steganography final ppt

8/3/2019 Steganography Final Ppt

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MAJOR PROJECT PRESENTATION

TOPICSTEGANOGRAPHY

PROJECT GUIDE

MR. RAJIV ARORA

TEAM MEMBERS-

ABHILASHA JAIN(108/IT/05)GAURAV TOLANI (129/IT/05)

SURUCHI SHARMA(122/IT/05)

YOGITA(064/IT/05)


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WHAT IS STEGANOGRAPHY?

Art and Science of hiding communication

A steganographic system embeds hidden content inunremarkable cover media

A steganographic system consists of :

Identifying covers medium redundant bits Embedding process which creates astego medium by

replacing the redundant bits with hidden message data


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OVERVIEW OF STEGANOGRAPHY

To send a hidden message, for example,

1. Alice creates a new image with digital camera

2. Alice supplies the steganographic system with her shared secret and message

3. The steganographic systems uses the shared secret to determine how the hidden messageshould be encoded in the redundant bits

4. The result is the stego image that Alice sends to Bob

5. When Bob receives the image, he uses the shared secret and the agreed steganographic

system to retrieve the hidden message


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COMPUTER STEGANOGRAPHY: TWOPRINCIPLES

Digitized images or sound can be altered without

losing theirfunctionality

Human inability to distinguish minor changes in

image color or sound quality


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OUR PREVIOUS RESEARCH

Minor Project-

-Thorough study of concepts and varioustechniques involved with Steganography.

-Study of various concepts of

LSB embedding,

Masking and Filtering,

Discrete Cosine Transform,

Bit Plane Methods,Palette based techniques n much more


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OUR STEPMAJOR PROJECT

For our major project, our team decided to work upon the

transformational domain to implement a steganography algorithm

of DCT (Discrete Cosine Transform) method.

The discrete cosine transform (DCT) helps separate the image into

parts (or spectral sub-bands) of differing importance (with respect

to the image's visual quality).

The DCT is similar to the discrete Fourier transform: it transforms

a signal or image from the spatial domain to the frequency domain.


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DISCRETE COSINE TRANSFORM

The forward equation, for image A, is

N

yv

N

xuyxavCuC

Nvub

N

x

N

y 2

)12(cos

2

)12(cos),()()(

2),(

1

0

1

0

N

yv

N

xuvubvCuC

Nyxa

N

u

N

v 2

)12(cos

2

)12(cos),()()(

2),(

1

0

1

0

The inverse equation, for image B, is

Here

otherwise

uifuC

0

1)( 2

1


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WHY WE CHOSE JPEG AND DCT ??

JPEG uses DCT to compress an image

Many different approaches to use DCT to hideinformation

Message is embedded in signal, not noise

Studies on visual distortions conducted by source codingcommunity can be used to predict the visible impact ofthe hidden data in the cover image

Can be implemented in compressed domain, saving time


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Discrete Cosine Transform

Basic idea:

1. Convert image to YIQ color space

2. Each color plane is partitioned into 8x8 blocks

3. Apply DCT to each block

4. Values are quantized by dividing with presetquantization values (in a table)

5. Values are then rounded to nearest integer


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STEGANOGRAPHY TECHNIQUE:DISCRETE COSINE TRANSFORM

peppers.bmp 8x8 DCTBlock

The DC coefficient receives the data to be hidden.


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IMPORTANTPOINTSREGARDINGDCT

i. For most images, much of the signal energy lies at lowfrequencies; these appear in the upper left corner ofthe DCT.

ii. Compression is achieved since the lower right valuesrepresent higher frequencies, and are often small -small enough to be neglected with little visibledistortion.

iii. it turns out that cosine functions are much moreefficient (fewer are needed to approximate a typicalsignal), whereas for differential equations the cosinesexpress a particular choice of boundary conditions


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12

JPEG COMPRESSION INTERFACE

DCT QUANTIZER

QUANTIZER

TABLE

ENTROPYENCODER

COMPRESSEDIMAGE

8 X 8BLOCK

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

(0,0)


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13

ONE APPROACHUSING DCT

The sender and receiver agree ahead of time onlocation for two DCT coefficients in the 8 x 8 block

Middle frequencies with same quantization value:

Location 1 is (4,1) & Location 2 is (3,2)

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


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OUR APPROACH-THE MODEL BASEDSTEGANOGRAPHY

In model based steganography technique we use the concept ofDCT compression and steganography parallaely.

