSB. In using redundant pattern

encoding, you must trade off message size against robustness. For example, a small message may be

painted many times over an image so that if the stego-image is cropped, there is a high probability that

the watermark can still be read. A large message may be embedded only once because it would occupy

a much greater portion of the image area.

Techniques such as Patchwork are ideal for watermarking of images

because as mentioned before, even if the image is cropped, there is a good probability that the

watermark will still be invisible Other techniques encrypt and scatter the hidden message throughout

the image in some pre-determined manner. Scattering the message makes it appear like noise. It is

assumed that even if the message bits are extracted, they will be useless without the algorithm and

stego-key to decode them. Although such techniques do help protect against hidden message

extraction, they are not immune to destruction of the hidden message through image manipulation. The


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IMAGE STEGANOGRAPHY

patchwork approach is used independent of the host image and proves to be quite robust as the hidden

message can survive conversion between lossy and lossless compression

6.2.5 ENCRYPT AND SCATTER TECHNIQUE

The encrypt and scatter technique tries to emulate white noise. White

Noise Storm is one such program that employs spread spectrum and frequency hopping. It does this by

scattering the message throughout an image on eight channels within a random number that is

generated by the revious window size and data channel. The channels then swap rotate, and interlace

amongst each other. Each channel represents one bit and as a result there are many unaffected bits in

each channel.

White Noise Storm also includes an encryption routine to randomize the

bits within an image. The software extracts the LSBs from the cover image and stores them in a file.

The message is encrypted and applied to these bits to create a new set of LSBs. The modified bits are

then injected into the cover image to create the new stego-image. The main disadvantage of applying

White Noise Storm’s encryption method to steganography is the loss of many bits that can be used to

hold information. Relatively large files must be used to hold the same amount of information that other

methods provide with much smaller cover images.


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IMAGE STEGANOGRAPHY

Figure 4 - Steganographic image produced on the left with the stego-tool: White Noise Storm. The

image of Shakespeare is too small to contain the Airfield, but we embedded the text message without

any image degradation.

This technique is a lot harder to extract a message out of than an LSB

scheme because to decode you must first detect that a hidden image exists and extract the bit pattern

from the file. While that is true for any stegoimage you will also need the algorithm and stego key to

decode the bit pattern, both of which are not required to recover a message from LSB. Some people

prefer this method due to the considerable amount of extra effort that someone without the algorithm

and stego-key would have to go through to extract the message. Even though White Noise Storm

provides extra security against message extraction it is just as susceptible as straight LSB to image

degradation due to image processing.

6.3 MODEL BASED TECHNIQUES

Unlike techniques discussed in the two previous subsections, model

based techniques try to model statistical properties of an image, and preserve them in the embedding

process. For example Sallee proposes a method which breaks down transformed image coeffcients into

two parts, and replaces the perceptually insignificant component with the coded message signal.


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IMAGE STEGANOGRAPHY

Initially, the marginal statistics of quantized (non-zero) AC DCT coefficients are modeled with a

parametric density function. For this, a low precision histogram of each frequency channel is obtained,

and the model is fit to each histogram by determining the corresponding model parameters. Sallee

defines the offset value of coefficient within a histogram bin as a symbol and computes the

corresponding symbol probabilities from the relative frequencies of symbols (offset values of

coeffcients in all histogram bins).

In the heart of the embedding operation is a non-adaptive arithmetic

decoder which takes as input the message signal and decodes it with respect to measured symbol

probabilities. Then, the entropy decoded message is embedded by specifying new bin o®sets for each

coeffcient. In other words, the coefficients in each histogram bin are modified with respect to

embedding rule, while the global histogram and symbol probabilities are preserved. Extraction, on the

other hand, is similar to embedding. That is, model parameters are determined to measure symbol

probabilities and to obtain the embedded symbol sequence (decoded message). (It should be noted that

the obtained model parameters and the symbol probabilities are the same both at the embedder and

detector). The embedded message is extracted by entropy encoding the symbol sequence.

