Project Report Libre

5/20/2018 Project Report Libre

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Chapter 1

Introduction

1.1 Steganography

Steganography [34] is the art of hiding information within innocuous cover carriers in ways such

that the hidden message is undetectable. In Greek, stego means covered or secret and

graphy means to write and therefore, steganography becomes covered or secret writing.

The information to be hidden is embedded into the cover object which can be a text matter, some

image, or some audio /video file in such a way that the very existence of the message is

undetected by maintaining the appearance of the resulted object exactly same as the original. The

main goal of steganography is to hide the fact that the message is present in the transmission

medium.

1.2 Steganography vs. Cryptography:

Cryptography [35] is the science of encrypting data in such a way that one cannot understand the

encrypted message, whereas in steganography the mere existence of data is concealed, such that

even its presence cannot be noticed. Using cryptography might raise some suspicion whereas in

steganography the existence of secret message is invisible and thus not known. We can think of

steganography as an extension of cryptography, and it is commonly used under the

circumstances where encryption is not allowed.

1.3 Types of steganography

On the basis of cover object steganography may be of many types like Audio Steganography,

Video Steganography, image Steganography etc. Image Steganography is very popular because

of popularity of digital image transmission over the internet. Image Steganography use

redundancy of digital image [2, 11] to hide the secret data. It may be divided into two categories.

They are spatial-domain methods and frequency-domain ones. In the spatial domain, the secret

messages are embedded in the image pixels directly. In the frequency-domain, however, the


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secret image is first transformed to frequency-domain, and then the messages are embedded in

the transformed.

1.3.1 Spatial domain Techniques:

Least Significant Bit (LSB) substitution- LSB substitution [3, 4] method, which uses

fixed k LSBs in each pixel to embed secret message, is the easiest method to hide

message in an image. However, it is easy to reveal a stego-image produced by the LSB

insertion method.

Distortion technique- In distortion technique some pixel property of cover image is

changed according to secret message and then deflection of distorted from original image

contains secret information.

1.3.2 Transform domain techniques:

If we embed information in spatial domain, it may be subjected to the losses if the image

undergoes any image processing technique like compression, cropping etc. To overcome

this problem we embed the information in frequency domain such that the secret

information is embedded on the significant frequency values while higher frequency part

is omitted. We first apply transformations to the image then data is to be hidden by

changing the values of the transformation coefficients accordingly.

There are mainly three transformation techniques:

Fast Fourier Transform (FFT) Steganography- In this technique two dimensional FFT

is used to convert the cover image into transform domain first and then secret bits are

embedded on the significant coefficients. FFT includes complex term also, hence it

computes more mathematical computations and time complexity is higher than DCTsteganography.

Discrete Cosine Transform (DCT) Steganography- In this technique two dimensional

DCT is used for transformation of cover image [5, 6]. DCT is derived from the FFT,

however it requires fewer multiplications than the FFT since it works only with real


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numbers. Also, the DCT produces fewer significant coefficients in its result, which leads

to greater compression. Hence DCT is the popular technique in the field of steganography

[17].

If after DCT transformation, quantization step is also taken as in Joint Photographic

Experts Group (JPEG) compression [1] then it becomes robust to JPEG compression and

this technique is called as JPEG steganography [26].

Discrete Wavelet Transform (DWT) Steganography- In this technique DWT [9-12] is

used to separate high frequency components from low frequency components and then

replacing the high frequency part by secret data. The embedding capacity of this

technique is far greater than DCT steganography [23].

1.4 Challenges in Steganography

The major challenges of effective steganography are:-

Security of Hidden Communication: In order to avoid raising the suspicions of

eavesdroppers, while evading the meticulous screening of algorithmic detection, the

hidden contents must be invisible both perceptually and statistically. Steganography

techniques should produce high imperceptible Stego-image.

Size of Payload:Unlike watermarking, which needs to embed only a small amount of

copyright information, steganography aims at hidden communication and therefore

usually requires sufficient embedding capacity. Requirements for higher payload and

secure communication are often contradictory. Depending on the specific application

scenarios, a tradeoff has to be sought.

Robustness: Stego-image should provide robustness to image processing techniques like

compression, cropping, resizing etc. i.e. when any of these techniques are performed on

stego-image, secret information should not be destroyed completely.

There is no technique of steganography which provide all the three properties at high level.

There is a trade-off between the capacity of the embedded data and the robustness to certain

attacks, while keeping the perceptual quality of the stego-medium at an acceptable level. It is not


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possible to attain high robustness to signal modifications and high insertion capacity at the same

time [30].

