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

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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