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ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission
(12 – 12 – 06)
Digital Watermarking What? secondary information in media data
Why? to convey additional information to detect alteration
How? insertion and detection Attacks, Legal issues
Binary Images
Self Embedding
12/12
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Embedding in Binary Images
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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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How to Embed Information
• Flip pixels from black to white or white to black• Pixel is flippable if changing it causes no noticeable artifacts• Construct flippable score of all patterns.
Compute: smoothness – # of transition
connectivity – # of clusters
Compare smoothness and connectivity before and after flipping
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Flippable Score
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How to Embed Information
• Odd–Even Embedding– Partition image into blocks – To embed “0” in a block, enforce the total number of black pixels
in that block to be even. – Change that pixel with the highest flippable score– To embed “1”, force total number of black pixels to be odd.
• Generalization – Choose a step size of Q and enforce the total number of black
pixels in a block to be 2kQ (for some k) to embed a “0”, and to be (2k+1)Q otherwise.
– Enhances robustness– Q = 1 Odd–Even
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Not all blocks have flippable pixels
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– embedding rate … >= 1 bit / block
Wu-Liu Scheme: shuffling (cont’d)
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Summary
Extraction of watermark does not require original
Geometric Attacks
Variations and Generalizations
ELE 488 F06 Fridrich & Goljan ICIP 99
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Self – Correcting Images
• Divide image into 8 x 8 blocks; DCT; Zig – Zag• Quantize DCT coefficients using quantization matrix corresponding
to a 50% quality JPEG• Encode each coeff with a fixed # of bits, so that total # of bits is 64
or 128.• Insert the 64-bit string of block B into the LSB of the block B + p,
where p is a vector of length approximately 3/10 of the image size with a randomly chosen direction.
Q 16 11 10 16 24 40 51 61 L 7 7 7 5 4 3 2 1 12 12 14 19 26 58 60 55 7 6 5 5 4 2 1 0 14 13 16 24 40 57 69 56 6 5 5 4 3 1 0 0 14 17 22 29 51 87 80 62 5 5 4 3 1 0 0 0 18 22 37 56 68 109 103 77 4 4 3 1 0 0 0 0
24 35 55 64 81 104 113 92 3 2 1 0 0 0 0 0 49 64 78 87 103 121 120 101 2 1 0 0 0 0 0 0 72 92 95 98 112 100 103 99 1 0 0 0 0 0 0 0
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“Self Embedding”
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Reconstruction
Original
Watermarked
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Encoding
• First 3 coefficients encoded using the same # of bits as in Table.
• Next 18 bits indicate which of coefficients No. 4–21 are not zero. Followed by values of nonzero coefficients.
• Continue same way if under bit budget, 2 coeff at a time• Average code length ~100 bits (1.55 bits / pixel) • Vulnerable to attacks
Generalizations and Variations?
Trade off vs payload
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Example
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Issues 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
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Uneven capacity equalization
Error correction
Security
……
Low
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Lay
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Imperceptible embeddingof one bit
Multiple-bit embedding
Coding of embedded data
Robustness
Capacity
Imperceptibility
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ELE 488 F06
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 U
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“Innocent 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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• 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 Copies
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ELE 488 F06
Collusion 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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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.
• M. Wu, W. Trappe, Z.J. Wang, and K.J.R. Liu: “Collusion-resistant fingerprinting for Multimedia,” IEEE Signal Proc Magazine, March 2004.
• J. Fridrich and M. Goljan, “Images with Self-Correcting Capabilities”, ICIP 1999.
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission
Bede Liu, B330, x4628, [email protected]
Bryan Conroy, F-2G2, [email protected]
Office hours, help sessions, reviews, etc will be announced on blackboard
image = picture; processing = working on Working on Pictures
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Why Image Processing?
• Enhancement – improve appearance, reveal more details• Analysis and extraction of content – face, motion, patterns,
object tracking• Storage and distribution – encoding, compression, transmission• Digital Library – indexing, searching• Medical imaging – diagnosis, exploration • Remote sensing – weather map, terrain mapping, monitoring of
environment• Radar and microwave imaging – mapping of sky, moon• Security – biometrics, detection of events• Watermarking – data hiding • Composition – Magazines, Movies• Display and printing
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Some Logistics
• Labs, homeworks, project suggestions will be posted on blackboard.
• Assignments, Labs and Project are based on Paintshop Pro and MATLAB. You can use any computer loaded with MATLAB and the Signal and Image Processing Toolboxes.