1.5 Objective of the project

In this project my task is to implement 2 techniques of spatial domain steganography(LSB

substitution) and 2 techniques of JPEG steganography (Jsteg and Steghide) taking secret

information as a data file and as an image and to do comparisons between them in terms of

embedding capacity and quality of produced stego-image and robustness to attacks.

1.6 Tool Used:

MATLAB is used as simulator to implement the techniques of steganography. MATLAB

provides highly computing environment and advanced in-built function for image processing.

1.7 Performance evaluation matrices

The parameters under which the performance of the Steganography Techniques is obtained are

as follows:-

1.

Embedding Capacity:It is the maximum size of the secret data that can be embed in cover image

without deteriorating the integrity of the cover image. It can be represented in

bytes or Bit Per Pixel(bpp).

2. Mean Square Error (MSE):

It is defined as the square of error between cover image and stego-image [33].

The distortion in the image can be measured using MSE and is calculated using

Equation 1.

[ ] (1)

Where:

The intensity value of the pixel in the cover image.


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: The intensity value of the pixel in the stego image.M*N: Size of an Image.

3. Peak Signal Noise Ratio (PSNR):

It is defined as the ratio of peak square value of pixels by MSE. It is expressed in

decibel. it measures the statistical difference between the cover and stego-image,

is calculated using Equation 2.

.(2)

4. Histograms:

Histogram is a measure of the number of occurrence of pixels with respect to

particular pixel value [29]. During embedding pixel value changes hence number

of pixel having a particular pixel value changes. These changes can be used to

detect steganography. Hence lesser the difference of histograms of cover and

stego-image indicates more resistivity to detect.

1.8 Achievements

The aim of this project work was to implement various spatial and transform domain

steganographic techniques. The following achievements or we can say objectives were achieved

as follows:

Spatial domain techniques are easy ways to embed information, but they are highly

vulnerable to even small cover modifications.

The embedding capacity of Jpeg steganography is very less than spatial domain

techniques.

The spatial domain techniques provide high PSNR, high perceptual quality and high

embedding capacity but these not provide robustness.

Transform domain provide robustness while providing very less embedding capacity, low

PSNR and low perceptual quality.


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1.9 Organization of Report

The remaining part of the project is organized into four chapters.

Chapter 2: The Spatial Domain techniques of steganography are explained. The two

techniques, LSB substitution and distortion, are explained with algorithm.

Chapter 3: first of all the JPEG compression which is the necessary step of JPEG

steganography, is explained. Then various techniques of JPEG steganography are

described. Two techniques which are implemented in project (Jsteg and Steg-Hide) are

explained with algorithms.

Chapter 4: simulation parameters and results of these four techniques are analyzed

Chapter 5: include the concluding remarks of various results and the future scope of the

project. In the end references of research papers and books are included.


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Chapter 2

Spatial Domain Steganography Techniques

2.1 LSB Substitution:

This is the most common method used. In this type, the data to be hidden is inserted into the least

significant bits of the pixel information. Increase or decrease of value by changing the least

significant bit doesnt change the appearance of the image, such that the resulted stego-image

looks exactly same as the cover image.

A more sophisticated approach is the use of a pseudorandom number generator [13-14] to

spread the secret message over the cover in a rather random manner; a popular approach is the

random interval method. If both communication partners share a stego-key k usable as a seed for

a random number generator, they can create a random sequence k1,.., kl(m) and use the

elements with indices

J1= k1

Ji= ji-1+ ki, i 2

for information transfer. Thus, the distance between two embedded bits is determined pseudo

randomly. Since the receiver has access to the seed k and knowledge of the pseudorandom

number generator, he can reconstruct kiand therefore the entire sequence of element indices j i.

If the message size increases, collisions must be taken into account. To overcome the

problem of collisions, we keep track of all cover bits which have already been used for

communication in a set B. If during the embedding process one specific cover-element has not

been used prior, we add its index to B and continue to embedding. If, however, the index of the

cover-element is already contained in B, we discard the element and choose another cover-

element pseudo randomly. At the receiver side, similar technique is to be followed.

These classic LSB steganography have a common weak point. The sample value changes

dissymmetrically. When the LSB of cover medium sample value is equal to the message bit, no

change should be made. Otherwise, change the value 2n to 2n+1 or 2n+1 to 2n. But the changes


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from 2n to 2n-1 or from 2n+1 to 2n+2 will never emerge. This dissymmetry is utilized by

steganalysis such as Chi-square analysis [16] and RS pair analysis [19-20].

