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EE565 Advanced Image Processing Copyright Xin Li@2008
Data Hiding in ImageData Hiding in Image
Adapted from ENEE631 UMD Adapted from ENEE631 UMD ECE by Courtesy of Prof. Min ECE by Courtesy of Prof. Min WuWu
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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [2]
Problem FormulationProblem Formulation
How do we turn visible watermark invisible?How do we turn visible watermark invisible?
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [3]
Idea 1: Data Embedding by Replacing LSBsIdea 1: Data Embedding by Replacing LSBs
Downloaded from http://www.cl.cam.ac.uk/~fapp2/steganography/image_downgrading/
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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [4]
LSB Replacement (cont’d)LSB Replacement (cont’d)
Replace LSB with Pentagon’s MSBUM
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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [5]
Idea 2: LSB Replacement of Higher BitplanesIdea 2: LSB Replacement of Higher Bitplanes
Replace 6 LSBs with Pentagon’s 6 MSBsUM
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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [6]
Review: Pixel DepthReview: Pixel Depth
– “Contour” artifacts for low pixel depthat gradual transition areas
– Human eyes distinguish about 50 gray levels => 5~6 bits/pixel
8 bits / pixel
4 bits / pixel
2 bits / pixel
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [7]
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 and 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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [8]
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
lookup table mapping
0 1 0 1
… 0 1 1 0 …
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [9]
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]
feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Q
odd-even mapping 0 1 0 1
-Q -Q/2 + Q/2 +Q
-Q/2 + Q/2
1/Q
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [10]
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
feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Q
“1”“0”
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [11]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [12]
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
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 …
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [13]
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
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [14]
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
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [15]
From Fragile to Robust WatermarkFrom Fragile to Robust Watermark
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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [16]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [17]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [18]
Watermarking Example by Cox et al.Watermarking Example by Cox et al.
Original Cox Difference between
whole image DCT marked and orig. Embed in 1000 largest coeff.
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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [19]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [20]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [21]
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 >
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [22]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [23]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [24]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [25]
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; …
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [26]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [27]
Block Replacement AttackBlock Replacement Attack
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [28]
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
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [29]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [30]
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
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [31]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [32]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [33]
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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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [34]
Data Hiding in Binary ImageData Hiding in Binary Image
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EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [35]
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)
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [36]
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.
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [37]
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)
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [38]
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
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [39]
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)
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [41]
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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image size: 288x48 red block size: 16x16
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [42]
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
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [43]
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
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [44]
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
EE565 Advanced Image Processing Copyright Xin Li@2008 Lec A.1 – Data Hiding [45]
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.
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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
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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
(f)
alter(a)
(b)
(g)
after alteration
(e)
(c)
(d)
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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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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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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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Appendix II: Introduction to Patent InventionAppendix II: Introduction to Patent Invention
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)
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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
Three classifications in US Patent laws:
– Utility patents of most interest to ECEer A term of 20 years from the date the patent application was filed Granted to anyone who invents or discovers any new and useful
process, machine, manufacture, or composition of matter, or any new and useful improvement thereof
– Design patents new, original & ornamental design for 14 years’ protection
– Plant patents
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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 nece.
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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
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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
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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