In this method we first try to model our cover image the best wecan and then use this model to embed our message.

This approach will hopefully shift the emphasis which has up tonow been placed on embedding methods towards methods based

on statistical models.

That is, we can start asking how to best model our cover datarather than trying to anticipate specific attacks or invent cleverways to flip least significant bits.


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Measurements of Interest

Capacity:

/

Embedding Efficiency:

/


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Some Steganography Methods

Method Coefficient HistogramMaximumCapacity

Embeddingefficiency

JSteg 13% 2

F5 13% 1.5

Outguess 6.5% 1


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Model-based Steganography

Cover xis an instance of a random variable Xdistributed according to model: PX

x= ( xa, xb)

Choose x0= (xa, x0b ) to encode a message M

while maintaining model statistics PX


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Model-Based Steganography: Decoding


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Capacity

Maximum capacity = entropy of PXb|Xa:

Entropy codec designed to achieve the entropy limit


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Model CDF

Cumulative density function easy to calculate:

Used to integrate density function for a given

histogram bin


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Fitting the Model Parameters

Parameters p, sfit by maximum likelihood:

where his a coefficient histogram


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Model Fit to Histogram

-30 -20 -10 0 10 20 3010

-4

10-3

10-2

10-1

100

log

probability

histmodel


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xa

Embedding

xa

xb{0,1} step size = 2 xa= bin group

xb= offset (like LSB)

step size = 3

xa is lower precision

3 offsets per groupxb{0,1,2}


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Embedding Efficiency

Embedding rate =

where p= P(xb

= 0 | xa)

Change rate =

Efficiency =


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OUTLINEOFENCODINGALGORITHM

Given a cover image in JPEG format, and an encrypted message, generate low precision(bin size > 1) histograms of coefficient values. This information comprises x.

Fit the p and s parameters of our parametric model to each histogram by maximumlikelihood.

Assign symbols to represent the offset of each coefficient within its respective histogram

bin. These symbols comprise x.Compute the probability of each possible symbol foreach coefficient using the model cdf.

Choose a pseudo-random permutation to determine the ordering of the coefficients.

Pass the message, and the symbol probabilities computed in step 3 in the order specifiedby step 4 to a non-adaptive arithmetic decoder in order to obtain symbols specifying thenew bin offsets for each coefficient. The resulting symbols comprise x.

Compute the new coefficients from the histogram bin indices (xa) of the symbol offsets(x').


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OUTLINEOFDECODINGALGORITHM

Same as embedding algorithm steps 1-4.

Pass the symbols and symbol frequencies obtained in steps1-4 to the non-adaptive arithmetic encoder to obtain the

original message.


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Future Possibilities

Use extra capacity to correct additional statistics:blockiness, wavelet statistics

Improve model:

Dependencies between coefficients

Embed in wavelet domain

JPEG2000, MP3, MPEG,


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OVERALLSYSTEM

OVERVIEW


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USE CASE

DIAGRAM


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USE CASE DIAGRAM Select The Image To Be Used AsCover Image

Create The Secret Message To Be

Embedded

Encode The Message Using

Symbol Frequencies

Arithmetic Decoding

Encoder

Verifying Original And Stego Image

Calculate The DCT Of The Image

Computing Symbol Frequencies

Decode The Message

Decoder

Arithmetic Encoding


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SEQUENCE

DIAGRAMS

SEQUENCE DIAGRAM FOR ENCODING


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SEQUENCE DIAGRAM FOR ENCODING

