enee631 digital image processing (spring'04) introduction to data hiding in image & video...
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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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [4]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [5]
Example of Replacing LSBs (2)Example of Replacing LSBs (2)
Replace LSB with Pentagon’s MSBUM
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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [6]
Example of Replacing LSBs (3)Example of Replacing LSBs (3)
Replace 6 LSBs with Pentagon’s 6 MSBsUM
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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [7]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [8]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [9]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [10]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [12]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [13]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [14]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [15]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [16]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [17]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [18]
More Detailed DiscussionsMore Detailed Discussions =>=>
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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [19]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [20]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [21]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [22]
Compare Cox & Podilchuk Schemes (cont’d)Compare Cox & Podilchuk Schemes (cont’d)
Cox Podilchuk
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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [23]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [24]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [25]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [26]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [27]
““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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [28]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [29]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [30]
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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ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [31]
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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