In this project LSB with pseudo random generator is implemented. Matlab inbuilt

pseudo-random number generator is used for this purpose and seed to this is taken as key of

steganography. First of all an array of random numbers, with the length equal to secret bit-

stream, is generated using key. Then with the help of this array, different pixel positions are

calculated. Now secret bits are embedded to LSB of these pixels. The algorithm for embedding

process is as below:

Embedding Algorithm:

Input: cover image, key, secret message

Procedure:

Step1: Convert the secret message into bit stream (Length L)

Step2: Generate L number of pseudo random number using seed key

Step3: Calculate the non-collide L pixel positions in the cover image

Step4: while complete bit stream not embedded

{ Replace LSB of pixel denoted by ith pixel position, with secret bit

Insert pixel into cover image

}

End

Output: Stego-image

On the receiver side, first of all the pixel positions are calculated in the same way with the use of

the same key. Then secret bit-stream is formed by the LSBs of these pixels. The Extraction

algorithm is as below:


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Extraction Algorithm:

Input: stego-image, key

Procedure:




Step4: for i=1 to L

{ Get lsb of pixel denoted by ith pixel position

Append this lsb into secret bit stream

}

Step5: Convert secret bit stream into secret message

End

Output: secret message

Advantages:

There is less chance for degradation of the original image.

More information can be stored in an image i.e. hiding capacity is more. Simple and less complex.

Disadvantages:

Less robust, the hidden data can be lost with image manipulation.

Hidden data can be easily detected by simple attacks.

Requirement of high transmission rate due to large size of stego image.


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2.2 Distortion:

Distortion techniques [10] require the knowledge of the original cover in the decoding process.

We apply a sequence of modifications to a cover in order to get a stego-image in such a way that

it corresponds to a specific secret message for embedding. Receiver measures the differences to

the original cover in order to reconstruct the sequence of modifications applied by sender, which

corresponds to the secret message.

Using a similar approach as in LSB, the sender first chooses l(m) different cover-pixels

he wants to use for information transfer. Such a selection can again be done using pseudorandom

number generators or pseudorandom permutations. To encode a 0 in one pixel, the sender leaves

the pixel unchanged; to encode a 1, he adds a random value x to the pixel's color. Although this

approach is similar to a substitution system, there is one significant difference: the LSB of the

selected color values do not necessarily equal secret message bits. In particular, no cover

modifications are needed when coding a 0. Furthermore, x can be chosen in a way that better

preserves the cover's statistical properties.

The receiver compares all l(m) selected pixels of the stego-object with the corresponding

pixels of the original cover. If the ith

pixel differs, the ith

message bit is a 1, otherwise a 0.

In this project the value of x is taken as 1 so that minimum deflection from cover imagewill produced. We defined a middle level of pixel value, such that if the pixel value is greater

than this value than x is added to pixel value otherwise x is subtracted from the pixel value. In

this way the high PSNR will produce and no problem of overflow will occur.

The embedding algorithm for this technique is as below:


Input: cover image, key, secret message

Procedure:





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Step4: while complete bit stream not embedded

{ If secret bit=1

{ If pixel value < 128

{ increase pixel value by x }

Else

{ Decrease pixel value by x }

}

}

End

Output: Stego-image

On the receiver side, first of all the difference between the pixel values of cover image and stego-

image is calculated. Then pixel positions are calculated in the same way with the use of the key

and pseudo random number generator. If the difference at a location is 0 then secret bit is taken

as 0 otherwise it is taken as 1. The algorithm for extraction process is as below:

Extraction Algorithm:

Input: cover image, key, stego-image

Procedure:




Step4: Calculate the difference between Cover image and Stego-image

Step5: for i=1 to L

{ If value of pixel difference=0

{ Secret bit =0

Else

Secret bit=1 }

}


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Step 6: convert the bit stream into secret message

End

Output: secret message

Advantage:

Less degradation of cover image than LSB i.e. less MSE will produce.

Embedding capacity is highest.

Simple and less complex.

Disadvantage:

In many applications, this technique is not useful, since the receiver must have access to

the original covers.

If someone also has access to original cover, he can easily detect the cover modifications

and has evidence for a secret communication.

Requirement of high transmission rate due to large size of stego-image.


