steganography and steganalysis: past, present, and futurerocha/wvu/talks/stegpastpresent... ·...
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganography and Steganalysis: past, present, and future
Institute of ComputingUniversity of Campinas (Unicamp)
CEP 13084-851, Campinas, SP - Brazil
Siome [email protected]
Anderson [email protected]
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Summary
‣ Steganography
• LSB insertion/modification
• FFTs and DCTs
‣ How to improve security
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Summary
‣ Steganalysis
• Aural
• Structural
• Statistical
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Summary
‣ Freely available tools and software
‣ Open research topics
‣ Conclusions and remarks
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganography
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Hiding scenario
+ =
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganography
‣ Computer Vision and Image Processing techniques
‣ Mostly based on replacing a noise component
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganography
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganography
‣ What are the problems of noise embedding?
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganography
‣ What are the problems of noise embedding?
• Compression
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganography
‣ What are the problems of noise embedding?
• Compression
• Filtering
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganography
‣ What are the problems of noise embedding?
• Compression
• Filtering
• Conversions
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganography
‣ What are the problems of noise embedding?
• Compression
• Filtering
• Conversions
‣ MSB-based techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
LSB insertion/modificationSteganography techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
LSB insertion/modificationSteganography techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
FFTs and DCTs based
1. Least significant coefficients
• JSteg and Outguess
2. Block tweaking
3. Coefficient selection
4. Wavelets
Steganography techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
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 or similar to further compress the streamlined coefficients.
5. Decompressing. Use inverse DCT/FFT to decompress.
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
FFTs and DCTs
‣ JSteg
• Sequentially replaces LSB of DCT/FFT coefficients
• Does not use shared key
• What is its main problem?
Steganography techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
FFTs and DCTsSteganography techniques
Require: message M, cover image I; 1: JSteg(M,I) 2: while M != NULL do 3: get next DCT coefficient from I 4: if DCT != 0 and DCT != 1 then 5: b = next bit from M 6: replace DCT LSB with message bit b 7: M = M - b 8: end if 9: Insert DCT into stego image S10: end while11: return S12: end procedure
JSteg general algorithm
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
FFTs and DCTs
‣ Outguess
• Improvement over JSteg
• PRNG
• Statistical profiling
Steganography techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Require: message M, cover image I, shared key k; 1: Outguess(M,I, k) 2: Initialize PRNG with the shared key k 3: while M != NULL do 4: get pseudo-random DCT coefficient from I 5: if DCT != 0 and DCT != 1 then 6: b = next bit from M 7: replace DCT LSB with message bit b 8: M = M - b 9: end if10: Insert DCT into stego image S11: end while12: return S13: end procedure
FFTs and DCTsSteganography techniques
Outguess general algorithm
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
FFTs and DCTs
2. Block tweaking
• DCT/FFT’s quantizer stage
• Keeps down distortions
• Vulnerable to noise
• Low-capacity embedding
Steganography techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
FFTs and DCTs
‣ Coefficient selection
• Selects k largest DCT/FFT coefficients
• Use a function f that considers the required strength of the embedding process
Steganography techniques
is the bit you want to embed in the coefficient !i
required strength
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
FFTs and DCTs
‣ Wavelets
• DCT/FFT transformations are not effective at higher-compression levels
• Possibility to embed in the high-frequency
• Embedding in the quantization stage
Steganography techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
How to improve security
‣ Kerckhoff’s Principle
‣ Destruction of the original
‣ Statistical profiling
Steganography techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
How to improve security
‣ Structural profiling
‣ Split the information
‣ Compaction
Steganography techniques
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganalysis
‣ Detection of hidden messages
‣ Early approaches focused on detection
‣ Next step: recovery
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Steganalysis
‣ Steganalysis attacks
1. Aural
2. Structural
3. Statistical
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Analysis
‣ An L-bit color channel represent 2L possible values
‣ Split in 2L-1 pairs differing in the LSBs only
‣ All possible patterns of neighboring bits for the LSBs
Statistical Steganalysis
A. Westfeld and A. Pfitzmann. Attacks on Steganographic Systems. IHW 1999. 25
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ What if we use all available LSBs?
