ELE 488 F06 ELE 488 Fall 2006 Image Processing and Transmission (12 – 7 – 06) Digital...
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Transcript of ELE 488 F06 ELE 488 Fall 2006 Image Processing and Transmission (12 – 7 – 06) Digital...
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission
(12 – 7 – 06)
Digital Watermarking
What?
Why?
How?
Attacks, Legal issues
12/7
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Digital Watermark
• What?– secondary information in perceptual media data– examples: picture in picture, words in words, data in data, sound in sound
• Why?
• How?
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Vatican Digital Library
– Founded 1451 by Pope Nicholas V– 150,000 manuscripts (trace back to 300~400 AD)– 1.5 million books– only 2,000 scholars visit each year– security
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from IBM web page
IBM - Vatican Library
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Picture in Picture
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Picture in Picture
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Picture in Picture
http://www.cl.cam.ac.uk/~fapp2/steganography/image_downgrading/
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Is seeing believing?
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All for a royal smile
LONDON, June 21 - One of the official photographs of the wedding of Prince Edward and Sophie Rhys-Jones last Saturday was doctored because Prince Williams ….. did not look happy enough ….
From the New York Times, June 22, 1999
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E-files: binary “images”
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Authorized and unauthorized modification
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Document Authentication
alter
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Words in Words
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Digital Watermark
• What?– Secondary information in perceptual media data
• Why?– To convey other information
– To Detect Alteration
• Easy to modify authentication
• Easy to copy without degradation ownership
• Seeing is believing?
• How?
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Picture in Picture
http://www.cl.cam.ac.uk/~fapp2/steganography/image_downgrading/
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Additional Functionality – error concealment
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How can watermark help to detect alteration?
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Document Authentication
– Embed prescribed 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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Document Authentication
– Embed prescribed 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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Digital Watermark
• What?– Secondary information in perceptual media data
• Why?– To Detect Alteration
• Easy to modify authentication
• Easy to copy without degradation ownership
• Seeing is believing?
– Convey other information
• How? – Insertion and detection
• Replacing LSB
• Fragile watermark
• Robust watermark
• Binary images (documents)
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Picture in Picture
http://www.cl.cam.ac.uk/~fapp2/steganography/image_downgrading/
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Replacing LSBU
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Replacing LSBs
Replace 6 LSBs with Pentagon’s 6 MSBsUM
CP
EN
EE
63
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s (c
rea
ted
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Fragile WM to Detect Modification
Yeung & Mintzer ICIP’97
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Fragile WM - Pixel-domain Table-lookup -
• Pro: simple; be able to localize alteration extracted wmk from altered image
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# of changeable coeff: 0-30 / blk embed 1bit/blk by repetition
Embed in Compressed Domain
• limit LUT runs of 1’s and 0’s• DC and small coeffs not modified
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Difficulties: smooth region
– smooth region difficult to embed, uneven embedding
– solutions: backup, shuffling
– use ‘error difusion’ in pixel domain embedding
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Uneven embedding capacity
0 5 10 15 20 25 30 350
0.05
0.1
0.15
0.2
0.25
# of embeddable coefficients per interval: (--) before shuffle (-) mean of shuffle (..) mean+/-std
% o
f int
erva
ls
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– embedding rate … >= 1 bit / block
Wu-Liu Scheme: shuffling (cont’d)
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Embedding and Extraction
– What to embed? @
• meaningful pattern: facilitate quick visual check and locate alteration
• low-level content-based features to help detect alteration
ELE 488 F06 Fridrich & Goljan ICIP 99
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• Ideas– Place wmk in perceptually significant spectrum– use random vector as wmk to avoid artifacts
– scaling N=1000 largest AC coeff. {vi} by a random factor
– wi – iid, zero mean, unit variance
• Embedding: vi’ = vi(1+a wi), a ~ 0.1
• How to extract w ?
2D DCT sort v’=v (1+a w) IDCT & nomalize
original image N largest coeff.
other coeff.
marked imageN random numbers
wmk
seed
Robust Watermark - Spread Spectrum Embedding(NEC scheme, Cox’s scheme)
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• Detection: based on hypothesis testing
– measure and threshold the similarity between Y & W
DCT
compute similarity
thresholdtest image
decision
wmkDCT select N
largest
original unmarked image
select N largest
preprocess
k watermar
watermarkno
:1
:0
NWYH
NYHXXY
-
YY
WYWYsim
,
,),(
-orig X
test X’
X’=X+W+N ?
X’=X+N ?
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Random sequence of 1 and –1
1 1 1 -1 -1 1 -1line up x) 1 1 1 -1 -1 1 -1 1 1 1 1 1 1 1 sum = 7
1 1 1 -1 -1 1 -1 shift 1 1 1 -1 -1 1 -1 1 1 -1 1 -1 -1 sum = 0 1 1 1 -1 -1 1 -1 0shift 2 0 1 1 1 -1 -1 1 -1 1 -1 -1 -1 1 sum = -1shift 3 , sum = 0shift 4 , sum = -1shift 5 , sum = 0shift 6, sum = -1 results symmetric if shifted to the left.
Correlation: -1 0 -1 0 -1 0 7 0 -1 0 -1 0 -1
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0 20 40 60 80 100-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
0 50 100 150 200-50
0
50
100
100 binary random numbers
0 50 100 150 200-30
-20
-10
0
10
20
30
correlation
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Two binary random sequences
0 20 40 60 80 100-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
0 50 100 150 200-40
-30
-20
-10
0
10
20
cross correlation
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0 500 1000 1500 2000-50
0
50
100
150
200
250
300
350
0 500 1000 1500 2000-60
-40
-20
0
20
40
60
1000 random numbers (-1, 1)
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0 500 1000 1500 2000-30
-20
-10
0
10
20
30
40
0 20 40 60 80 100-1.5
-1
-0.5
0
0.5
1
1.5
x: 1 or -1, s = sine signal, sx = s+.05*x, correlate x & sx
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Cox’s Scheme (cont’d)
• Robustness:– (claimed) scaling, JPEG, dithering, cropping, “printing-
xeroxing-scanning”, multiple watermarking
• Comment:– must store original unmarked image “private wmk”
– perform 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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Examples ... wmk & distortions
Original JPEG 10% (~30:1 compression) lowpass
– Robust watermark are designed to survive distortions:
markedorig.wmk
distorteddistortion
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Compare NEC & Bell schemes
Orig.? NEC ? Bell – PU ?
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Compare NEC & Bell schemes (cont’d)
NEC Bell - PU
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Document (AT&T)
from http://www.acm.org/~hlb/publications/dig_wtr/dig_watr.html
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Original Altered (flipx=2000)
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The Difference
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References
• F. Mintzer, G.W. Braudaway, M.M. Yeung, “Effective and Ineffective Digital Watermarks”, IEEE ICIP 97
• Cox, J. Kilian, T. Leighton, T. Shamoon: “Secure Spread Spectrum Watermarking for Multimedia'', IEEE Trans Image Processing, Dec 1997
• M Wu, B Liu, “Watermarking for image authentication”, ICIP 98.
• M. Wu, B. Liu, “Data Hiding in Binary Images for Authentication and Annotation", IEEE Trans Image Processing, August 2004.
• J. Fridrich, M. Goljan,”Image with Self-correcting Capabilities, ICIP 1997