ECE738 Advanced Image Processing Data Hiding (2 of 3) Curtsey of Professor Min Wu Electrical & Computer Engineering Univ. of Maryland, College Park

ECE738 Advanced Image Processing

Data Hiding (2 of 3)

Curtsey of Professor Min Wu Electrical & Computer EngineeringUniv. of Maryland, College Park

Min Wu @ U. Maryland 2002 2ECE738 Advanced Image Processing

Recall: Spread Spectrum Approach• 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)

• Cox’s approach

– Perform DCT on entire image and 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” AC coeff. in block-DCT domain

– Adjust watermark strength by explicitly computing JND


A Few Comments on Cox/Podilchuk Approaches

• “1000 largest coeff.” before and after embedding– May not be identical (and order may also changes)– Solutions: use “embeddable” mask to avoid mis-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• “Embedding with Side Info.” ~ will discuss this later

• Robustness– Very robust against additive noise (seen from detection theory)– Very sensitive to synchronization errors esp. under blind detection and …

• jitters (line dropping/addition)• geometric distortion (rotation, scale, translation)

=> add registration pattern or embed in RST-invariant domain


Data Hiding Beyond Additive Approach


Examples• Odd-even embedding

– Round a feature to nearest even# to embed “0” and to odd# to embed “1”

– Work in quantized domain to achieve limited robustness (-Q/2, +Q/2)

– Err. Correction Codes also help combat errors & improve hiding rate

– Equiv. formulation: Quantization Index Modulation (QIM) [Chen et al.]• select between two non-overlapped quantizer with relative offset Q’/2

• Table look-up embedding: give additional security

feature value 23Q 24Q 25Q 26Qlookup table mapping … 0 0 1 0 …

feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Qodd-even mapping

lookup table mapping

0 1 0 1

… 0 1 1 0 …


Type-II Relationship Enforcement Embedding

• Deterministically enforcing relationship – Partition host signal space into sub-regions

• each region is labeled with 0 or 1• marked sig. is from a region close to orig. & labeled w/ the bit to hide

– Secondary info. carried solely in X’• Difference (X’-X) doesn’t necessarily reflect the embedded data

• Representative: odd-even embedding– No interference from host signal => High capacity but limited robustness

– Robustness achieved by quantization or tolerance zone

mappingmapping{ b}data tobe hidden X

host sig.

X’= f( b )marked copy

1 or 01 or 0


Hiding Data in Binary Images

How Embedding Mechanisms are Used?

Other Issues Besides Embedding Mechanisms


Binary Image: A Simple yet Important Class

– scanned documents, electronic publishing, drawings, signatures

Social Security E-Files From Princeton EE201 lab material


Copyright 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 spread-spectrum or enforcement

from http://www.acm.org/~hlb/publications/dig_wtr/dig_watr.html

• N.F. Maxemchuk, S. Low: “Marking Text Documents”, ICIP, 1997.

•


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 (link no longer valid)

E-PAD (InterLink Electronics)


“Signature in Signature”

– Annotating digitized signature with content info. of the signed document


Binary Image D.H. for Authentication/ Annotation

• Security against forgery is a primary design requirement– Robustness against unintentional noise is desirable but not critical

• Challenges– little room for “invisible” changes– uneven distribution of changeable pixels

• A block-based pixel-domain method– hide a fixed number of bits in each block– extract hidden data without the use of original copy

• Three issues on data embedding– determine which pixels to flip for invisibility– embed data in each block using flippable pixels– handle uneven embedding capacity via shuffling

Robustness is not a major requirement for authentication and annotation applications.


Preserve Visual Quality

• Assign flippability score to each pixel– Determine how noticeable the flipping of a pixel is– Based on smoothness and connectivity– Hierarchical

• Sort pixels in each block according to the scores– Flip high-score pixels with high priority

(a) (b)


Embedding Mechanism• Extracting data without original image

– Hard to directly encode data in flippable pixels• flippability may change after encoding

• Embedding via deterministic enforcement– Manipulate flippable pixels to enforce block-based property

• enforce the total number of black pixels to be odd/even to hide 1 bit / block, or use more general mapping

• incorporate quantization or tolerance zone for robustness

# of black pixel per blk 2kQ (2k+1)Q (2k+2)Q (2k+3)Qodd-even mapping

lookup table mapping

0 1 0 1

… 0 1 1 0 …


Pixels with high flippability score are shown in the images.

Unevenness in Embedding• Uneven distribution of flippable

pixels– most are on rugged boundary

• Embedding rate (per block)

– variable: need side info.

– constant: require larger blk

• Random shuffling equalizes distribution – embed more bits

– enhance security

– 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


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 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


Example-1: “Signature in Signature”

– Annotating digitized signature with content info. of the signed document

Each block is 320-pixel large, 1bit / blk.


Example-2: Annotating Binary Line Drawings

originaloriginal marked w/ marked w/ “01/01/2000”“01/01/2000”

pixel-wise pixel-wise differencedifference


Summary• More on additive spread-spectrum embedding• Alternative embedding mechanism via enforcement• Data hiding in binary image

– Manipulate spacing for copyright protection of text document

– Pixel-domain enforcement for annotation

Next time– Data hiding for authentication

– Embedding Capacity and Advanced “Hybrid” embedding mechanism


Suggested Reading– Wu-Liu: 2-Part, binary wmk– Maxemchuk-Low: Doc. wmk

– Yeung-Mintzer: Authentication– Lin-Chang: Semi-Fragile wmk– Lin-Delp: Authentication survey

See the reading list in course web page