EE565 Advanced Image Processing Copyright Xin Li@2008 Data Hiding in Image Adapted from ENEE631 UMD ECE by Courtesy of Prof. Min Wu UMCP ENEE631 Slides.

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?


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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LSB Replacement (cont’d)LSB Replacement (cont’d)

Replace LSB with Pentagon’s MSBUM

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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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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


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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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


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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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 and orig. 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 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


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


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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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

MC

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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

(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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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

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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)


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


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.


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

EE565 Advanced Image Processing Copyright Xin Li@2008 Data Hiding in Image Adapted from ENEE631 UMD ECE by Courtesy of Prof. Min Wu UMCP ENEE631 Slides.

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