enee631 digital image processing (spring'09) image forensics spring ’09 instructor: min wu...

ENEE631 Digital Image Processing (Spring'09)

Image ForensicsImage Forensics

Spring ’09 Instructor: Min Wu

Electrical and Computer Engineering Department,

University of Maryland, College Park

bb.eng.umd.edu (select ENEE631 S’09) [email protected]

ENEE631 Spring’09ENEE631 Spring’09Lecture 22 (4/22/2009)Lecture 22 (4/22/2009)

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ENEE631 Digital Image Processing (Spring'09) Lec 22 – Image Forensics [2]

Overview and LogisticsOverview and Logistics

Last Time:

– Fragile data embedding for image forgery detection– Improving watermark robustness via quantization– Robust watermark via spread spectrum embedding

Today:

– Continue on spread spectrum watermark– Digital forensic fingerprinting for traitor tracing– Non-intrusive image forensics

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Background: Robust Watermark / Data-EmbeddingBackground: Robust Watermark / Data-Embedding

Embedding domain tailored to media characteristics & application requirement

10011010 …

© Copyright …


Spread Spectrum Watermark: Spread Spectrum Watermark: Cox et al (NECI)Cox et al (NECI)

What to use as watermark? Where to put it?– Place wmk in perceptually significant spectrum (for robustness)

Modify by a small amount below Just-noticeable-difference (JND)

– Use long random noise-like vector as watermark for robustness/security against jamming+removal & imperceptibility

Embedding v’i = vi + vi wi = vi (1+ wi)

– Perform DCT on entire image and embed wmk in DCT coeff.– Choose N=1000 largest AC coeff. and scale {vi} by a random factor

2D DCT sort v’=v (1+ w) IDCT & normalize

Original image

N largest coeff.

other coeff.

marked image

random vector generator

wmk

seed

1.0nceunit varia zeromean, iid,~iw

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Cox et al’s Scheme (cont’d): DetectionCox et al’s Scheme (cont’d): Detection Subtract original image from the test one before feeding to

detector (“non-blind detection”)

Correlation-based detection a correlator normalized by |Y| in Cox et al. paper

DCT

compute similarity

thresholdtest image

decision

wmk

DCT select N largest

original unmarked image

select N largest

preprocess

–

YY

WYWYsim

,

,),(

k watermar

watermarkno

:1

:0

NWYH

NYHXXY

–orig X

test X’

X’=X+W+N ?

X’=X+N ?

To think

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Performance of Cox et al’s SchemePerformance of Cox et al’s Scheme

Robustness

– (claimed) scaling, JPEG, dithering, cropping, “printing-xeroxing-scanning”, multiple watermarking

– No big surprise with high robustness equiv. to sending just 1-bit {0,1} with O(103) samples

Comment– Must store orig. unmarked image “private wmk”/“non-blind” detection– Perform image registration if necessary– Adjustable parameters: N and

Distortion none scale25%

JPG10%

JPG 5% dither crop25%

print-xerox-scan

similarity 32.0 13.4 22.8 13.9 10.5 14.6 7.0 threshold = 6.0 (determined by setting false alarm probability)

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Comments on Cox et al’s SchemesComments on Cox et al’s Schemes

“1000 largest coeff.” before and after embedding– May not be identical (and order may also changes)– Solutions: use orig. as ref; “embeddable” mask to maintain synch.

Detection without using original/host image– Treat host image as part of the noise/interference ~ Blind detection

need long wmk signal to combat severe host interference [Zeng-Liu]

– Can do better than blind detection, as embedder knows the host signal => “Embedding with Side Info.”

./(:blind-non

;/:blind

22

22

www)y

wwy

TdN

dT

N

T

T

H0: <(x + noise), w >H1: < y + noise, w > = < w + (x + noise), w >

vs.H0: < (x + noise) - x, w > = < noise, w > H1: < y + noise - x, w > = < w + noise, w >


Improve Invisibility and Robustness on Cox schemeImprove Invisibility and Robustness on Cox scheme

Apply better Human Perceptual Model– Global scaling factor is not suitable for all coefficients

– More explicitly compute just-noticeable-difference (JND) JND ~ max amount each coefficient can be modified invisibly Employ human visual model: freq. sensitivity, masking, …

– Use more localized transform => fine tune wmk for each region

block-based DCT; wavelet transform

Improve robustness: detection performance depends on ||s|| / d Add a watermark as strong as JND allows Embed in as many “embeddable” coeff. => improve robustness