Another model based technique was proposed by Radhakrishnan et al in

which the message signal is processed so that it would exhibit the properties of an arbitrary cover

signal, they call this approach data masking. As argued if Alice wants to send an encrypted message to

Bob, the warden Wendy would be able to detect such a message as an encrypted stream since it would

exhibit properties of randomness. In order for a secure channel to achieve covertness, it is necessary to

preprocess the encrypted stream at the end points to remove randomness such that the resulting stream

defeats statistical tests for randomness and the stream is reversible at the other end.


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The authors propose Inverse Wiener filtering as a solution to remove

randomness from cipher streams as shown in Figure. Let us consider the cipher stream as samples from

a wide sense stationary (WSS) Process, E. We would like to transform this input process with high

degree of randomness to another stationary process, A, with more correlation between samples by using

a linear filter, H. It is well known that the power spectrum of a WSS input, A(w), to a linear time

invariant system will have the output with the power spectrum E(w) expressed as E(w) = H(w)^2.A(w).

If E(w) is a white noise process, then H(w) is the whitening filter or Wiener filter. Since the encrypted

stream is random, its power spectral density is flat and resembles the power spectral density of a white

noise process. Then, the desired Wiener ¯lter can be obtained by spectral factorization of(E(w)/A(w))

followed by selection of poles and zeros to obtain the minimum phase solution for H(w).

7. DETECTION

The detection of steganographically encoded packages is called

steganalysis. Steganography tools typically hide relatively large blocks of information where

watermarking tools place less information in an image, but the watermark is distributed redundantly

throughout the entire image . In any case, these methods insert information and manipulate the images


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IMAGE STEGANOGRAPHY

in ways as to remain invisible to the human eye. However, any manipulation to the image introduces

some amount of distortion and degradation of some aspect in the “original” image’s properties. The

tools vary in their approaches for hiding information. Without knowing which tool is used and which,

if any, stegokey is used, detecting the hidden information may become quite complex. However, some

of the tools produce stego-images with characteristics that act as signatures for the steganography

method or tool used.

To begin evaluating images for additional, hidden information, the

concept of defining a “normal” or average image was deemed desirable. Defining a normal image is

somewhat difficult when considering the possibilities of digital photographs, paintings, drawings, and

graphics. Only after evaluating many original images and stego-images as to color composition,

luminance, and pixel7 relationship do anomalies point to characteristics that are not “normal” in other

images.

The simplest method to detect modified files, however, is to compare

them to the originals. Even though stego-images can rarely be spotted by the naked eye, they usually

leave behind some type of fingerprint or statistical hint that they have been modified. It is those

descrepancies which an analysis tool may be able to detect. Since some techniques and their effects are

commonly known, a statistical analysis of an image can be performed to check for a hidden message(s)

in it.

The simplest technique is to measure the entropy of redundant data and

check if its statistical properties have deviated from the data collected from the original image.Since we

do not always have the unaltered cover image readily availible a detection system can compare the

amount of 1’s and 0’s to detect the presence of a stego-image. A similar method of analysis can be used

for JPEG’s but the coefficients of the DCT are looked at instead of individual bits. These simple

methods do not conclusively proove that there is a secret message but are merely the first step. After a

suspected image is found then a dictionary attack must be conducted to verify that there is a hidden

message.

There have been multiple congressional committees dealing with

encryption over the past few years. The US government has done some private contracting to develop

steganography detection tools. One such contract is with WetStone Technologies who have been

contracted to develop a “blind steganography detection prototype”. (Mc Cullah, February 21, 2001)


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There is no doubt more research going on but not all of it will be made public and it is most probable

that the NSA is developing or has detection programs already.

In academia, graduate students Niel Provos and Richard Honeyman at

the University of Michigan have written a web crawling program to detect steganographic images in

the wild. The program has already digested 2 billion JPEG’s on popular sites such as ebay and has so

far found only one stego-image in the wild. The detected image was on an ABC web page that dealt

with the topic of steganography. It had a picture of a B-52 graveyard at Davis-Monthan Air Force Base

embedded into a surreal image of clocks and the earth.

