an lise forense de documentos digitaisrocha/teaching/2010s2/mo815/... · 2010-11-11 · a. rocha,...
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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 UniversitáriaCEP 13083-970 • Campinas/SP - Brasil
Análise Forense deDocumentos Digitais
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Organização
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Avisos
‣ Aulas
• Slides em Inglês
• Apresentados previamente no IEEE CVPR Workshop on Vision of the Unseen (WVU), 2008, Anchorage, Alaska
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Organização
‣ Mascaramento de Informações (Information Hiding)
‣ Esteganografia & Esteganálise (Steganography & Steganalysis)
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Summary
‣ Steganography
• LSB insertion/modification
• FFTs and DCTs
‣ How to improve security
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Summary
‣ Steganalysis
• Aural
• Structural
• Statistical
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Summary
‣ Freely available tools and software
‣ Open research topics
‣ Conclusions and remarks
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Steganography
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Hiding scenario
+ =
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Steganography
‣ Computer Vision and Image Processing techniques
‣ Mostly based on replacing a noise component
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Steganography
‣ What are the problems of noise embedding?
• Compression
• Filtering
• Conversions
‣ MSB-based techniques
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
LSB insertion/modificationSteganography techniques
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
LSB insertion/modificationSteganography techniques
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
FFTs and DCTs
‣ Outguess
• Improvement over JSteg
• PRNG
• Statistical profiling
Steganography techniques
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
How to improve security
‣ Kerckhoff’s Principle
‣ Destruction of the original
‣ Statistical profiling
Steganography techniques
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
How to improve security
‣ Structural profiling
‣ Split the information
‣ Compaction
Steganography techniques
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Steganalysis
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Steganalysis
‣ Detection of hidden messages
‣ Early approaches focused on detection
‣ Next step: recovery
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Steganalysis
‣ Steganalysis attacks
1. Aural
2. Structural
3. Statistical
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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. 29
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ 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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ The embedding affects only the LSBs
‣ Arithmetical mean remains the same in each PoV
‣ to detect hidden messages
AnalysisStatistical Steganalysis
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ Probability of hiding
AnalysisStatistical Steganalysis
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ 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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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. 34
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ 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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ Define a discriminant function to classify the G groups
RS Analysis (RS)Statistical Steganalysis
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ Flipping invertible function
‣ Shifting invertible function
‣ Identity function
RS Analysis (RS)Statistical Steganalysis
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ 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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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ It holds that
‣ Statistical hypothesis
RS Analysis (RS)Statistical Steganalysis
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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 41
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ 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)
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ After any kind of embedding GE’ becomes
Gradient Energy Flipping Rate (GEFR)Statistical Steganalysis
43
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Gradient Energy Flipping Rate (GEFR)
‣ To perform the detection, define a function to simulate new embeddings
Statistical Steganalysis
44
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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
45
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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. 46
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
High-order Statistical analysisStatistical Steganalysis
QMF decomposition
47
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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
48
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
High-order Statistical analysis
‣ Second set of statistics
• Errors on an optimal linear predictor of coefficient magnitude
• Spatial, orientation, and scale neighborhood
Statistical Steganalysis
49
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ 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)+
50
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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
51
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
‣ Minimization through differentiation wrt w
‣ Calculate wk using the linear predictor log error
High-order Statistical analysisStatistical Steganalysis
52
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
High-order Statistical analysis
‣ 12(n-1) basic statistics
‣ 12(n-1) error statistics
‣ 24(n-1) feature vector
Statistical Steganalysis
53
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
High-order Statistical analysis
‣ Supervised learning
‣ Training set of stego and clean images
‣ LDA and SVMs
Statistical Steganalysis
54
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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. 55
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Image Quality Metrics (IQMs)
‣ IQMs
‣ Multivariate regression analysis (ANOVA)
‣ Exploits Steganographic schemes artifacts
Statistical Steganalysis
56
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Image Quality Metrics (IQMs)
‣ IQMs
1. Mean absolute error
2. Czekznowski correlation
3. Image fidelity
4. HVS error
5. etc
Statistical Steganalysis
57
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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
58
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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. 59
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Progressive Randomization (PR)
‣ Four stages
1. Randomization process
2. Feature regions selection
3. Statistical descriptors analysis
4. Invariance
Statistical Steganalysis
60
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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
61
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
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
62
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Progressive Randomization (PR)
‣ Randomization stage
• It simulates new embeddings
• n = 6
• P = {1%,5%,10%,25%,50%,75%} of the LSBs
Statistical Steganalysis
63
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Progressive Randomization (PR)
‣ Statistical descriptors stage
•
• Ueli Maurer that measures randomness
Statistical Steganalysis
64
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Progressive Randomization (PR)Statistical Steganalysis
65
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Progressive Randomization (PR)
‣ Invariance stage
• The variation rate is more interesting
• Normalize all transformation’s result (T1...Tn) wrt. T0
Statistical Steganalysis
66
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Progressive Randomization (PR)
‣ Classification stage
• Training set of stego and clean images
• Supervised learning
• |M| = 25% (~13% changed LSBs) > 90% accuracy (SVMs)
Statistical Steganalysis
67
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Progressive Randomization (PR)Statistical Steganalysis
68
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Software and tools
69
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Software and tools
‣ EzStego
‣ Stego Online
‣ Mandelsteg
‣ Stealth
70
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Software and tools
‣ White Noise
‣ S-Tools
‣ Hide and Seek
‣ JSteg
‣ Outguess
71
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
CamaleãoSoftware and Tools
www.ic.unicamp.br/~rocha/sci/stego
72
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Interesting research topics
73
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Open research topics
‣ Images are subjected to many operations
• Translation, rotation, shear
• Blurring, filtering, lossy compression
• Printing, rescanning, conversion
Steganography
74
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Open research topics
‣ Designing of robust IH techniques
• Robustness to geometrical attacks
• Embeddings in regions with richness of details
Steganography
75
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Open research topics
‣ Good IQMs
‣ Public key systems
‣ Multiple embeddings with no interference
Steganography
76
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Open research topics
‣ Blind detection
‣ Very small embedding detection
‣ Adaptive techniques
‣ Hidden content recovery
Steganalysis
77
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Conclusions
78
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Conclusions
‣ Steganography and Steganalysis overview
‣ IH embedding and detection techniques
‣ Open research topics
79
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Conclusions
‣ Data hiding has passed its period of hype
‣ Public fear created by mainstream press reports
‣ Laws against IH techniques dissemination
80
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A. Rocha, 2010 – Análise Forense de Documentos Digitais
Conclusions
‣ Nowadays...
• Steganography and Steganalysis are mature disciplines
• Applications
• Research opportunities
81
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A. Rocha, 2010 – Análise Forense de Documentos Digitais 82
Conclusions
...
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A. Rocha, 2010 – Análise Forense de Documentos Digitais 83
Steg in real world
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Obrigado!