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Chapter 3

Transform Domain Steganography Techniques

In the project JPEG steganography is implemented which use Discrete Cosine Transform to

convert image into frequency domain. Before discussing these techniques in detail we first

discuss the JPEG compression because JPEG steganography is just the modified version of JPEG

compression.

3.1 JPEG Compression

The Joint Photographic Experts Group developed the JPEG [1] algorithm in the late 1980s and

early 1990s. to address the problems of that era, specifically the fact that consumer-level

computers had enough processing power to manipulate and display full color photographs.

However, full color photographs required a tremendous amount of bandwidth when transferred

over a network connection, and required just as much space to store a local copy of the image.

Other compression techniques had major tradeoffs. They had either very low amounts of

compression, or major data loss in the image. Thus, the JPEG algorithm was created to

compress photographs with minimal data loss and high compression ratios.

JPEG compression and decompression consist of 4 distinct and independent phases.

First, the image is divided into 8 x 8 pixel blocks. Next, a discrete cosine transform is applied to

each block to convert the information from the spatial domain to the frequency domain. After

that, the frequency information is quantized to remove unnecessary information. Finally,

standard compression techniques compress the final bit stream. The basic flow of JPEG is as

below:


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JPEG compression interface

Source image

8 x 8 blocks

Figure 3.1: Basic block diagram of JPEG Compression

Phase One: Divide the Image

Attempting to compress an entire image would not yield optimal results. Therefore,

JPEG divides the image into matrices of 8 x 8 pixel blocks. If the image dimensions are

not multiples of 8, extra pixels are added to the bottom and right part of the image to pad

it to the next multiple of 8 so that we create only full blocks. The dummy values are

easily removed during decompression. From this point on, each block of 64 pixels is

processed separately from the others, except during a small part of the final compression

step.

It may optionally include a change in colorspace. Normally, JPEG will convert

RGB colorspace to YCbCr colorspace. In YCbCr, Y is the luminance, which represents

the intensity of the color. Cb and Cr are chrominance values, and they actually describe

the color itself. YCbCr tends to compress more tightly than RGB.

Phase Two: Conversion to the Frequency Domain

In JPEG all 8 x 8 blocks are converted to frequency domain using DCT. The Discrete

Cosine Transform (DCT) is derived from the FFT, however it requires fewer

multiplications than the FFT since it works only with real numbers. Also, the DCT

DCT Quantizer Huffman-coder

Quantization

tableTable

CompressedIma e


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produces fewer significant coefficients in its result, which leads to greater compression.

That is why DCT is used for transformation purpose.

The 2D discrete cosine transform equation is given below:

7

0

7

0 16

12cos

16

12cos,

4

1,

x y

vyuxyxfvCuCvuF

where f (x, y) is the 8-bit image value at coordinates (x, y) and F (u, v) is the new entry in

the frequency matrix. Also, C(x) = 1/if x is 0, and C(x) = 1 for all other cases.

The frequency domain matrix contains values from -10241023. The upper-left entry,

also known as the DC value, is the average of the entire block, and is the lowest

frequency cosine coefficient. As we move right the coefficients represent cosinefunctions in the vertical direction that increase in frequency. Likewise, as we move

down, the coefficients belong to increasing frequency cosine functions in the horizontal

direction. The highest frequency values occur at the lower-right part of the matrix. The

higher frequency values also have a natural tendency to be significantly smaller than the

low frequency coefficients since they contribute much less to the image. Typically the

entire lower-right half of the matrix is factored out after quantization. This essentially

removes half of the data per block, which is one reason why JPEG is so efficient at

compression. Computing the DCT is the most time-consuming part of JPEG

compression. Thus, it determines the worst-case running time of the algorithm.

Phase Three: Quantization

Having the data in the frequency domain allows the algorithm to discard the least

significant parts of the image. The JPEG algorithm does this by dividing each cosine

coefficient in the data matrix by some predetermined constant, and then rounding up ordown to the closest integer value. These predefined constants are then entered into

another 8 x 8 matrix, called the quantization matrix. Each entry in the quantization

matrix corresponds to exactly one entry in the frequency matrix. A typical quantization

matrix will be symmetrical about the diagonal, and will have lower values in the upper

left and higher values in the lower right. Since any arbitrary values could be used during


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quantization, the entire quantization matrix is stored in the final JPEG file so that the

decompression routine will know the values that were used to divide each coefficient.