8. DESTROYING STEGANOGRAPHY AND WATERMARKS

Detecting the existence of hidden information defeats the goal of

imperceptibility.Tools in the transform set are far more difficult to detect without the original image for

comparison. Knowledge of an existing watermark may be knows so detecting it is not always


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IMAGE STEGANOGRAPHY

necessary. A successful attack on a watermark is to destroy or disable it . With each of the image and

transform domain tools, there is a trade off between the size of the payload (amount of hidden

information) that can be embedded and the survivability or robustness of that information to

manipulation of the images. The methods devised by the authors for destruction are not intended to

advocate the removal or disabling of valid copyright information from watermarked images, asan illicit

behavior, but to evaluate the claims of watermarks and study the robustness of current methods. Some

methods of disabling hidden messages may require considerable alterations to the stego-image.

Destruction of embedded messages is fairly easy in cases where bit-wise methods are used since these

methods employ the LSBs of images which may be changed with compression of small image

processes. More effort is required with transform set of data hiding tools since the hidden message is

integrated more fully into the cover. A goal for many transform methods is to make the hidden

information (the watermark) such an integral part of the image that the only way to remove or disable it

is to destroy the stego-image. Doing so will render the image useless to the attacker.

Bit-wise methods are vulnerable to small amounts of image processing.

A quick way to destroy many messages hidden with these techniques is to convert the image to a lossy

compression format such as JPEG. Recompressing JPEG images processed with Jpeg-Jsteg will

destroy the message embedded in the DCT coefficients as they are recalculated. The transform set of

techniques that may apply transformations, redundancy, and masking merge the hidden information

with integral properties of the images. These methods are more robust than the bit-wise methods, but

are still vulnerable to destruction.

9. USES OF STEGANOGRAPHY

Steganography can be used anytime you want to hide data.

There are many reasons to hide data but they all boil down to the desire to prevent unauthorized

persons from becoming aware of the existence of a message.


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In the business world steganography can be used to hide a secret chemical formula or plans for

a new invention.

Steganography can also be used for corporate espionage by sending out trade secrets without

anyone at the company being any the wiser.

Steganography can also be used in the non-commercial sector to hide information that someone

wants to keep private.

Spies have used it since the time of the Greeks to pass messages undetected. Terrorists can

also use steganography to keep their communications secret and to coordinate attacks.

Steganography is used by some modern printers, including HP and Xerox brand color laser

printers. Tiny yellow dots are added to each page. The dots are barely visible and contain

encoded printer serial numbers, as well as date and time stamps.

10. SECURITY

A method, SBIPM, for providing the security of our important

information is based on the techniques of signal processing, cryptography, and steganography. The


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security of information has been strengthened by applying scanning, coding, and encryption, cover

processing and embedding techniques in the method. Reshaping step of the method provides robustness

for detecting message correctly in such situation when stego image is distorted. The method developed

is safe from various attacks. Simulation and steganalysis results show that one will not be able to

distinguish the cover and stego images. Thus we conclude that the strength of security achieved is very

high and unauthorized receiver will not be able to get back the original message using exhaustive

without the knowledge of key parameters.

Digital Steganography is interesting field and growing rapidly for

information hiding in the area of information security. It has a vital role in defence as well as civil

applications. In future we will have more secure systems based on this technology.

Several methods for hiding data in, images, text were described, with

appropriate introductions to the environments of each medium, as well as the strengths and weaknesses

of each method. The key algorithm for designing the steganography system has been dealt. Most data-

hiding systems take advantage of human perceptual weaknesses, but have weaknesses of their own. We

conclude that for now, it seems that no system of data-hiding is totally immune to attack.

