1 exposing digital forgeries in color array interpolated images presented by: ariel hutterer final...
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Exposing Digital Forgeries in Color Array Interpolated
Images
Presented by:Ariel Hutterer
Final Fantasy ,2001
My eye
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References Alin C.Popescu and Hany Farid:
Exposing Digital Forgeries in Color Filter Array Interpolated Images.
Yizhen Huang: Can Digital Forgery Detection Unevadable?
A Case Study : Color Filter Array Interpolation Statistical Feature Recovery.
Hagit El Or Demosaicing.
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Outline Introduction Digital Cameras Interpolations Detecting CFA Interpolation Results Crack Methods Computer Graphics
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Introduction- forgeries Low cost: cameras ,photo editing software. Images can be manipulated easily. Splicing.
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Introduction- forgeries Images have a huge impact in public
opinion. Legal world. Scientific evidence.
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Introduction - preventing forgeries approaches Two principal approaches to prevent forgeries:
Digital watermarking: Means that image can be authenticated. Drawbacks:
Specially equipped digital cameras ,that insert the watermark. Assume that watermark cannot be easily removed and
reinserted. (but ….it is???) Statistic analysis:
Most color digital cameras , introduces specific correlation: A third of the image are captured by a sensor. Two thirds of the image are interpolated.
Images manipulated must alter this specific statistic.
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Outline Introduction Digital Cameras Interpolations Detecting CFA Interpolation Results Crack Methods Computer Graphics
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Digital Cameras Most Color digital Cameras have a single
monochrome Array of sensors
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Digital Cameras How does color form with monochrome
sensor for each pixel?
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Digital Cameras-Bayer Color Array Half pixels are Green ,quarter are Red and
quarter are Blue
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Digital Cameras-Bayer Color Array Several possible
arranges
BayerDiagonal
Bayer Diagonal Striped
Psudo-randomBayer
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Digital cameras - forming color
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Digital cameras - forming color
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Digital cameras - forming color
Interpolation
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Digital cameras - forming color Bayer Array For almost
all Digital Cameras
Color Interpolation different for each make of Digital Camera
Interpolation
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Outline Introduction Digital Cameras Interpolations Detecting CFA Interpolation Results Crack Methods Computer Graphics
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Interpolations Naive – per channel interpolation
Nearest neighbor ,Bilinear interpolation Inter-channel dependencies and
correlations – Reconstruct G channel, then reconstruct R & B
based on G. Reconstruct all 3 channels constrained with inter-channel dependence.
Adaptive reconstruction – Measure local image variations (e.g. edges,
gradients, business) and reconstruct accordingly.
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Interpolations - Aliasing
R R R R
R R R R
R R R R
G G G
G G G G
G G G
G G G G
G G G
G G G G
B B B
B B B
B B B
R R R R R R R
R R R R R R R
R R R R R R R
R R R R R R R
R R R R R R R
R R R R R R R
G G G G G G G
G G G G G G G
G G G G G G G
G G G G G G G
G G G G G G G
G G G G G G G
B B B B B B B
B B B B B B B
B B B B B B B
B B B B B B B
B B B B B B B
B B B B B B B
Interpolate
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Interpolations - Aliasing
R R R R
R R R R
R R R R
G G G
G G G G
G G G
G G G G
G G G
G G G G
B B B
B B B
B B B
R R R R R R R
R R R R R R R
R R R R R R R
R R R R R R R
R R R R R R R
R R R R R R R
G G G G G G G
G G G G G G G
G G G G G G G
G G G G G G G
G G G G G G G
G G G G G G G
B B B B B B B
B B B B B B B
B B B B B B B
B B B B B B B
B B B B B B B
B B B B B B B
Interpolate
Result
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Interpolations - Samples
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Interpolation-Bilinear Bicubic Red and Blue Kernels:
Separable 1-D filters
Rw
Rw = ½(Rnw+Rsw)
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Interpolation-Bilinear Bicubic Green kernels
2-D filters:
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Interpolation- Gradient Based First, calculate Green channel:
Calculate derivates estimators
Determination of Green’s values
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Interpolation – Evaluation Tools
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Interpolation -Results
Original Linear Kimmel
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Outline Introduction Digital Cameras Interpolations Detecting CFA Interpolation Results Cracks Methods Computers Graphics
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Detecting CFA Interpolation In Each pixel only one color derives from
the sensor ,two others derive from interpolation from their neighbors .
The correlation are periodic. Tampering will destroy these correlations. Splicing together two images from
different cameras will create inconsistent correlations across the composite image.