The constant values that are used in the division may be arbitrary, although research has

determined some very good typical values. The standard quantization matrix used in

JPEG is as below:

16 11 10 16 24 40 51 61

12 12 14 19 26 58 60 55

14 13 16 24 40 57 69 56

14 17 22 29 51 87 80 62

18 22 37 56 68 109 103 77

24 35 55 64 81 104 113 92

49 64 78 87 103 121 120 101

72 92 95 98 112 100 103 99

Standard quantization matrix

Dividing by a high constant value can introduce more error in the rounding process, but

high constant values have another effect. As the constant gets larger the result of the

division approaches zero. This is especially true for the high frequency coefficients,

since they tend to be the smallest values in the matrix. Thus, many of the frequencyvalues become zero. Phase four takes advantage of this fact to further compress the data.

The algorithm used to calculate the quantized frequency matrix is fairly simple. It

takes a value from the frequency matrix (F) and divides it by its corresponding value in

the quantization matrix (Q). This gives the final value for the location in the quantized

frequency matrix (Fquantize). The quantization equation that is used for each block in the

image is below:

5.0,,,

vuQvuFvuFQuantize

By adding 0.5 to each value, we essentially round it off automatically when we truncate

it, without performing any comparisons.


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Phase Four: Entropy Coding:

After quantization, the algorithm is left with blocks of 64 values, many of which are zero.

Of course, the best way to compress this type of data would be to collect all the zero

values together, which is exactly what JPEG does. The algorithm uses a zigzag ordered

encoding, which collects the high frequency quantized values into long strings of zeros.

To perform a zigzag encoding on a block, the algorithm starts at the DC value and begins

winding its way down the matrix, as shown in figure. This converts an 8 x 8 table into a

1 x 64 vector.

Figure 3.2: Zigzag ordered encoding

All of the values in each block are encoded in this zigzag order except for the DC value.

For all of the other values, there are two tokens that are used to represent the values in the

final file. The first token is a combination of {size, skip} values. The size value is the

number of bits needed to represent the second token, while the skip value is the number

of zeros that precede this token. The second token is simply the quantized frequency

value, with no special encoding. At the end of each block, the algorithm places an end-

of-block sentinel so that the decoder can tell where one block ends and the next begins.

The first token, with {size, skip} information, is encoded using Huffman coding.

Huffman coding scans the data being written and assigns fewer bits to frequently

occurring data, and more bits to infrequently occurring data. Thus, if a certain values of


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size and skip happen often, they may be represented with only a couple of bits each.

There will then be a lookup table that converts the two bits to their entire value. JPEG

allows the algorithm to use a standard Huffman table, and also allows for custom tables

by providing a field in the file that will hold the Huffman table. DC values use delta

encoding, which means that each DC value is compared to the previous value, in zigzag

order. This is the only instance where blocks are not treated independently from each

other. The difference between the current DC value and the previous value is all that is

included in the file.

3.2 JPEG Steganography

JPEG steganography is more important and popular because stago-image produce by these

techniques are robust to jpeg compression. In this technique secret data is embed after

quantization phase of JPEG compression. Only significant quantized DCT coefficients are

modified according to secret bits. Remaining steps are similar to JPEG compression. In this way

stego-image is produced in .jpg format directly. The basic flow diagram of embedding and

extraction process is as below:

Figure 3.3: Block diagram of Embedding process of JPEG Steganography

Cover

image

Stego

image

8x8

blocks

DCT

QuantizerEmbedding Entropy

coding

Secret message

into bit stream

conversion

Quantization

table

SecretMessage


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Figure 3.4: Block diagram of Extraction process of JPEG Steganography

Following are some JPEG steganography techniques :

Jsteg [7]

Steghide

Outguess [18]

F3,F4,F5 [15],[21],[25]

J3 [31]In the project only 2 techniques Jsteg and Steghide is implemented and these are explained

below with algorithms.

3.2.1 Jpeg-Jsteg:

In JpegJsteg, the secret messages are embedded in LSB of quantized DCT coefficients whose

values are not (0, 1, or 1). Its execution steps are described briefly as follows. First, JPEG

partitions a cover-image into non-overlapping blocks of 8*8 pixels, and then it uses DCT to

transform each block into DCT coefficients. The results of the DCT coefficients are scaled

according to a quantization table.