‣ Expected frequency vs observed one
‣ Expected frequency is not available
‣ In the original the EF is the arithmetical mean in each PoV
AnalysisStatistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ The embedding affects only the LSBs
‣ Arithmetical mean remains the same in each PoV
‣ to detect hidden messages
AnalysisStatistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ Probability of hiding
AnalysisStatistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ Only detects sequential messages
‣ The threshold value for detection may be quite distinct for different images
‣ Low-order statistics
AnalysisStatistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
RS Analysis (RS)
‣ Analysis of the LSB loss-less embedding capacity
‣ The LSB plane is correlated with other bit planes
‣ 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. 30
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ Let I be the image with WxH pixels
‣ Pixel values in P = {1...255}
‣ Divide I in G disjoint groups of n adjacent pixels (e.g., n = 4)
RS Analysis (RS)Statistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ Define a discriminant function to classify the G groups
RS Analysis (RS)Statistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ Flipping invertible function
‣ Shifting invertible function
‣ Identity function
RS Analysis (RS)Statistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
RS Analysis (RS)
‣ Define a mask M = {-1,0,1}
‣ The mask defines which function to apply
‣ The mask’s compliment is -M
Statistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ Apply the functions over the groups for M and -M masks. Classify them as
• Regular.
• Singular.
• Unusable.
RS Analysis (RS)Statistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ It holds that
‣ Statistical hypothesis
RS Analysis (RS)Statistical Steganalysis
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Gradient Energy Flipping Rate (GEFR)
‣ Gradient of an unidimensional signal
‣ The I(n)’s GE is
Statistical Steganalysis
L. Zhi, S. Fen, and Y. Xian. An LSB Steganography detection algorithm. Intl. Symposium on Personal, Indoor, Mobile Radio Communication, 2003 37
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ After hiding a signal S(n) in the original signal, 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)
38
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ After any kind of embedding GE’ becomes
Gradient Energy Flipping Rate (GEFR)Statistical Steganalysis
39
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Gradient Energy Flipping Rate (GEFR)
‣ To perform the detection, define a function to simulate new embeddings
Statistical Steganalysis
40
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
1. Find the test image’s
2. Apply F over the test image and calculate
3. Find
4. GE(0) is based on
5. Find the message’s estimated size
Gradient Energy Flipping Rate (GEFR)Statistical Steganalysis
GEFR general algorithm
41
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
High-order Statistical analysis
‣ Natural images have regularities
‣ They can be detected with high-order statistics
‣ Use QMF decomposition for multi-scale analysis
Statistical Steganalysis
S. Lyu and H. Farid. Detecting Hidden Messages Using Higher-order Statistics and Support Vector Machines. IHW 2002. 42
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
High-order Statistical analysisStatistical Steganalysis
QMF decomposition
43
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
High-order Statistical analysis
‣ Let Vi(x,y), Hi(x,y), and Di(x,y) be the vertical, horizontal, and diagonal sub-bands for a given scale i = {1,...n}
‣ Statistical model composed by Mean, Variance, Skewness, and Kurtosis
‣ Basic coefficients distribution
Statistical Steganalysis
44
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
High-order Statistical analysis
‣ Second set of statistics
• Errors on an optimal linear predictor of coefficient magnitude
• Spatial, orientation, and scale neighborhood
Statistical Steganalysis
45
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ For instance: errors for all neighbors in the vertical 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)+
46
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
High-order Statistical analysis
‣ Quadratic minimization of the error function
‣ V is a column vector of magnitude coefficients
‣ Q is the magnitude neighbors’ coefficients
Statistical Steganalysis
47
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
‣ Minimization through differentiation wrt w
‣ Calculate wk using the linear predictor log error
High-order Statistical analysisStatistical Steganalysis
48
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
High-order Statistical analysis
‣ 12(n-1) basic statistics
‣ 12(n-1) error statistics
‣ 24(n-1) feature vector
Statistical Steganalysis
49
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
High-order Statistical analysis
‣ Supervised learning
‣ Training set of stego and clean images
‣ LDA and SVMs
Statistical Steganalysis
50
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