11. TERRORISTS AND STEGANOGRAPHY

When one considers that messages could be encrypted

steganographically in e-mail messages, particularly e-mail spam, the notion of junk e-mail takes on a


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whole new light. A sender could get messages out and cover their tracks all at once. Rumors about

terrorists using steganography started first in the daily newspaper USA Today on February 5, 2001 in

two articles titled "Terrorist instructions hidden online" and "Terror groups hide behind Web

encryption". In October 2001, the New York Times published an article claiming that Al-Qaeda had

used steganographic techniques to encode messages into images, and then transported these via e-mail

to prepare and execute the September 11, 2001 Terrorist Attack.

The Al-Qaida terrorist network receive money from Muslim sympathizers,

buy computers and then go online and download encryption programs from the web. Here are brief

accounts from USA Today that describe three instances where terrorists have used some sort of

encryption:

Wadih El Hage, one of the suspects in the 1998 bombing of two U.S.

embassies in East Africa, sent encrypted e-mails under various names, including "Norman" and "Abdus

Sabbur," to "associates in al Qaida," according to the Oct. 25, 1998, U.S. indictment against him. Hage

went on trial Monday in federal court in New York.

Khalil Deek, an alleged terrorist arrested in Pakistan in 1999, used

encrypted computer files to plot bombings in Jordan at the turn of the millennium, U.S. officials say.

Authorities found Deek's computer at his Peshawar, Pakistan, home and flew it to the National Security

Agency in Fort Meade, Md. Mathematicians, using supercomputers, decoded the files, enabling the

FBI to foil the plot.

Ramzi Yousef, the convicted mastermind of the World Trade Center

bombing in 1993, used encrypted files to hide details of a plot to destroy 11 U.S. airliners. Philippines

officials found the computer in Yousef's Manila apartment in 1995. U.S. officials broke the encryption

and foiled the plot. Two of the files, FBI officials say, took more than a year to decrypt.

The events that took place on September 11 were obviously very

coordinated and the terrorists must have had to use some form of communication to coordinate their

attacks. Since their communications were not detected, it would lead one to believe that they were

using some type of encryption and/or message hiding system.

Whether or not al-Qaida uses steganography, it would be a very effective

high tech communication method. They can use bulletin boards and other public places where you can

put images as cyber dead drops for stego-images. A dead drop is a place where you drop off a


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deliverable at some pre-determined time and place without ever meeting or directly communicating

with the other party. Of course, communication will have to be initiated but after that, all

communications/exchanges can be made in the manner outlined above.

For covert purposes, this communication technique has two very distinct

advantages over most other forms of communication. The first is that the communication is

asynchronous, which means that it is simpler to implement and helps to avoid suspicion as involved

parties aren’t directly associated with each other. The second reason is that only one of the parties is

required to know who the other is. This is especially valuable if one party is caught then they may not

be able to divulge who they were dealing with, regardless of the interrogation methods used. The last

point makes steganography an especially appealing method of communication to the al-Qaida network

because they operate as cells and the anonymity that dead drops provide will help to avoid uncovering

of the entire network even if some members are caught.

12. MODERN STEGANOGRAPHIC TECHNIQUES


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Thumbnail steganography: It is a new type of steganography designed to increase the

complexity required when attempting to automate steganography detection. It requires the

original image (jpg, gif, etc) as well as the thumbnail to extract the file from the thumbnail.

Embedded pictures in video material (optionally played at slower or faster speed).

Blog steganography: Messages are fractionalyzed and the (encrypted) pieces are added as

comments of orphaned web-logs (or pin boards on social network platforms). In this case the

selection of blogs is the symmetric key that sender and recipient are using. The carrier of the

hidden message is the whole blogosphere.

A new steganographic technique involves injecting imperceptible delays to packets sent over

the network from the keyboard. Delays in keypresses in some applications can mean a delay in

packets, and the delays in the packets can be used to encode data

13. CONCLUSION


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In this seminar, we take an introductory look at steganography. Several

methods for hiding data in, images were described, with appropriate introductions to the environments

of each medium, as well as the strengths and weaknesses of each method.The key algorithm for

designing the steganography system has been dealt. Most data-hiding systems take advantage of human

perceptual weaknesses, but have weaknesses of their own. We conclude that for now, it seems that no

system of data-hiding is totally immune to attack.