The standard quantization table is listed in Fig. , which is a matrix that contains 64

coefficients. Next, JpegJsteg uses an encryption algorithm to protect the message. A message

Distorted

Cover

image

Stego

image

8x8

blocks

DCT

QuantizerEmbedding Entropy

coding

Quantization

table

Secret

Message

Secret

message in

bit stream


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after encrypting is called secret message S={ s1, s2, s3, s4, . . .,sn }, where siis a secret bit. After

the above steps, JpegJsteg embeds si into LSB of quantize DCT coefficients whose values are

not 0, 1, or 1. The bit-flip diagram of J-steg is as below:

JPEG Coefficients

in cover image -4 -3 -2 -1 0 1 2 3 4 5

0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1

JPEG coefficients in

Stego-image

Figure 3.5: Bit-flip in JPEG-JSTEG

The embedding sequence employed in JpegJsteg is in the zigzag scan order, which is

listed in fig. After embedding the secret message in each block, JpegJsteg uses Huffman

coding, Run-Length coding, and DPCM of JPEG entropy coding to compress each block.

Finally, JpegJsteg obtains a JPEG stego-image.

The message capacity of JpegJsteg is limited. If there are many quantifized coefficients

equal to 0, 1, or 1, then the message capacity of JpegJsteg will be decreased. Besides, in DCT

transformation, most important coefficients are located around the low-frequency part. Jpeg

Jsteg modifies the quantized DCT coefficients right in the low-frequency part. Therefore, the

image quality of JpegJsteg is degraded, especially when the cover-image undergoes a high

compression ratio.


Input: secret message, cover image

Procedure:

Step1. Convert the secret message into bit stream

Step2. Divide the cover image into 8x8blocks


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Step3. Calculate DCT coefficients for each block

Step4. Quantize the coefficients

Step5. while complete message not embedded do

{ get next DCT coefficient

if DCT 0 , DCT 1 and DCT -1 then

{ get next bit from message

replace DCT LSB with message bit }

}

Step6. De-quantize and take inverse DCT to obtain stego-image

End.

Output: Stego- image

Extracting Algorithm:

Input: Stego image

Procedure:Step1. Divide the stego image into 8x8 blocks



Step4. While secret message not completed do

{ Get next DCT coefficient

If DCT 0 , DCT 1and DCT -1 then

{ Concatenate DCT LSB to secret message bit steam }

}

Step5. Convert secret message bit stream into the message.

End.

Output: Secret message


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Advantage:

Robust to JPEG compression.

Less bandwidth requirement for stego image transmission because its size can be

reduced.

Disadvantage:

Only few secret messages can be embedded in cover image.

High detectability due to the fact that it introduces characteristic artifacts into the first

order statistics (histogram) of DCT coefficients.

3.2.2 Steg-Hide:

Steghide uses a graph-theoretic approach to steganography. Steghide embeds by swapping DCT

coefficients and thus avoids changing the histogram. The fact that the embedding is done by

exchanging pixel values implies that the first-order statistics (i.e. the number of times a color

occurs in the picture) is not changed. During the encoding process, the sender splits the cover-

image in 8 x 8 pixel blocks; each block encodes exactly one secret message bit. The embedding

process starts with selecting a pseudorandom block bi which will be used to code the ith message

bit.

Before the communication starts, both sender and receiver have to agree on the location

of two DCT coefficients, which will be used in the embedding process; let us denote these two

indices by (u1,v1) and (u2, v2). The two coefficients should correspond to cosine functions with

middle frequencies; this ensures that the information is stored in significant parts of the signal

(hence the embedded information will not be completely damaged by JPEG compression).


Input: secret message, cover image

Procedure:

Step1. Convert the secret message into bit stream

Step2. Divide the cover image into 8x8blocks


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Step5. While complete message not embedded do

{ Get next block

Ifblock(u1,v1) block(u2,v2) then

{ get next bit from message stream

if bit =1 and block(u1,v1)< block(u2,v2)

{ swap(block(u1,v1),block(u2,v2) }

If bit =0 and block(u1,v1)> block(u2,v2)

{ swap(block(u1,v1),block(u2,v2) }

}

}

Step6. De-quantize and take inverse DCT to obtain stego-image

End.

Output: Stego- image

Extracting Algorithm:

Input: Stego image

Procedure:

Step1. Divide the stego image into 8x8 blocks



Step4. While secret message not completed do

{ Get next block

If block(u1,v1) block(u2,v2)then

{ If block(u1,v1)< block(u2,v2)

{ concatenate 0 to bit stream

Else

Concatenate 0 to bit stream }


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}

}

Step5. Convert secret message bit stream into the message.

end.

Output: Secret message

Advantage:

It cannot be detected by steganalysis which uses first order characteristics, because

histogram is preserved [27]

It provide Robustness to JPEG compression.