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 quality metrics. TIP vol. 12, n. 2, pp. 221-229, 2003. 51
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Image Quality Metrics (IQMs)
‣ IQMs
‣ Multivariate regression analysis (ANOVA)
‣ Exploits Steganographic schemes artifacts
Statistical Steganalysis
52
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Image Quality Metrics (IQMs)
‣ IQMs
1. Mean absolute error
2. Czekznowski correlation
3. Image fidelity
4. HVS error
5. etc
Statistical Steganalysis
53
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
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
54
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)
‣ It captures the differences between image classes
‣ Statistical artifacts inserted during the hiding process
Statistical Steganalysis
A. Rocha and S. Goldenstein. Progressive Randomization for Steganalysis. IEEE MMSP, 2006. 55
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)
‣ Four stages
1. Randomization process
2. Feature regions selection
3. Statistical descriptors analysis
4. Invariance
Statistical Steganalysis
56
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)
‣ The idea behind PR
‣ Let X be a Bernoulli RV
‣ Transformation T(I,p)
Statistical Steganalysis
L(pxi) = pixel’s LSBbi = bit to be hiddenS = Random set of pixelsp = percentage of S
57
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)Statistical Steganalysis
Require: Input image I; Percentage P = {Pi, ... ,Pn}; 1: Randomization: perform n LSB pixel disturbances on I
2: Region selection: select r feature regions of each image
3: Statistical descriptors: calculate m descriptors 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...n!r!m =
!
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
58
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)
‣ Randomization stage
• It simulates new embeddings
• n = 6
• P = {1%,5%,10%,25%,50%,75%} of the LSBs
Statistical Steganalysis
59
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)
‣ Statistical descriptors stage
•
• Ueli Maurer that measures randomness
Statistical Steganalysis
60
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)Statistical Steganalysis
61
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)
‣ Invariance stage
• The variation rate is more interesting
• Normalize all transformation’s result (T1...Tn) wrt. T0
Statistical Steganalysis
62
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)
‣ Classification stage
• Training set of stego and clean images
• Supervised learning
• |M| = 25% (~13% changed LSBs) > 90% accuracy (SVMs)
Statistical Steganalysis
63
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Progressive Randomization (PR)Statistical Steganalysis
64
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Software and tools
65
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Software and tools
‣ EzStego
‣ Stego Online
‣ Mandelsteg
‣ Stealth
66
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Software and tools
‣ White Noise
‣ S-Tools
‣ Hide and Seek
‣ JSteg
‣ Outguess
67
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
CamaleãoSoftware and Tools
www.ic.unicamp.br/~rocha/sci/stego
68
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Interesting research topics
69
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Open research topics
‣ Images are subjected to many operations
• Translation, rotation, shear
• Blurring, filtering, lossy compression
• Printing, rescanning, conversion
Steganography
70
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Open research topics
‣ Designing of robust IH techniques
• Robustness to geometrical attacks
• Embeddings in regions with richness of details
Steganography
71
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Open research topics
‣ Good IQMs
‣ Public key systems
‣ Multiple embeddings with no interference
Steganography
72
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Open research topics
‣ Blind detection
‣ Very small embedding detection
‣ Adaptive techniques
‣ Hidden content recovery
Steganalysis
73
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Conclusions
74
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Conclusions
‣ Steganography and Steganalysis overview
‣ IH embedding and detection techniques
‣ Open research topics
75
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Conclusions
‣ Data hiding has passed its period of hype
‣ Public fear created by mainstream press reports
‣ Laws against IH techniques dissemination
76
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Conclusions
‣ Nowadays...
• Steganography and Steganalysis are mature disciplines
• Applications
• Research opportunities
77
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future 78
Conclusions
...
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future 79
Steg in real world
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WVU, Anchorage - 2008 .:.Rocha & Goldenstein, Steganography and Steganalysis: past, present, and future
Questions?
The thinker by Rodin
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