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Detecting CFA Interpolation Two different tools:
EM algorithm : Produce Map of Probabilities and interpolation
coefficients Used to detect kind of interpolation
Farid’s Indicator: Produce Map of Similarities Used to quantify the similarity to CFA Interpolated
Image
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EM Algorithm (Expectation/Maximization): Two possible models:
M1:the sample is linearly correlated to its neighbors
M2:the sample is not correlated to its neighbors
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EM Algorithm (Expectation/Maximization):
f(x,y) – color channel alpha - parameters ,where(0,0) = 0. denotes
the specific correlation. n - independent and identically samples
drawn from a Gaussian distribution, with 0 mean and unknown variance
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EM Algorithm (Expectation/Maximization): Two-step iterative algorithm:
E-step : calculate the probability of each sample M-step: the specific form of the correlation is
estimated. Based in Bayes rule:
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Farid’s indicator The similarity between the probability and a
synthetic map is obtained by:
Where:
Similarity measure is phase insensitive
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Farid’s indicator How to use it:
CFA-Interpolated : if at least one channel is greater than threshold1
Non CFA Interpolated: if all 3 channels are smaller than threshold2
Ind(cfa-sf)
CFA InterpolatedNon CFA Interpolated Unknown
threshold2 threshold1
result
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Huang indicator Motivation: Farid’s Indicator is proportional
to image size. Table of Green Channel Indicator
Huang Indicator:
Indicator function
32x32 128x128 256x256 512x512
Farid 140 2303 9419 52361
Huang 2.70 2.70 2.84 4.31
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Outline Introduction Digital Cameras Interpolations Detecting CFA Interpolation Results Cracks Methods Computers Graphics
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Results Detecting different interpolation methods Detecting tampering Measuring Sensitivity and robustness
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Detecting different interpolation methods Hundreds of images from 2 digital
cameras Blur 3x3 Down sampled Cropped Resample in CFA Interpolations
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Detecting different interpolation methods
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Detecting different interpolation methods
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Detecting different interpolation methods
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Detecting different interpolation methods
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Detecting different interpolation methods Coefficients are 8 to each color so we are
a 24-D vector ,LDA classifier ,results: 97% Interpolations kinds was detected
2D projection of LDA
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Detecting tampering Hiding the damage of the car
Air-brushing ,smudging ,blurring and duplication
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Detecting tampering Result:
Left F(p) : for tampered portion Right F(p) : for unadulterated portion
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Measuring Sensitivity and robustness
remember
False 0% Median 5x5
97
Bilinear 100% Gradient based
100%
Bicubic 100% Adaptive color plane
97%
Median 3x3
99 Variable number of gradients
100%
Testing different interpolations with Farid’s indicator
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Measuring Sensitivity and robustness Testing influence of jpeg
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Measuring Sensitivity and robustness Testing influence of Gaussian Noise
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Outline Introduction Digital Cameras Interpolations Detecting CFA Interpolation Results Crack Methods Computer Graphics
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Cracking What’s a “true digital image” General Model
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True digital image It was taken by a CCD/CMOS digital
camera, or other device with similar function and remains intact after shooting except for embedding ownership and other routinely added information.
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General Model where:
W all images S all possible images tacked by an ideal
camera c. N are S enlarged because of noise.
Detection method: Pm(I), a projection of Image I I is true when: I is Artificially CFA-interpolated
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General Model The result image should be as close as
possible to the original The mean of the difference to an ideally
CFA interpolated image should be controlled in a specific range.
Such difference should be distributed averagely.
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General Model Im: Tampered Image Im’: cracked Image Int(I) : Ideal Interpolated
Dif(Im,Im’) Dif(Im’,Int(Im’))
K2
K1
Dif(Im,Im’,Int(Im’))
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General Model We are looking for We want to minimize the 3d distance:
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Outline Introduction Digital Cameras Interpolations Detecting CFA Interpolation Results Crack Methods Computer Graphics
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Computer Graphic A naïve approach:
Computer Graphic will be detected like non CFA-Interpolated.
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Computer Graphics Huge improvement of dedicated hardware
in the last 7 years SGI:Onyx2 ,Infinity reality 3(2000) :
12 bits * 4 channels No shaders End User license ,250,000$
Pc d/core, geforce 8(2006): 32 bits * 4 channels Shaders w/24 parallels pipes 1,500-5,000$
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Computer Graphics 2001,Final fantasy ,first Film made with
PC.
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Computer Graphics See cg not like an Image, see it like
REALITY.
Render Reality high resolution ,by 32bits for each color
Optical distortions, ghost and blurring
Sensor CFA sampling and noise
Interpolation
Image
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Computer Graphics From Image Forgeries to Science Fiction
Image forgeries are a “positive issue“ for development of: Simulators. Trainers. Robots………
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Computer Graphics From Image Forgeries to Science Fiction
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Conclusion Detection CFA-Interpolated methods are
not enough robust. Compression like jpeg destroy the
interpolation correlation. Interpolation can be artificially made.
The End