Disadvantage

Its embedding capacity is less than Jsteg too.

Cover size in which message embedded is always multiple of 8 which can be used to

detect the presence of secret message.


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Chapter 4

Simulation and results

4.1 Simulation Setup:

The MATLAB Version 7.13.0.564 (R2011b) is used to implement and simulate 4 steganography

techniques: LSB & Distortion of spatial domain steganography and Jsteg & Steg-hide of

transform domain steganography. MATLAB is used because of large number of advanced

inbuilt functions and image processing toolbox. We take results for various color cover image for

different secret files.

The various simulation parameters are as given below:

Cover image pixel size (N x N x3) N=250, 500, 1024, 2048

Secret text file size (kb) 1,5

Image type Tiff, jpg

Simulation Tool MATLAB 7.13.0.564

Pseudo Random Number Generator MATLAB rng with key 2

Secret data for JPEG Steganography Ajay Nain

Minor Project YMCA

Table 4.1: Simulation parameter setup

The following results are taken for evaluation of these techniques:

4.2 Perceptual Quality:

We saw that visual quality of spatial domain steganography is better than transform domain

techniques. The stego-image produced by all the 4 techniques on baby cover image with size of

500 x 500 x 3 are given below:


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Figure 4.1: original cover image (500 x 500) color

(a) Stego-image with LSB technique (b) Stego-image with distortion technique

Figure 4.2: Stego- image produced by Spatial domain techniques with secret file of 5 kb .

We see that there is no so much change in perceptual quality of image to detect visual changes

i.e. quality of embedded image is not degraded by these techniques.


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(a) Stego-image with Jsteg (b) Stego-image with Steg-hide

Figure 4.3: Stego- image produced by transfer domain techniques

Result shows that visual quality of stego-image produced by spatial domain techniques is less

than that of produced by transform domain techniques. In spatial domain techniques as the size

of secret file changes the more degradation is produced but in case of the degradation of image is

not depend upon size of secret file so much.

4.3 Embedding Capacity

It is the size of the secret data that can be embed in cover image without deteriorating the

integrity of the cover image.It can be represented in bytes or bit per pixel (bpp). It depends upon

the characteristics of cover image and the embedding algorithm used for steganography.

The table shows the embedding capacity ofthe 4 techniques for different cover image:


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Image size(N x

N x 3) N=

LSB

Substitution

Steganography

Distortion

Steganography

Jsteg

Steganography

Steg-hide

Steganography

250 23437 23437 61 8

500 93750 93750 136 30

1024 393216 393216 5798 797

2048 1572864 1572864 7843 829

Table 4.2: Embedding Capacity

Figure 4.4: Comparison of embedding capacity

Result shows that embedding capacity of spatial domain capacity is fix large quantity for a cover

image size but capacity of transform domain techniques is very less and it is not fixed for a given

size of cover image, it depends upon characteristics of cover image .

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

N=250 N=500 N=1024 N=2048

Emb

eddingCapacity

Image Size

Embedding Capacity

LSB Substitution

Distortion

Jsteg

Steghide


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4.4 Mean Square Error (MSE):

The results of the all techniques for the given setup parameters are in the following table:

Image size(N x N x

3) N=

LSB

Substitution

Steganography

Distortion

Steganography

Jsteg

Steganography

Steg-hide

Steganography

250 5.84E-07 1.11E-07 8.81E-05 8.87E-05

500 3.63E-08 6.94E-09 1.39E-05 1.45E-05

1024 3.95E-10 3.41E-10 2.20E-05 2.69E-05

2048 2.63E-11 2.13E-11 1.11E-06 1.23E-06

Table 4.3: MSE

Figure 4.5: Comparison of Mean Square Error

0.00E+00

1.00E-05

2.00E-05

3.00E-05

4.00E-05

5.00E-05

6.00E-05

7.00E-05

8.00E-05

9.00E-05

1.00E-04

N=250 N=500 N=1024 N=2048

MSE

Image Size

Mean Square Error

LSB Substitution

Distortion

Jsteg

Steghide


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We can analyze from the results that MSE for spatial domain techniques is very less than that of

for transform domain technique. In case of transform domain techniques the lossy compression

step of jpeg compression i.e. quantization is performed in the embedding process and hence very

large MSE is produced and quality of cover image degraded more.

4.5 Peak Signal to Noise Ratio (PSNR):

It is the measure of quality of the image by comparing the cover image with the stego-image.