Block-DCT schemes: Podichuk-Zeng; Swanson-Zhu-Tewfik ’97

– Leverage existing visual model for block DCT from JPEG

iiii wJNDvv '

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Perceptual Comparison: Cox vs. PodilchukPerceptual Comparison: Cox vs. Podilchuk

Original Cox Podilchukwhole image DCT block-DCTEmbed in 1000 largest coeff. Embed to all “embeddables”

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Compare Cox & Podilchuk Schemes (cont’d)Compare Cox & Podilchuk Schemes (cont’d)

Cox Podilchuk

Amplified pixel-wise difference between marked and original (gray~0)

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Comments on Cox & Podilchuk’s SS WmkComments on Cox & Podilchuk’s SS Wmk

Robustness

– Very robust against additive noise (seen from detection theory)

– Sensitive to synchronization errors, esp. under blind detection jitters (line dropping/addition) geometric distortion (rotation, scale, translation)

Question: How to improving synchronization resilience?

=> add registration pattern; embed in RST-invariant domain; …


Localized Embedding: Double-Edge SwordLocalized Embedding: Double-Edge Sword

“Innocent Tools” exploited by attackers: block concealment

Recovery of lost blocks

– for resilient multimedia transmission of JPEG/MPEG– good quality by edge-directed interpolation: Jung et al; Zeng-Liu

Remove robust watermark by block replacement

edge estimation

edge-directed interpolation

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Block Replacement AttackBlock Replacement Attack


Attack effective on block-DCT based spread-spectrum watermark

marked original (no distortion)JPEG 10% after proposed attack

JPEG 10% w/o distort Interp.

w/ orig 34.96 138.51 6.30

w/o orig 12.40 19.32 4.52

512x512 lenna Threshold: 3 ~ 6

Recall: claimed high robustness&quality by fine tuning wmk strength for each region


Summary: Spread Spectrum EmbeddingSummary: Spread Spectrum Embedding

Main ideas– Place wmk in perceptually significant spectrum (for robustness)

Modify by a small amount below Just-noticeable-difference (JND)

– Use long random vector of low power as watermark to avoid artifacts (for imperceptibility, robustness, and security)

Cox’s approach

– Perform DCT on entire image & embed wmk in large DCT AC coeff.– Embedding: v’i = vi + vi wi = vi (1+ wi)

– Detection: subtract original and perform correlation w/ wmk

Podilchuk’s improvement– Embed in many “embeddable” coeff. in block-DCT domain– Adjust watermark strength by explicitly computing JND


Summary: Type-I Additive EmbeddingSummary: Type-I Additive Embedding

Add secondary signal in host media

Representative: spread spectrum embedding

– Add a noise-like signal and detection via correlation– Good tradeoff between security, imperceptibility & robustness– Limited capacity: host signal often appears as major interferer

modulationmodulation

data to be hidden

Xoriginal source

X’ = X + marked copy

10110100 ...10110100 ...

< X’ + noise, > = < + (X + noise), >

< X’ + noise - X, > = < + noise, >

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Type-II Relationship Enforcement EmbeddingType-II Relationship Enforcement Embedding

Deterministically enforcing relationship – Secondary information carried solely in watermarked signal– Typical relationship: parity/modulo in quantized features

Representative: odd-even (quantized) embedding– Alternative view: switching between two quantizers w/ step size 2Q

“Quantization Index Modulation”

– Robustness achieved by quantization or tolerance zone– High capacity but limited robustness

even “0”odd “1”

feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Q

odd-even mapping

lookup table mapping

0 1 0 1

… 0 1 1 0 …

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Robustness vs. PayloadRobustness vs. Payload Blind/non-coherent detection ~ original copy unavailable Robustness and payload tradeoff Advanced embedding: quantization w/ distortion-compensation

– Combining the two types with techniques suggested by info. theory

RobustnessRobustness

PayloadPayload

ImperceptibilityImperceptibility

stronger noisenoise weaker

-15 -10 -5 0 5 10 15 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10log10

(E2/2) (dB)

Capacity C

(bits/c

h.

use)

Capacity of Type-I (host=10E) and Type-II AWGN ch. (wmk MSE E2)

Type-I (C-i C-o, blind detection)Type-II (D-i D-o)

