enee631 digital image processing (spring'09) data hiding in images spring ’09 instructor: min...

ENEE631 Digital Image Processing (Spring'09)

Data Hiding in ImagesData Hiding in Images

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 21 (4/20/2009)Lecture 21 (4/20/2009)

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ENEE631 Digital Image Processing (Spring'09) Lec 21 – Data Hiding [2]

Overview and LogisticsOverview and Logistics

Last Time:

– Overview of content-based image retrieval– A quick guide on video communications– Introduction on data embedding in images

Today:

– Continue on data hiding in images

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Recall: Embedding Basics – Two Simple TriesRecall: Embedding Basics – Two Simple Tries

Data Hiding: To put secondary data in host signal

(1) Replace LSB

(2) Round a pixel value to closest even or odd numbers

Both equivalent to reduce effective pixel depth for representing host image

Detection scheme is same as LSB, but embedding brings less distortion in the quantized case, esp. for higher LSB bitplane

+ Simple embedding; Fragile to even minor changes

even “0”odd “1”

pixel value 98 99 100 101

odd-even mapping

lookup table mapping

0 1 0 1

… 0 1 1 0 …

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How to Improve the Robustness?How to Improve the Robustness?

Introduce quantization to embedding process

– Make features being odd/even multiple of Q

Tradeoff between embedding distortion and robustness

Larger Q => Higher resilience to minor changes => Higher average changes required to embed

data

Questions: What’s the expected embedding distortion? Relation with distortion by quantization alone?

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

odd-even mapping


0 1 0 1

… 0 1 1 0 …


Distortion from Quantization-based EmbeddingDistortion from Quantization-based Embedding

Uniform quantization with step size Q

(Assume source’s distribution within each interval is approx. constant)

MSE = Q2 / 12

Odd-even embedding with quantization

MSE = ½ * (Q2/12) + ½ * (7Q2/12) = Q2 / 3

MSE equiv. to quantize with 2Q step size! “Predistort” via quantization to gain resilience [-Q/2, Q/2]


odd-even mapping 0 1 0 1

-Q -Q/2 + Q/2 +Q

-Q/2 + Q/2

1/Q


Two Views of Quantization-based EmbeddingTwo Views of Quantization-based Embedding

From decoder’s view

– Partition the signal space into two subsets labeled “0” & “1”– Decode according to which subset a sample belongs to

Embedder picks watermarked sample from the subset labeled with to-be-embedded bit, and tries to minimize the amount of changes

From embedder’s view

– Design two quantizers “#0”, “#1”: step size 2Q, offset by Q– Embedder perform quantization using the quantizer labeled

with to-be-embedded bit => “Quantization Index Modulation (QIM)” Decoder looks for closest representative


“1”“0”


Tampering Detection by Pixel-domain Fragile WmkTampering Detection by Pixel-domain Fragile Wmk

Downloaded from ICIP’97 CD-ROM paper by Yeung-Mintzer

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Fight Against Forging Tamper-Detection Watermark?Fight Against Forging Tamper-Detection Watermark?

If using LSB to embed a fragile watermark for tampering detection, adversary can alter image but retain LSB

[Solution 1] Add uncertainty to the embedding mapping

– through a random look-up table with controlled run length

[Solution 2] Make watermark securely depend on host content

E.g. embed a robust/content-base hash of host image


odd-even mapping


0 1 0 1

… 0 1 1 0 …


Pixel-domain Table-lookup EmbeddingPixel-domain Table-lookup Embedding (Yeung-Mintzer ICIP’97)

– Simple to implement; be able to localize alteration extracted wmk from altered image


Yeung’s Fragile Watermark for Tampering DetectionYeung’s Fragile Watermark for Tampering Detection

Basic idea:

– enforce certain relationship to embed data– minimize distortion: nearest neighbour, constrained runs– diffuse error incurred to surrounding pixels

v’=v+d1+d2: LUT(v’)=boriginal image

marked image

lookup table generator

LUT( )

seed

data to be embedded

table lookuptest

image

extracted data

LUT( )

visualize &decide

embed detect

d1: diffused error


LUT Embedding: Distortion/Security/RobustnessLUT Embedding: Distortion/Security/Robustness

What’s new compared with odd-even embedding?– Mapping from feature to embedded bit is less predictable– Adjacent intervals may be mapped to the same bit value

How much security gained with proprietary LUT? =>

– Proprietary LUT brings uncertainty and makes it difficult for attackers to embed specific data at his/her will

How much MSE introduced by embedding? =>

– Larger than odd-even embedding How much resilience gained? =>

– Moving away by Q/2 step may not trigger detection error Due to possible continuous run in LUT

Ref: M. Wu: "Joint Security and Robustness Enhancement for Quantization Based Embedding," IEEE Trans. on Circuits and Systems for Video Technology, vol. 13, no. 8, pp.831-841, August 2003. (see ICIP’03 for shorter conf. version)


Case Study: Mintzer-Yeung Patent on Fragile WmkCase Study: Mintzer-Yeung Patent on Fragile Wmk