Window:UserInterface

Stegtest:MainFunction

Jpeg Encoder Get SymbolFrequencies

Steg Encoder ArithmeticDecoder

Show Image

1.LoadImageAndMessageAndCallStegtest():image,

message

2.CalculateDctOfImage():void

3.ShowOriginalImage():void

4.CallsJpegEncoder():OriginalImage,Message

5.EmbedsMessageIntoJpgCofficientsInJpegObject():message,image

8..EmbedMessageBySelectingNewSymbolValues():Symbol,Frequencies,Message

9.AllowsFrequenciesToBePassedForEachSymbol():Symbols,MessageBits

6.ModelCofficientHistogramsAndComputeFrequenci

esRepresentingCoeffValues():Symbol Frequencies

7.CallsStegEncoder():symbol,Frequencies,Message

10.ReturnsEncodedMessage():void

11.ReturnsStegoImageWithEncodedMessage():void

12.ReadStegoImageAndCallShowImage():void

13.DisplayStegoImageWithEmbeddedMessage():void

SEQUENCE DIAGRAM FOR DECODING


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SEQUENCE DIAGRAM FOR DECODING

Stegtest:Main

Function

Jpeg Decoder Get Symbol

Frequencies

Steg Decoder Arithmetic

Encoder

Window:User

Interface

1.DecodesTheMessage

AndVerifyThatItMatchesWithTheOriginal():void

2.DecodesMessageFrom

JpegCoeffStoredInJpeg

Image():void

3.ModelCoeffHistogramsAnd

ComputeFrequenciesRepres

entingCoeffValues():symbol,

Frequencies

4.CallsStegDecoderToDecodeTheMessage():void

7.ReturnsTheDecodedMessage():void

5.DecodeTheMessageTha

tIsEmbeddedByStegEnco

der():Symbol,Frequencies

6.AllowsFrequenciesToB

ePassedForEachSymbol

():Symbols,Frequencies

8.ReturnsTheDecoded

MessageEmbeddedInto

StegoImage():void

9.VerifyThatMessageHasBeenDecodedSuccessfullyElsePrintErrorMessage():void


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ACTIVITY

DIAGRAMS

ACTIVITY DIAGRAM


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ACTIVITY DIAGRAMFOR ENCODING

Select Image

Create The Message

To Be Embedded

Find DCT Of

The Image

Model Coefficient Histogram

And Compute Frequencies

Encode The Message By

Selecting New Symbol Values

Pass Frequencies For

Each Symbol

Convert NX1 Array Of Bits Into NX1 Output

Array Of Symbols Ranging From 1...S

Embed The Message Into The Cover Image

Using The Selected Coefficients


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ACTIVITY DIAGRAMFOR DECODING Read And Calculate The DCT

Of The Received Image

Model Coefficient Histograms

And Compute Frequencies

Decode The Message By Evaluating The

Modified Frequecies

Pass Frequencies For

Each Symbol

Convert NX1 Output Array Of Symbols

Ranging From 1...S Into NX1 Araay Of Bits

Decode The Message Using

Modified Bits Of Coefficients


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SCREENSHOTS


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REFERENCES Anderson, R.J., Petitcolas, F.A.P.: On the Limits of

Steganography. IEEE Journal of Selected Areas inCommunications: Special Issue on Copyright and PrivacyProtection), 16(4) (1998) 474-481

Buccigrossi, R.W., Simoncelli, E.P.: Progressive WaveletImage Coding Based on a Conditional Probability ModelProceedings ICASSP-97, Munich Germany (1997)

Cachin, C.: An Information-Theoretic Model forSteganography Proceedings of 2nd Workshop on InformationHiding, LNCS, Springer (1998)

Cover, T., Thomas, J.: Elements of Information Theory.Wiley, New York, (1991)


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Any

Questions


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steganography final ppt

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