-4 -3 -2 -1 0 1

0

0.02

0.04

0.06

0.08

0.1

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Distortion Compensated Quantization EmbeddingDistortion Compensated Quantization Embedding

Distortion compensation technique– Increase quantization step by a factor for higher robustness– Compensate the extra distortion by dragging the enforced feature

toward the original feature value

Overall embedding distortion unchanged

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d’0

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odd/even mapping 0 1 0

X0

Choose alpha to maximize a distortion-compensation SNR


Improved Robustness by Distortion CompensationImproved Robustness by Distortion Compensation

– ICS (ideal Costa’s scheme)– SS (spread spectrum additive

embedding)– binary DM (odd-even quantized

embedding)– binary SCS (odd-even quantized

embedding with distortion compensation)


Issues to AddressIssues to Address

Tradeoff among conflicting requirements– Imperceptibility– Robustness & security– Capacity

Key elements of data hiding– Perceptual model– Embedding one bit– Multiple bits– Uneven embedding capacity– Robustness and security– What data to embed

Up

per

L

ayer

s

Uneven capacity equalization

Error correction

Security

……

Low

er

Lay

ers

Imperceptible embeddingof one bit

Multiple-bit embedding

Coding of embedded data

Robustness

Capacity

Imperceptibility

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Embedded Fingerprint for Tracing TraitorsEmbedded Fingerprint for Tracing Traitors

Insert special signals to identify recipients

– Deter leak of proprietary documents– Complementary protection to encryption– Consider imperceptibility, robustness, traceability

– Attacks mounted by single and multiple users

Collusion Attack Collusion Attack (to remove fingerprints)(to remove fingerprints)

AliceAlice

BobBob

Colluded CopyColluded Copy

Unauthorized Unauthorized rere--distributiondistribution

Fingerprinted docfor different users

Collusion Attack Collusion Attack (to remove fingerprints)(to remove fingerprints)

AliceAlice

BobBob

Colluded CopyColluded Copy

Unauthorized Unauthorized rere--distributiondistribution

Fingerprinted docfor different users

Extract Extract FingerprintsFingerprints

Suspicious Suspicious CopyCopy

101110 …101110 …

Codebook

Alice, Bob, …

Identify Identify TraitorsTraitors

Extract Extract FingerprintsFingerprints

Suspicious Suspicious CopyCopy

101110 …101110 …

Codebook

Alice, Bob, …

Identify Identify TraitorsTraitors

Multi-user Attacks

Traitor Tracing

President

Satellite Image

Alice

Bob

Carl

w1

w2

w3

LeakLeak


Collusion Attacks by Multiple UsersCollusion Attacks by Multiple Users Collusion: A cost-effective attack against multimedia fingerprints

– Users with same content but different fingerprints come together to produce a new copy with diminished or attenuated fingerprints

– Fairness: Each colluder contributes equal share through averaging, interleaving, and nonlinear combining

Collusion byaveraging 1/3

Alice ChrisBob

Colluded copy

Originally fingerprinte

dcopies

Alice Chris

Collage


Key IssuesKey Issues

How to construct fingerprints?

– Identify individual users– Resist multi-user collusion

How to embed fingerprints in media data?

– Tailor to media characteristics for robustness & imperceptibility

Interaction between choices of fingerprint construction, embedding, and detection

– esp. to combat collusion attacks– Analogous to “cross-layer” methods in communications


Road Map on Media Fingerprinting ResearchRoad Map on Media Fingerprinting Research

Robust EmbeddingOrthogonal Fingerprints

Represent how many users?Resist how many colluders?

“most effective” collusions?

Amount of resources used?

Group-based FP to exploit Attacker Behavior

Coded FP

Joint Coding-Embedding Framework

overcome prior work’s problems of long code length, low resilience, and limited scalability

adapt to media characteristics

Combinatorial codes + CDM

Error correcting codes + TDM

Correlated Fingerprints


Example: Orthogonal Fingerprint for Curves/GraphicsExample: Orthogonal Fingerprint for Curves/Graphics Use (approx.) orthogonal sequences as FPs for different users

– Detection by looking for high correlation result

Embed in parametric modeling domain of curve

– Perturb B-spline parameters according to spread spectrum sequences

Detection Statistics

Typical threshold is 3~6 for false alarm of 10-3 ~ 10-9

Original Curve(captured by TabletPC)

Fingerprinted Curve(100 control points)


Fingerprinting Topographic MapFingerprinting Topographic Map

– Traditional protection: intentionally alter geospatial content

– Embed much less intrusive digital fingerprint for a modern protection

• 9 long curves are marked; 1331 control points used to carry the fingerprint

1100x1100 Original Map Fingerprinted Map


Collusion-Resistant Fingerprinting of MapsCollusion-Resistant Fingerprinting of Maps

2-User Interleaving Attack5-User Averaging Attack

. . .

