enee631 digital image processing (spring'04) introduction to data hiding in image & video...

ENEE631 Digital Image Processing (Spring'04)

Introduction to Data Hiding in Image & VideoIntroduction to Data Hiding in Image & Video

Spring ’04 Instructor: Min Wu

ECE Department, Univ. of Maryland, College Park

www.ajconline.umd.edu (select ENEE631 S’04)

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Based on ENEE631 Based on ENEE631 Spring’04Spring’04Section 16Section 16

ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [3]

Embedding Basics: Two Simple TriesEmbedding 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

+ 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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Example of Replacing LSBs (1)Example of Replacing LSBs (1)

Downloaded from http://www.cl.cam.ac.uk/~fapp2/steganography/image_downgrading/

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Replace LSB with Pentagon’s MSBUM

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Replace 6 LSBs with Pentagon’s 6 MSBsUM

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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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From Fragile to Robust WatermarkingFrom Fragile to Robust Watermarking Applications of fragile watermark

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

Situations demanding higher robustness

– Protect ownership (copyright label), prevent leak (digital fingerprint)– Desire robustness against compression, filtering, etc.

How to make it robust?

– Use “quantization” from signal processing => Type-II

– Borrow theories from telecommunications => Type-I “Spread Spectrum Watermark”: use “noise” as watermark and

add it to the host signal for improved invisibility and robustness

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Robust Wmk Application for Tracing TraitorsRobust Wmk Application for Tracing Traitors Leak of information as well as alteration and repackaging

poses serious threats to government operations and commercial markets

– e.g., pirated content or

classified document

Promising countermeasure:robustly embed digital fingerprints

– Insert ID or “fingerprint” (often through conventional watermarking) to identify each user

– Purpose: deter information leakage; digital rights management(DRM)– Challenge: imperceptibility, robustness, tracing capability

studio

The Lord ofthe Ring

Alice

Bob

Carl

w1

w2

w3

SellSell

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Type-II Relationship Enforcement EmbeddingType-II Relationship Enforcement Embedding

Deterministically enforcing relationship – Secondary info. 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

e.g. to hide data in binary image, enforcing # of black pixels per block to odd/even => additional issues in handling uneven embedding capacity

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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Type-I Additive EmbeddingType-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 imperceptibility and 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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Spread Spectrum Approach: Cox et al (NECI)Spread Spectrum Approach: Cox et al (NECI)

Key points– Place wmk in perceptually significant spectrum (for robustness)

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

– Use long random vector as watermark to avoid artifacts (for imperceptibility & robustness)

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 conveying just 1-bit {0,1} with O(103) samples

Comment

– Must store original unmarked image “private wmk”, “non-blind” detect.

– 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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Robustness vs. CapacityRobustness vs. Capacity

Blind/non-coherent detection ~ original copy unavailable Robustness and capacity tradeoff Advanced embedding: quantization w/ distortion-compensation

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

RobustnessRobustness

CapacityCapacity

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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Issues and ChallengesIssues and Challenges

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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More Detailed DiscussionsMore Detailed Discussions =>=>

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SS Wmk Detection Based on Hypothesis TestingSS Wmk Detection Based on Hypothesis Testing

Optimal detection for On-Off Keying (OOK)

– OOK under i.i.d. Gaussian noise {di} b{0,1} represents absence vs. presence of ownership mark Use a correlator-type detector (recall the review last week)

– Need to determine how to choose {si}

Neyman-Pearson Detection [Poor’s book Sec.2.4]

– False-alarm ~ claiming wmk existence when nothing embedded– Given max. allowed false-alarm, try to minimize prob. of miss

detection Use likelihood ratio as detection statistic Determine threshold according to false-alarm prob.

n~1ifor )1 (if :

)0 (if :

1

0

bdsyH

bdyH

iii

ii

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Invisible Robust Wmk: Improved SchemesInvisible Robust Wmk: Improved Schemes

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 freq. coeff. can be modified

imperceptibly Use i for each coeff. finely tune wmk strength

– Better tradeoff between imperceptibility and robustness Try to add a watermark as strong as possible

Block-DCT based schemes:– Podichuk-Zeng and Swanson-Zhu-Tewfik– Existing visual model for block DCT: JPEG

)1(' iiii wvv

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Compare Cox & Podilchuk SchemesCompare Cox & Podilchuk Schemes

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

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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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Fragile Watermark for Document AuthenticationFragile Watermark for Document Authentication

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

(f)

alter(a)

(b)

(g)

after alteration

(e)

(c)

(d)

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Pixels with high flippability score are shown in the images.

Unevenness in Data Hiding Unevenness in Data Hiding (Binary Image Example)(Binary Image Example)

Uneven distribution of flippable pixels– most are on rugged boundary

Embedding rate (per block)

– variable: often need side info. worthwhile if such overhead

is relatively small

– constant: require larger block

Random shuffling equalizes distribution – embed more bits– enhance security

a key to generate shuffle table

– con: sensitive to jitter and mis-alignment

0 5 10 15 20 25 30 35 40 45 500

0.05

0.1

0.15

0.2

0.25

embeddble coeff. # per block (signature img)

port

ion

of b

lock

s

before shuffleafter shuffle

Important !Important !

image size 288x48, red block size 16x16

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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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““Innocent Tools” Exploited by AttackersInnocent Tools” Exploited by Attackers

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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Potential civilian use for digital rights management (DRM) Copyright industry – $500+ Billion business ~ 5% U.S. GDP

Alleged Movie Pirate Arrested (23 January 2004)

– A real case of a successful deployment of 'traitor-tracing' mechanism in the digital realm

– Use invisible fingerprints to protect screener copies of pre-release movies

Carmine Caridi Russell friends … Internetw1Last Samurai

Hollywood studio traced pirated version

http://www.msnbc.msn.com/id/4037016/

Case Study: Tracing Movie Screening CopiesCase Study: Tracing Movie Screening Copies

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http://www.msnbc.msn.com/id/4037016/


Collusion Attacks by Multiple UsersCollusion Attacks by Multiple Users

. . .

Averaging Attack Interleaving Attack

Collusion: A cost-effective attack against MM fingerprints

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

Result of fair collusion: – Each colluder contributes equal share through averaging, interleaving,

and nonlinear combining– Energy of embedded fingerprints may decrease

=> Need for Collusion-resistant Fingerprinting

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Embedded Fingerprinting for MultimediaEmbedded Fingerprinting for Multimedia

embedembedDigital

Fingerprint

Multimedia Document

101101 …101101 …

Customer’s ID: Alice

Distribute to Alice

Fingerprinted CopyFingerprinted Copy

embedembedDigital

Fingerprint

Multimedia Document

101101 …101101 …

Customer’s ID: Alice

Distribute to Alice

Fingerprinted CopyFingerprinted Copy

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

Embedded Finger-printing

Multi-user Attacks

Traitor Tracing

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Summary of Today’s LectureSummary of Today’s Lecture Sampling and resampling issues in 2-D and 3-D

– Frequency-domain interpretation of sampling lattice– Sampling rate conversion: spatial-temporal

Basic considerations and techniques of data hiding

To explore more on data hiding1. I. Cox, J. Kilian, T. Leighton, T. Shamoon: “Secure Spread Spectrum

Watermarking for Multimedia'', IEEE Transaction on Image Processing, vol.6, no.12, pp.1673-1687, 1997.

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

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

4. I. Cox, M. Miller, and J. Bloom: “Digital Watermarking,” Morgan Kauffman, 2002.

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enee631 digital image processing (spring'04) introduction to data hiding in image & video...

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