ele 488 f06 ele 488 fall 2006 image processing and transmission (12 – 12 – 06) digital...

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

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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

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Uneven capacity equalization

Error correction

Security

……

Low

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Imperceptible embeddingof one bit

Multiple-bit embedding

Coding of embedded data

Robustness

Capacity

Imperceptibility

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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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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


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).

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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

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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

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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

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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

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09-28-06

Edge Map

Laplacian

Median Filter

Filtering Images in Frequency Domain

Image Restoration

9/28/06

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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

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Re-sampling and Re-sizing

1D 2D, sinc interpolation nearest neighbor, bilinear, bicubic, . . .

Geometric Transformation

translation rotation scaling Affine

10/12/06

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Geometric Transformation

translation rotation scaling Affine

Image Registration

Goodness of fit

10/17/06

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Image Compression

Review of Basics Huffman coding run length coding

Quantization independent samples uniform and optimum correlated samples vector quantization

10/19/06

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Image Compression

Quantization independent samples uniform and optimum correlated samples vector quantization

JPEG block based transform coding . . . .

10/24/06

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Image Compression


10/26/06

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ELE 488 Fall 2006Image Processing and Transmission (11-10 -06)


Why DCT for Image transform? DFT DCT Wavelet

11/10

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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

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Lossy wavelet encoding

Subband decomposition & coding Wavelet transform Embedded zero tree

Successive approximation quantization

Digital Video

11/20

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Digital Video

•Motion Pictures

•Broadcast Television

•Digital Video

11/28

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ELE 488 Fall 2006Image Processing and Transmission (12 – 5 – 06)

Reconstruction from Projection

•Radon Transform

•Reconstruction

•Computation

12/5

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ELE 488 Fall 2006Image Processing and Transmission (12 – 7 – 06)

Digital Watermarking

What?

Why?

How?

Attacks, Legal issues

12/7

ele 488 f06 ele 488 fall 2006 image processing and transmission (12 – 12 – 06) digital...

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