Can survive combined attacks of collusion + print + scan

Can extend to 3-D Digital Elevation Map


16-bit Codes for Detecting 16-bit Codes for Detecting 3 Colluders Out of 20 3 Colluders Out of 20

User-1 ( -1,-1, -1, -1, 1, 1, 1, 1, …, 1 ) ( -1, 1, 1, 1, 1, 1, …, -1, 1, 1, 1 ) User-4

Extracted fingerprint code ( -1, 0, 0, 0, 1, …, 0, 0, 0, 1, 1, 1 )

Collude by AveragingUniquely Identify User 1 & 4

Embed fingerprint via HVS-based spread spectrum embedding in block-DCT domain


Many Forensic Questions …Many Forensic Questions …

arise from military, intelligence, law enforcement, and commercial applications

What type of sensor was used?

Which camera brand took this picture? What model?

What processing has been done?

– Has it been tampered? manipulated?

What technologies were employed?

– Given two images, are they acquired by devices with similar imaging technologies?


Break down the info. processing chain into individual components

Identify algorithms and parameters employed in major components of a digital device or processing system

Exploit Intrinsic Fingerprints via Component ForensicsExploit Intrinsic Fingerprints via Component Forensics

Ref: Swaminathan/Wu/Liu in ICASSP’06 and IEEE Trans. Info Forensics & Security (’07)

Color Filter Array (CFA)

ColorInterpolation

White Balancing

Real world scene Digital imageCamera Components

… Sensors …


Forensic Estimation and IdentificationForensic Estimation and Identification

Establish a processing model and estimate parameters– Small # possibilities => exhaustive search or by classifier design – More continuous valued parameters => analyze based on estimation

theory

Example: color interpolation in digital camera– Approximate by texture classification and linear filter

(one set of interpolation coeff. for smooth, horizontal & vertical)

– Find best linear estimate of filter coeff. in each class(least-square type of method for robustness)

– Find CFA pattern in a search space that minimizes fitting errors

CFA Interpolation

R ?

? ?

R ?

? ?

R ?

? ?

R ?

? ?

? ?? ?

CandidateCFA pattern

A x = b Interp. equation set

A ~ non-interp pixels;b ~ interpolated pixels

Interp. coeff. x and fitting error for each region type and color


Experiments with Images from Digital CamerasExperiments with Images from Digital Cameras

Camera Model Camera Model

123456789

10

Canon Powershot A75Canon Powershot S400Canon Powershot S410Canon Powershot S1 IS Canon Powershot G6Canon EOS Digital REBELNikon E4300 Nikon E5400Sony Cybershot DSC P7Sony Cybershot DSC P72

111213141516171819

Olympus C3100Z/C3020ZOlympus C765UZMinolta DiMage S304Minolta DiMage F100Casio QV-UX2000FujiFilm Finepix S3000FujiFilm Finepix A500Kodak CX6330Epson PhotoPC 650

19 cameras and 200 image blocks per camera model

– 512 x 512 regions with maximum gradients chosen for analysis (s.t. have substantial revealing evidence on color interpolation)


Detecting Which Camera Detecting Which Camera BrandBrand Took the Image Took the Image

Canon Nikon Sony Olympus Minolta Casio Fuji Kodak Epson

Canon 96% * * * * * * * *

Nikon * 83% 5% * * * * * *

Sony * * 90% * * * * * *

Olympus * * * 93% * * * * *

Minolta 8% * * * 81% * * * *

Casio * * * 6% * 89% * * *

Fuji * * * * 7% * 87% * *

Kodak * * * * * * * 89% *

Epson * * * * * * * * 100%

Interpolation coefficients as features for classifier Average accuracy: 90% for 9 camera brands on uncontrolled scenes Best related work under controlled, uncompressed setting on input scenes

84% for 3 brands, uncompressed [Kharrazi et al’ 05]; 96% for 3 brands [Bayram et al’ 06]

(* denotes values below 4%)


Tampering DetectionTampering Detection

Explore intrinsic fingerprints left by various processing modules

– To infer the algorithms and parameters employed in various components of the digital device and processing systems

– New traces or vanished old traces suggests potential post-camera operations

Estimated coeff.

