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Prof. Dr. Anderson [email protected]
http://www.ic.unicamp.br/~rocha
Reasoning for Complex Data (RECOD) Lab.Institute of Computing, Unicamp
Av. Albert Einstein, 1251 - Cidade Universitria
CEP 13083-970 Campinas/SP - Brasil
http://www.ic.unicamp.br/~rochahttp://www.ic.unicamp.br/~rochamailto:[email protected]:[email protected] -
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Organizao
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A. Rocha, 2012 Anlise Forense de Documentos Digitais
Avisos
! Aulas
Slides em Ingls Apresentados previamente no IEEE CVPR
Workshop on Vision of the Unseen(WVU),2008, Anchorage, Alaska
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Organizao
! Mascaramento de Informaes (InformationHiding)
! Esteganografia & Esteganlise (Steganography &
Steganalysis)
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Steganography and Steganalysis:past, present, and future
Institute of ComputingUniversity of Campinas (Unicamp)
CEP 13084-851, Campinas, SP - Brazil
Anderson [email protected]
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Summary
!Steganography LSB insertion/modification
FFTs and DCTs
! How to improve security
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Summary
! Steganalysis
Aural
Structural
Statistical
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Summary
! Freely available tools and software
! Open research topics
!Conclusions and remarks
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Steganography
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Hiding scenario
+ =
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Steganography
! Computer Vision and Image Processingtechniques
! Mostly based on replacing a noisecomponent
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Steganography
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Steganography
! What are the problemsof noise embedding?
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A. Rocha, 2012 Anlise Forense de Documentos Digitais
Steganography
! What are the problemsof noise embedding?
Compression
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Steganography
! What are the problemsof noise embedding?
Compression
Filtering
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Steganography
! What are the problemsof noise embedding?
Compression
Filtering
Conversions
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Steganography
! What are the problemsof noise embedding?
Compression
Filtering
Conversions
! MSB-basedtechniques
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LSB insertion/modificationSteganography techniques
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LSB insertion/modificationSteganography techniques
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FFTs and DCTs based
1. Least significant coefficients
JStegand Outguess2. Block tweaking
3. Coefficient selection
4. Wavelets
Steganography techniques
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FFTs and DCTs based
DCT and FFT general algorithm
Steganography techniques
1. Splitting. Split up the image into 8x8 blocks.
2. Transformation. Transform each block via a DCT/FFT.
3. Compression stage 1. Use a quantizer to round the coefficients.
4. Compression stage 2. Use a Huffman encoding scheme orsimilar to further compress the streamlined coefficients.
5. Decompressing. Use inverse DCT/FFT to decompress.
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FFTs and DCTs
! JSteg
Sequentially replaces LSB of DCT/FFTcoefficients
Does not use shared key
What is its main problem?
Steganography techniques
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FFTs and DCTsSteganography techniques
Require: messageM, cover image I; 1: JSteg(M,I) 2: whileM!= NULL do 3: get next DCT coefficient from I 4: ifDCT != 0 and DCT != 1 then
5: b= next bit fromM 6: replace DCT LSB with message bit b 7: M = M - b 8: end if 9: Insert DCT into stego image S10: end while11: returnS12: end procedure
JSteg general algorithm
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FFTs and DCTs
! Outguess
Improvement over JSteg
PRNG
Statistical profiling
Steganography techniques
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Require: messageM, cover image I, shared key k; 1: Outguess(M,I, k) 2: Initialize PRNG with the shared key k 3: whileM!= NULL do 4: get pseudo-random DCT coefficient from I
5: ifDCT != 0 and DCT != 1 then 6: b= next bit fromM 7: replace DCT LSB with message bit b 8: M = M - b 9: end if10: Insert DCT into stego image S
11: end while12: returnS13: end procedure
FFTs and DCTsSteganography techniques
Outguess general algorithm
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FFTs and DCTs
2. Block tweaking
DCT/FFTs quantizerstage
Keeps down distortions
Vulnerable to noise
Low-capacityembedding
Steganography techniques
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FFTs and DCTs
!Coefficient selection Selects k largestDCT/FFT coefficients
Use a function f that considers the required
strengthof the embedding process
Steganography techniques
is the bit you want to embed in the coefficient i
required strength
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FFTs and DCTs
! Wavelets
DCT/FFT transformations are not effective athigher-compression levels
Possibility to embed in the high-frequency
Embedding in the quantizationstage
Steganography techniques
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How to improve security
! Kerckhoffs Principle
! Destruction of the original
! Statistical profiling
Steganography techniques
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How to improve security
! Structural profiling
! Split the information
! Compaction
Steganography techniques
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Steganalysis
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Steganalysis
! Detection of hidden messages
! Early approaches focused on detection
! Next step: recovery
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Steganalysis
! Steganalysis attacks
1. Aural
2. Structural
3. Statistical
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Analysis! An L-bit color channel represent 2Lpossible
values
! Split in 2L-1pairs differing in the LSBs only
! All possible patterns of neighboringbits for theLSBs
Statistical Steganalysis
A. Westfeld and A. Pfitzmann.Attacks on Steganographic Systems.IHW 1999. 29
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! What if we use all available LSBs?