F. Mintzer and M.M. Yeung: “Invisible Image Watermark for Image Verification,” U.S. Patent 5,875,249, issued Feb. 1999.

Acquire knowledge on latest art in industry from patent– Especially useful when industry don’t publish all key

techniques (but they often aggressively patent these “IP”) Watermark is a good example: Digimarc, Verance, IBM, NEC …

– Complementary to literature search of journal/conf. papers

For details on how to patent your novel ideas– Talk to your supervisor & lawyers, and check univ./company policies

– Resource Online workshop on Patent 101 (see also the patent handout)

http://www.invent.org/workshop/3_0_0_workshop.asp

US Patent Officewww.uspto.gov (full-text patent search and patent doc. images)

ENEE698T – Technology Laws (first offered F’08; see future offering)


A Glimpse at the Patent SystemA Glimpse at the Patent System

Intended to add "the fuel of interest to the fire of genius" (Abraham Lincoln)

– In exchange for disclosing an invention to the public, the inventor receives the exclusive right to control exploitation of the invention and to realize any profits for a specific length of time

Common types of U.S. patent: http://www.uspto.gov/go/taf/patdesc.htm

– Utility patents of most interest to ECEer A term up to 20 years from the date the patent application was filed Granted to anyone who invents a new and useful process, machine,

manufacture, or composition of matter, or a new and useful improvement thereof

– Design patents new, original & ornamental design for 14 years’ protection

– Plant patents


Patent ProcessPatent Process

Idea– Make sure your idea is new and practical/useful

Document– Keep records that document your discovery– File “Invention Disclosure” & Be careful with public disclosure

Research– Search existing literature & patents related to your invention– Analyze existing patents and literature

Apply– Prepare and file the patent application documents– Review by PTO examiner; amend your patent claims if needed


Structure of A Utility PatentStructure of A Utility Patent

Title Page– Title, Patent Number, File & Issue Dates– Inventors, Assignees, Patent examiners– Related patents and references– Abstract, # of claims, # of drawings, representative drawing

Drawings

Main text– Field of the Invention (usually in one sentence)– Background– Summary– Brief description of drawings– Detailed description of the preferred embodiment– Claims


Useful Contents for Technical StudiesUseful Contents for Technical Studies

Detailed descriptions of invention (process/method/apparatus)

– Along with drawings (and background/summary)– They are often intended to be written in an easily accessible

way

Technical discussions on “Preferred embodiment(s)” in Mintzer-Yeung’s patent

– Image stamping via LUT embedding– Image verification via Table lookup and visualization– Error diffusion to alleviate visual distortion incurred by

embedding– Apply the process to DC-image for embedding data in JPEG

image


Claims: Crucial Part for Business ValuesClaims: Crucial Part for Business Values

Not always “fun” to read– Many legally speaking terms and wordings

Usually prefer broad (and allowable) claims

Claims are the hot spot examined by USPTO– Determines whether

(1) the proposed claims have been claimed by other patents? (2) anticipated by other already issued patents? (3) straightforward combination or extensions of existing methods for similar purposes by those “skilled in the art”

28 Claims in Mintzer-Yeung patent– “Root” claims and “child” claims

Claim Tree: useful tree visualization to illustrate relations between claims


From Fragile to Robust WatermarkFrom Fragile to Robust Watermark

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From Fragile/Semi-Fragile to Robust WatermarkFrom Fragile/Semi-Fragile to Robust Watermark

Applications of fragile/semi-fragile watermark

– Tampering detection– Secret communications => “Steganography” (covert writing)– Convey side info. in a seamless way: lyric, director’s notes

Situations demanding higher robustness

– Protect ownership (copyright label), prevent leak (digital fingerprint)

– Desired robustness against compression, filtering, etc.

How to make it robust?

– Use “quantization” from signal processing– Use error correcting coding – Borrow theories from signal detection & telecommunications

“Spread Spectrum Watermark”: use “noise” as watermark and add it to the host signal for improved invisibility and robustness

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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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Watermarking Example by Cox et al.Watermarking Example by Cox et al.

Original Cox Difference between

whole image DCT marked & original Embed in 1000 largest coeff.

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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 equivalent 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 original as ref; use “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” coefficients => 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; …


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

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

And the related references cited by these publications.UM

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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


Watermark Attacks: What and Why?Watermark Attacks: What and Why?

Attacks: intentionally obliterate watermarks

– remove a robust watermark– make watermark undetectable (e.g., miss synchronization)

– uncertainty in detection (e.g., multiple ownership claims)

– forge a valid (fragile) watermark– bypass watermark detector

Why study attacks?

– identify weaknesses– propose improvement– understand pros and

limitation of tech. solution

To win each campaign, To win each campaign, a generala generalshould know both his should know both his troop and troop and the opponent’s as well the opponent’s as well as possible.as possible.

-- -- Sun Tzu, Sun Tzu, The Art of War, The Art of War, 500 B.C.500 B.C.

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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




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.






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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”


odd-even 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

Choose alpha to maximize a distortion-compensation SNR

odd/even mapping 0 1 0UM

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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 ACC for Detecting 16-bit ACC 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




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.






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