From direct camera output

After post-camera filtering

ColorInterpolation

ColorSensors

Scene Optical Lens System CAMERA

Other SoftwareProcessing

ATampering

/ Stego

B

Black: Sony P72; White: Canon Powershot S410

Grey: Classified as other cameras with low confidence


Overview of Scanner ModelOverview of Scanner Model

Tri-linear CCD sensors

Tri-linear CCD sensorsHardcopy

graphic data

Lamp & MirrorsLamp & Mirrors

Lens

Tri-linearcolor filter array

Scanner head

Motion systemMotion system

Shift RegisterAmplifier

A/D Converter

Shift RegisterAmplifier

A/D Converter

Post-processing

Interpolation, Color transformation White balancing, Exposure controlNoise reduction,…

Digitalimage

Software operation

Scanning noise

StatisticalStatisticalnoise featuresnoise features

Scanner modelIdentification


E.g.: Noise Features from Wavelet AnalysisE.g.: Noise Features from Wavelet Analysis

ScannedImage I

One stagewavelet

decomposition

HHHLLH

Subband STD &goodness of

Gaussian fitting

Statisticalfeatures

f (3)(I), f (4)(I)

Digital photograph Scanner model 1 Scanner model 2

Histogram Mean and STD Gaussian distribution Goodness of Gaussian fitting

Fitting errorFitting error

HH,HL,LH sub-bandsRGB components2x3x3 = 18 features


Acquisition Forensics w/ Noise + Interp. FeaturesAcquisition Forensics w/ Noise + Interp. Features

Q1) What type of device was used to capture the image?

94% accuracy in identifying device type

Q2) What brand/model of the device captured the image?

Cellphone cameras: 98% accuracy over 5 brands

Standalone cameras: 90% over 19 camera models from 9 camera brands

Scanners: 93% accuracy over 9 scanner brands

Cell Phone Camera

Standalone Camera

Scanner

Computer Generated

Input Image

Brand/Model

IdentificationAcquisition Device

Type Identification

Further Forensic Analysis

Sony

Samsung

Nokia

AudiovoxMotorola

CanonFujiFilm

CasioMinolta

Epson

Microtek

AcerScan

Canon

Step 1Step 2


Suggested ReadingsSuggested Readings

1. I. Cox, J. Kilian, T. Leighton, T. Shamoon: “Secure Spread Spectrum Watermarking for Multimedia'', IEEE Trans. on Image Proc., vol.6, no.12, pp.1673-1687, 1997.

2. M. M. Yeung, F. Mintzer: “An Invisible Watermarking Technique for Image Verification", Proc. of the IEEE Int’l Conf. on Image Processing (ICIP), Oct. 1997.

3. M. Wu and B. Liu: "Data Hiding in Image and Video: Part-I -- Fundamental Issues and Solutions", IEEE Trans. on Image Proc., vol.12, no.6, pp.685-695, June 2003.

4. M. Wu, W. Trappe, Z.J. Wang, and K.J.R. Liu: “Collusion-resistant fingerprinting for Multimedia,” IEEE Signal Proc Magazine, March 2004.

5. Special Issue on Digital Forensics, IEEE Signal Processing Magazine, March 2009.=> Several articles on (non-intrusive) image and video forensics

6. M. Wu and B. Liu: Multimedia Data Hiding, Springer-Verlag, 2003.

7. I. Cox, M. Miller, and J. Bloom: Digital Watermarking, Morgan Kauffman, 2002.

And the related references cited by these publications.

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Data Hiding in Binary ImageData Hiding in Binary Image

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Binary Image: A Simple yet Important ClassBinary Image: A Simple yet Important Class

– Scanned documents, drawings, signatures

Social Security E-Files From Princeton EE201 lab material

E-PAD (InterLink Electronics)


Copyright Protection for E-PublishingCopyright Protection for E-Publishing

Change horizontal and vertical spacing to embed data

– Eyes can not easily identify such changes– “Make it difficult and not worthwhile rather than impossible”

for cheap, high-volume content ~ newspaper, magazine, E-books possible to remove watermark, but why not just pay a bulk

– Embedding may be through additive or enforcement methods

from http://www.acm.org/~hlb/publications/dig_wtr/dig_watr.html

• N.F. Maxemchuk, S. Low: “Marking Text Documents”, ICIP, 1997.