! Expected frequencyvsobserved one
! Expected frequencyis not available
!In the original the EF is the arithmetical mean ineach PoV
Analysis
Statistical Steganalysis
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! The embedding affects only the LSBs
! Arithmetical mean remains the sameineach PoV
! to detect hidden messages
Analysis
Statistical Steganalysis
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! Probability of hiding
Analysis
Statistical Steganalysis
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!Only detects sequential messages
! The thresholdvalue for detection may be quitedistinct for different images
! Low-order statistics
Analysis
Statistical Steganalysis
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RS Analysis (RS)
!Analysis of the LSB loss-less embedding capacity
! The LSB plane is correlated with other bitplanes
! Simulates artificial new embeddings
Statistical Steganalysis
J. Fridrich, M. Goljan, and R. Du. Detecting LSB Steganography in Color
and Grayscale Images. IEEE Multimedia, vol. 8, n. 4, pp. 22-28, 2001. 34
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!Let Ibe the image with WxHpixels
! Pixel values in P = {1...255}
! Divide Iin Gdisjoint groups of nadjacent pixels
(e.g., n = 4)
RS Analysis (RS)Statistical Steganalysis
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! Define a discriminant function to classify
the Ggroups
RS Analysis (RS)Statistical Steganalysis
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! Flippinginvertible function
! Shiftinginvertible function
! Identityfunction
RS Analysis (RS)Statistical Steganalysis
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RS Analysis (RS)
! Define a mask M = {-1,0,1}
! The mask defines which function to apply
! The masks compliment is -M
Statistical Steganalysis
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! Apply the functions over the groups for Mand -
Mmasks. Classify them as Regular.
Singular.
Unusable.
RS Analysis (RS)Statistical Steganalysis
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! It holds that
! Statistical hypothesis
RS Analysis (RS)Statistical Steganalysis
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Gradient Energy Flipping Rate (GEFR)
! Gradient of an unidimensional signal
! The I(n)s GEis
Statistical Steganalysis
L. Zhi, S. Fen, and Y. Xian.An LSB Steganography detection algorithm. Intl.Symposium on Personal, Indoor, Mobile Radio Communication, 2003 41
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! After hiding a signalS(n) in the originalsignal, I(n) becomes I(n) and the gradient
becomes
Gradient Energy Flipping Rate (GEFR)Statistical Steganalysis
r(n) = I(n) I(n 1)
= (I
(n
) +S
(n
))
(I
(n
1) +S
(n
1))= r(n) + S(n) S(n 1)
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! After any kind of embeddingGE becomes
Gradient Energy Flipping Rate (GEFR)Statistical Steganalysis
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Gradient Energy Flipping Rate (GEFR)
! To perform the detection, define a function tosimulate new embeddings
Statistical Steganalysis
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1. Find the test images
2.Apply F over the test image and calculate
3. Find
4. GE(0) is based on5. Find the messages estimated size
Gradient Energy Flipping Rate (GEFR)Statistical Steganalysis
GEFR general algorithm
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High-order Statistical analysis
! Natural images have regularities
! They can be detected with high-order statistics
! Use QMF decompositionfor multi-scale
analysis
Statistical Steganalysis
S. Lyu and H. Farid. Detecting Hidden Messages Using Higher-orderStatistics and Support Vector Machines. IHW 2002. 46
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High-order Statistical analysisStatistical Steganalysis
QMF decomposition
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High-order Statistical analysis
! LetVi(x,y), Hi(x,y), and Di(x,y)be the vertical,horizontal, and diagonal sub-bands for a givenscale i = {1,...n}
! Statistical model composed by Mean,Variance,Skewness, and Kurtosis
! Basic coefficients distribution
Statistical Steganalysis
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High-order Statistical analysis
! Second set of statistics
Errors on an optimal linear predictor ofcoefficient magnitude
Spatial, orientation, and scale neighborhood
Statistical Steganalysis
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! For instance: errors for all neighbors in thevertical sub-band at scale i
! wk denotes scalar weighting values
High-order Statistical analysisStatistical Steganalysis
w4Vi(x, y+ 1) + w5Vi+1(x
2,y
2) + w6Di(x, y) + w7Di+1(
x
2,y
2)
Vi(x, y) =w1Vi(x 1, y) + w2Vi(x + 1, y) + w3Vi(x, y 1)+
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High-order Statistical analysis
! Quadratic minimizationof the errorfunction
! Vis a column vector of magnitudecoefficients
! Qis the magnitude neighbors coefficients
Statistical Steganalysis
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! Minimizationthrough differentiation wrt w
! Calculate wkusing the linear predictor log
error
High-order Statistical analysisStatistical Steganalysis
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High-order Statistical analysis
! 12(n-1) basic statistics
! 12(n-1) error statistics
! 24(n-1) feature vector
Statistical Steganalysis
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High-order Statistical analysis
! Supervised learning
! Training set of stego and clean images
! LDA and SVMs
Statistical Steganalysis
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Image Quality Metrics (IQMs)
! Often used for
Coding artifact evaluation
Performance prediction of vision algorithms
Quality loss due to sensor inadequacy
Statistical Steganalysis
I. Avcibas, N. Memon, B. Sankur. Steganalysis using image qualitymetrics. TIP vol. 12, n. 2, pp. 221-229, 2003. 55
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Image Quality Metrics (IQMs)
! IQMs
! Multivariate regression analysis(ANOVA)
! Exploits Steganographic schemes artifacts
Statistical Steganalysis
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Image Quality Metrics (IQMs)
! IQMs
1. Mean absolute error2. Czekznowski correlation
3. Image fidelity
4. HVS error5. etc
Statistical Steganalysis
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Image Quality Metrics (IQMs)Statistical Steganalysis
y1 = 1x11 + 2x12 + . . . + qx1q + 1y2 = 2x21 + 2x22 + . . . + qx2q + 2
.