During the Clinton administration restrictions on the exportation of

cryptography, hardware and software tools were laxed. After the September 11 attack, there will be no

doubt a very close inspection of many aspects of our national security and there will be new proposals

to try to stop another tragedy from occurring. In our current position if terrorists used a good stego-tool

and a solid encryption algorithm it would be very difficult to discover their plans before they are

executed. Let’s just rely on our government make the correct decisions in the matter because privacy is

important but not to the point where people can use it as shield to kill people.

However, steganography has its place in security. Though it cannot

replace cryptography totally, it is intended to supplement it. Its application in watermarking and

fingerprinting, for use in detection of unauthorised, illegally copied material, is continually being

realised and developed.

Also, in places where standard cryptography and encryption is outlawed,

steganography can be used for covert data transmission. Steganography can be used along with

cryptography to make an highly secure data high way. Formerly just an interest of the military,

Steganography is now gaining popularity among the masses. Soon, any computer user will be able to

put his own watermark on his artistic creations.

14. REFERENCES


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IMAGE STEGANOGRAPHY

1)AN OVERVIEW OF IMAGE STEGANOGRAPHY,T. Morkel 1, J.H.P. Eloff 2, M.S. Olivier 3

Information and Computer Security Architecture (ICSA) Research Group,Department of Computer

Science,University of Pretoria, 0002, Pretoria, South Africa

2) Journal of Electronic Imaging 15(4), 041104 (Oct–Dec 2006), Performance study of common image

steganography and steganalysis techniques

3) Analysis of LSB based image steganography techniques by R.Chandramouli and Nasir Memon

4) Digital image steganography using stochastic modulation,Jessica Fridrich∗ and Miroslav Goljan

Department of Electrical and Computer Engineering, SUNY Binghamton, Binghamton

5) CPSC 350 Data Structures:Image Steganography,Nick Nabavian [email protected] Nov. 28,

2007

6) IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 2, FEBRUARY 2003 221

Steganalysis Using Image Quality Metrics,Ismail Avcıbas¸, Member, IEEE, Nasir Memon, Member,

IEEE, and Bülent Sankur, Member, IEEE

7) Developments in Steganography Published in the Proceedings of the Third International Information

Hiding Workshop,Dresden, Germany, September 29-October 1, 1999. Springer-Verlag Lecture Notes

in Computer Science, 1768) Joshua R. Smith and Chris Dodge

8) Implementation of Spread Spectrum Image Steganography by Frederick S. Brundick and Lisa

M.Marvel

9) Steganalysis of Images Created Using Current Steganography Software,Neil F. Johnson and Sushil

Jajodia,Center for Secure Information Systems,George Mason University

10) Digital image steganography using stochastic modulation

Jessica Fridrich∗ and Miroslav Goljan, Department of Electrical and Computer Engineering, SUNY

Binghamton, Binghamton, NY13902-6000, USA


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mailto:[email protected]

IMAGE STEGANOGRAPHY

11) A StegnographyImplementation ,Beenish Mehboob and Rashid Aziz Faruqui ,Department of

Computer Science and Engineering Bahria University, Karachi, Pakistan

12) International Conference on Computational Intelligence and Multimedia Applications 2007, Data

Security using Data Hiding

13) STEGANOGRAPHIC METHODS, PERIODICA POLYTECHNICA SER. EL. ENG. VOL. 44,

NO. 3–4, PP. 249–258 (2000)

14) A Novel Steganographic Method for Gray-Level Images, International Journal of Computer, Information, and Systems Science, and Engineering 3:1 2009

15) A DCT-based Image Steganographic Method Resisting Statistical Attacks1 Rufeng Chu, Xinggang You, Xiangwei Kong, Xiaohui Ba Department of Electronic Engineering, Dalian University of Technology, Dalian, China

16) A New Image Steganography Based On First Component Alteration Technique, (IJCSIS) International Journal of Computer Science and Information Security,Vol. 6, No. 3, 2009


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