High PSNR indicates good perceptual quality of stego-image. The results of PSNR for all the

techniques are in following table:

Image Size(N x

N x 3) N=

Substitution

Steganography

Distortion

Steganography

Jsteg

Steganography

Steg-hide

Steganography

250 110.4659 117.6791 88.6813 88.6532

500 122.5315 129.7203 96.6949 96.5209

1024 142.169 142.7986 94.7016 93.8368

2048 153.9334 154.8398 107.6790 107.2257

Table 4.4: PSNR in db

Figure 4.6: Comparison of PSNR for 4 techniques

0

20

40

60

80

100

120

140

160

180

N=250 N=500 N=1024 N=2048

PSNRindb

Image Size

PSNR

LSB Substitution

Distortion

Jsteg

Steghide


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4.6 Histograms:

Histogram is a measure of the number of occurrence of pixels with respect to particular pixel

value. During embedding pixel value changes hence number of pixel having a particular pixel

value changes. These changes can be used to detect steganography. We take histograms for baby

color image with size of (500 x 500) for all the four the techniques separately.

4.6.1 Histograms of spatial domain techniques:

For spatial domain techniques, we have to take 3 histograms each for red, green and blue color

plane. Hence 3 histograms of cover image, 3 histograms for stego-image and 3 differences are to

be taken for analysis one technique. For better technique difference histogram or difference of

occurrence is less. Below are the results for both the spatial domain techniques:

Figure 4.7: Cover image histograms for red, green and blue color plane respectively

Figure 4.8: Stego-image histograms for red, green and blue color plane respectively


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Figure 4.9: Difference between histograms of Cover image and Stego-image for red, green and blue color plane

with secret file of 5 kb & (500 x 500 x 3) cover image

We see that histogram of cover image and stego-image seems to be similar but by plotting

difference between the two we can analyze the change in statistical properties of cover image. As

the size of secret file increases the difference is increases.

Figure 4.10: stego-image histograms for red, green and blue color plane respectively


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Figure 4.11: Difference between histograms of Cover image and Stego-image for 3 color plane with (500

x500) cover image and secret file of 5 kb

We see that difference between histograms by distortion technique is lesser than LSB

substitution technique with same cover image and secret data file.

4.6.2 Histograms of Transform domain techniques:

For transform domain techniques quantized coefficients are used for calculation of histogram

instead of pixel value. The histogram results of jsteg and Steg-hide are below:

Figure 4.12: Histogram of Cover Image


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Figure 4.13: Histogram of stego-image produced by Jsteg

(i) (ii)

Figure 4.14: Difference between original and Stego-image histogram(i) with JPEG compression (ii) without

JPEG compression; stego-image is save in tif format


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Figure 4.15: Histograms of stego-image produced by Steg-hide

(i) (ii)

Figure 4.16: Difference between original and Steg-hide produced stego-image histogram (i) with JPEG compression

(ii) without JPEG compression; stego-image is save in tif format

As we see if no compression is performed after embedding process the difference between the

histogram is zero, hence this technique cannot be detect by steganalysis which use first order

statics of stego-image


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Chapter 5

Conclusion and Future Scope

5.1 Conclusion

Spatial domain techniques are easy ways to embed information, but they are highly vulnerable to

even small cover modifications. Hence the size of stago-image cannot be reduced. An attacker

can simply apply signal processing techniques in order to destroy the secret information entirely.

In many cases even the small changes resulting out of lossy compression systems yield to total

information loss. Transform domain methods hide messages in significant areas of the cover

image which makes them more robust to attacks, such as compression, cropping, and some

image processing. Hence lossy compression i.e. Jpeg compression can be performed and size of

stago-image can be reduced. But the disadvantage of Jpeg-Steganography is that only few

messages can be embedded in the cover-image. The embedding capacity of Jpeg steganography

is very less than spatial domain techniques.The spatial domain techniques provide high PSNR,

high perceptual quality and high embedding capacity but these not provide robustness. On the

other hand transform domain provide robustness while providing very less embedding capacity,

low PSNR and low perceptual quality.

5.2 Future Scope

We see there is a tradeoff between the three properties, perceptuality, embedding capacity and

robustness. The new techniques should be develop to maintain the three properties at high level.

The few areas which are still open in steganography are as below:

Wavelet transform can be used to increase the embedding capacity while maintaining the

robustness of Stego-image.

Hamming coding or Matrix coding can be used to reduce the impact of steganography i.e.

to increase the PSNR.

Cryptography techniques like RSA, AES and hash functions can also be used with

steganography to provide more security.


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