..
yN = nxn1 + 2x12 + . . . + qxnq + n,
! Training set of stego and clean images
! ANOVA
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Progressive Randomization (PR)
! It captures the differences between imageclasses
! Statistical artifactsinserted during the hidingprocess
Statistical Steganalysis
A. Rocha and S. Goldenstein. Progressive Randomization forSteganalysis. IEEE MMSP, 2006. 59
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Progressive Randomization (PR)
! Fourstages
1. Randomization process
2. Feature regions selection
3. Statistical descriptors analysis
4. Invariance
Statistical Steganalysis
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Progressive Randomization (PR)
! The idea behind PR! Let X be a Bernoulli RV
! Transformation T(I,p)
Statistical Steganalysis
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Progressive Randomization (PR)Statistical Steganalysis
Require: Input image I; Percentage P = {Pi, ... ,Pn}; 1:Randomization:perform nLSB pixel disturbances on I
2:Region selection:select rfeature regions of each image
3:Statistical descriptors:calculate mdescriptors for each region
4: Invariance:normalize the descriptors based on I
i {Oi}i=0...n
{Oij} i = 0 . . . n,j = 1 . . . r.
= {O01, . . . , Onr}.
{dijk}= {dk(Oij)} i = 0 . . . n,j = 1 . . . r,
k = 1 . . . m.
F = {fe}e=1...nrm =
dijk
d0jk
i = 0 . . . n,
j = 1 . . . r,
k = 1 . . . m.
{Oi}i=0...n.={I, T(I, P1), . . . , T (I, Pn)}
Progressive Randomization algorithm
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Progressive Randomization (PR)
! Randomization stage
It simulates new embeddings
n = 6
P= {1%,5%,10%,25%,50%,75%} of the LSBs
Statistical Steganalysis
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Progressive Randomization (PR)
! Statistical descriptors stage
Ueli Maurer that measures randomness
Statistical Steganalysis
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Progressive Randomization (PR)Statistical Steganalysis
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Progressive Randomization (PR)
! Invariance stage
The variation rate is more interesting
Normalize all transformations result (T1...Tn)
wrt. T0
Statistical Steganalysis
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Progressive Randomization (PR)
! Classification stage
Training set of stego and clean images
Supervised learning
|M| = 25% (~13% changed LSBs) > 90%accuracy (SVMs)
Statistical Steganalysis
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Progressive Randomization (PR)Statistical Steganalysis
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Software and tools
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Software and tools
! EzStego
! Stego Online
! Mandelsteg
! Stealth
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Software and tools
! White Noise
! S-Tools
! Hide and Seek
! JSteg
! Outguess
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CamaleoSoftware and Tools
www.ic.unicamp.br/~rocha/sci/stego
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Interestingresearch topics
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Steganography
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Open research topics
! Images are subjected to many operations
Translation, rotation, shear
Blurring, filtering, lossy compression
Printing, rescanning, conversion
Steganography
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Steganography
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Open research topics
! Designing of robust IH techniques
Robustness to geometrical attacks
Embeddings in regions with richness of
details
Steganography
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Steganography
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Open research topics
! Good IQMs
! Public key systems
! Multiple embeddings with no interference
g g p y
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Steganalysis
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Open research topics
! Blind detection
! Very smallembedding detection
! Adaptive techniques
! Hidden content recovery
g y
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Conclusions
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Conclusions
! Steganographyand Steganalysisoverview
! IH embedding and detection techniques
! Open research topics
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Conclusions
! Data hiding has passed its period of hype
! Public fearcreated by mainstream press reports
! Laws against IH techniques dissemination
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Conclusions
! Nowadays...
Steganographyand Steganalysisare maturedisciplines
Applications
Research opportunities
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Conclusions
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Steg inreal world
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Obrigado!