Authentic Signatures?Authentic Signatures?

Digitized signatures become popular in everyday life– At least a good interim solution to carry a long tradition

to digital world

Forgery and mis-use of signatures

Clinton electronically signed Electronic Signatures Act - Yahoo News 6/30/00 http://

www.whitehouse.gov/media/gif/bil.gif as of 7/00

E-PAD (InterLink Electronics)


Challenges on Hiding Data in Binary ImagesChallenges on Hiding Data in Binary Images

Only two levels are available

– Black-white flipping– Minor tuning on the color is not available

Little room for “invisible changes”

– What places can be changed and what cannot

Uneven distribution of changeable pixels

Related to authentication

– Extract hidden data without the use of original copy


Identify Flippable PixelsIdentify Flippable Pixels

Flippability score

– Take the human perception into account Based on smoothness and connectivity

– 0~1, with 0 indicating the pixels that should not be flipped

flip-score 0.625 0.375 0.25 0.125 0.1 0.05 0.01

# of pixels 250 32 3 86 382 662 71

(a)

(b)


Uneven Distribution of Flippable PixelsUneven Distribution of Flippable Pixels Most on rugged boundary

Multi-bit embedding via spatial division– Partition the image into non-overlapping blocks

Embedding rate (per block)

– variable: need side info.– constant: require larger blocks

Two advanced mechanisms to equalize the distribution– Random shuffling– Recent generalized approach: Wet paper codesU

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alk

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image size: 288x48 red block size: 16x16


Shuffling and Block-based EmbeddingShuffling and Block-based Embedding

Shuffling to equalize distribution of flippables (54 blocks)

Divide the image into blocks and hide one bit in each block – Manipulating pixels with the highest flippability scores in the block – Odd-even embedding

To embed a “0”: even number of black pixels To embed a “1”: odd number of black pixels


Shuffling-based Embedding and ExtractionShuffling-based Embedding and Extraction

Data embedding

Data Extraction

Marked Marked binary imagebinary image

ShufflingShufflingBlock-Block-based based

embeddingembedding

Inverse Inverse shufflingshuffling

Original Original binary imagebinary image

Data to be Data to be embeddedembedded

KeyKey

ComputeComputeflippabilityflippability

ShufflingShuffling

ShufflingShufflingBlock-Block-based based

extractionextraction

Test Test binary imagebinary image KeyKey

Extracted dataExtracted data

Enhance security

Simple


0 5 10 15 20 25 30 35 400

0.05

0.1

0.15

0.2

0.25

# of flippable pixels per block (signature img)

port

ion o

f blo

cks (

x 1

00%

)

before shuffsimulation meansimulation stdanalytic meananalytic stdbefore shuffle

std after shuffle

mean after shuffle

Compare Analysis with Simulation for ShufflingCompare Analysis with Simulation for Shuffling

Simulation: 1000 indep. random shuff.

q = 16 x 16

S = 288 x 48

N = S/q = 18 x 3

p = 5.45%

before shuffle

mean after shuffle std after shuffle

analysis simulation analysis simulation

m0/N (0th bin) 20.37% 5.16x10-5 % 0 % 9.78x10-5 0

m1/N (1st bin) 1.85% 7.77x10-4 % 0 % 3.79x10-4 0

m2/N (2nd bin) 5.56% 5.81x10-3 % 5.56x10-3 % 0.0010 0.0010


Application: “Signature in Signature”Application: “Signature in Signature”

– Annotating digitized signature with content info. of the signed document

Each block is 320-pixel large, 1bit / blk.


Application: Annotating Binary Line DrawingsApplication: Annotating Binary Line Drawings

10 characters (~ 70bits) are embedded

originaloriginal marked w/ marked w/ “01/01/2000”“01/01/2000”

pixel-wise pixel-wise differencedifference


Fragile Watermark for Tamper Detection of DocumentFragile Watermark for Tamper Detection of Document

Embed pre-determined pattern or content features beforehand Verify hidden data’s integrity to decide on authenticity

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alter(a)

(b)

(g)

after alteration

(e)

(c)

(d)

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Documents

entire image

image dctmarked

research talks

robustnessumcp enee631

paperumcp enee631 slides

quantizationrobust watermark

cox et als schemes1000

original cox difference