• A Lab Room (F-113) for ELE 488 has been reserved on Wednesday and Thursday evening, except next week.
• Bryan will be in the Lab to answer questions on those evenings.
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Grading(tentative)
• Midterm I (Thursday, midterm week) 20% • Assignments and Labs 20%• Midterm II (last week of classes in Dec.) 20%• Final project: 4 parts, all graded: 40%
– Project Proposal (due week after fall break)– Progress Report (due last day of classes Dec.)– Oral Presentation using power point (due reading period)
** Note that we will meet 2 or 3 times during reading period– Final Report (due Dean’s date).
ELE 488 F06
Books and References
• Primary– A.K. Jain: Fundamentals of Digital Image Processing, Prentice-
Hall, 1989. – R.C. Gonzalez and R.E. Woods: Digital Image Processing,
Prentice Hall, 2001. – R.C. Gonzalez, R.E. Woods and S.L. Eddins: Digital Image
Processing using Matlab, Prentice Hall, 2004. – Y. Wang, J. Ostermann, Y-Q. Zhang: Digital Video Processing
and Communications, Prentice-Hall, 2001.– Introductory sections in Matlab Image Processing Toolbox
http://www.mathworks.com/access/helpdesk/help/toolbox/images/images.shtml
• Other references– To be announced
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission
Syllabus
1. Human Visual System 2. Image Representations (gray level, color)3. Simple Processing: point operations and filtering 4. Still Image Coding5. Resampling, Resizing, Interpolation, and Registration6. Probability Models, Quantization, Estimating Densities7. Synthesizing Pixels, Segmentation8. Radon Transform, Other imaging modes9. Video, Video Compression 10. Selected Topics: watermarking, feature description, face recognition, . . .
9/19/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission
09-21-06
1. Generate and Display of Gray Scale images in Matlab
2. Histogram of Gray Scale Image
3. Point Operations: brightness, contrast, gamma – correction
9/21/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission
09-26-06
Linear 2-D Image Filtering
1-D discrete convolution 2-D discrete convolution 2-D spatial masks Mask filtering Mask filtering and 2-D convolution
Spatial Averaging, Blurring, Image Sharpening
Edge Map - gradient
9/26/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission
09-28-06
Edge Map
Laplacian
Median Filter
Filtering Images in Frequency Domain
Image Restoration
9/28/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission
10-3-06
Image Restoration
distortion noise
Inverse Filtering
Wiener Filtering
Ref: Jain, Sec 8.1 – 8.3. Gonzalez–Woods, Sec 5.5 – 5.8
10/3/06
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ELE 488 Fall 2006Image Processing and Transmission (10-5-06)
Wiener Filtering
Derivation Comments
Re-sampling and Re-sizing
1D 2D
10/5/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (10-12-06)
Re-sampling and Re-sizing
1D 2D, sinc interpolation nearest neighbor, bilinear, bicubic, . . .
Geometric Transformation
translation rotation scaling Affine
10/12/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (10-17-06)
Geometric Transformation
translation rotation scaling Affine
Image Registration
Goodness of fit
10/17/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (10-19-06)
Image Compression
Review of Basics Huffman coding run length coding
Quantization independent samples uniform and optimum correlated samples vector quantization
10/19/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (10-24-06)
Image Compression
Quantization independent samples uniform and optimum correlated samples vector quantization
JPEG block based transform coding . . . .
10/24/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (10-26-06)
Image Compression
JPEG block based transform coding . . . .
10/26/06
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (11-10 -06)
JPEG block based transform coding . . . .
Why DCT for Image transform? DFT DCT Wavelet
11/10
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (11-16 -06)
JPEG block based transform coding Is DCT best? decorrelation, energy compaction Karhunen-Loeve (K-L) transform
Spatial correlation Subband decomposition & coding Wavelet transform Zero tree
11/16
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (11-20 -06)
Lossy wavelet encoding
Subband decomposition & coding Wavelet transform Embedded zero tree
Successive approximation quantization
Digital Video
11/20
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (11-28 -06)
Digital Video
•Motion Pictures
•Broadcast Television
•Digital Video
11/28
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (12 – 5 – 06)
Reconstruction from Projection
•Radon Transform
•Reconstruction
•Computation
12/5
ELE 488 F06
ELE 488 Fall 2006Image Processing and Transmission (12 – 7 – 06)
Digital Watermarking
What?
Why?
How?
Attacks, Legal issues
12/7