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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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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