ENEE631 Digital Image Processing (Spring'09) Data Hiding in Images Spring ’09 Instructor: Min Wu Electrical and Computer Engineering Department, University.
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What’s new compared with odd-even embedding?– Mapping from feature to embedded bit is less predictable– Adjacent intervals may be mapped to the same bit value
How much security gained with proprietary LUT? =>
– Proprietary LUT brings uncertainty and makes it difficult for attackers to embed specific data at his/her will
How much MSE introduced by embedding? =>
– Larger than odd-even embedding How much resilience gained? =>
– Moving away by Q/2 step may not trigger detection error Due to possible continuous run in LUT
Ref: M. Wu: "Joint Security and Robustness Enhancement for Quantization Based Embedding," IEEE Trans. on Circuits and Systems for Video Technology, vol. 13, no. 8, pp.831-841, August 2003. (see ICIP’03 for shorter conf. version)
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Case Study: Mintzer-Yeung Patent on Fragile WmkCase Study: Mintzer-Yeung Patent on Fragile Wmk
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)
ENEE698T – Technology Laws (first offered F’08; see future offering)
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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
Common types of U.S. patent: http://www.uspto.gov/go/taf/patdesc.htm
– Utility patents of most interest to ECEer A term up to 20 years from the date the patent application was filed Granted to anyone who invents a new and useful process, machine,
manufacture, or composition of matter, or a new and useful improvement thereof
– Design patents new, original & ornamental design for 14 years’ protection
– Plant patents
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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 needed
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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
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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
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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
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From Fragile to Robust WatermarkFrom Fragile to Robust Watermark
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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 original as ref; use “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 >
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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” coefficients => improve
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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; …
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Suggested ReadingsSuggested Readings
1. I. Cox, J. Kilian, T. Leighton, T. Shamoon: “Secure Spread Spectrum Watermarking for Multimedia'', IEEE Trans. on Image Proc., vol.6, no.12, pp.1673-1687, 1997.
2. M. M. Yeung and F. Mintzer: “An Invisible Watermarking Technique for Image Verification", Proc. of the IEEE Int’l Conf. on Image Processing (ICIP), Oct. 1997.
3. M. Wu and B. Liu: "Data Hiding in Image and Video: Part-I -- Fundamental Issues and Solutions", IEEE Trans. on Image Proc., vol.12, no.6, pp.685-695, June 2003.
4. M. Wu, W. Trappe, Z.J. Wang, and K.J.R. Liu: “Collusion-resistant fingerprinting for Multimedia,” IEEE Signal Proc Magazine, March 2004.
5. M. Wu and B. Liu: Multimedia Data Hiding, Springer-Verlag, 2003.
6. I. Cox, M. Miller, and J. Bloom: Digital Watermarking, Morgan Kauffman, 2002.
And the related references cited by these publications.UM
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Block Replacement AttackBlock Replacement Attack
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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
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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.
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
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Suggested ReadingsSuggested Readings
1. I. Cox, J. Kilian, T. Leighton, T. Shamoon: “Secure Spread Spectrum Watermarking for Multimedia'', IEEE Trans. on Image Proc., vol.6, no.12, pp.1673-1687, 1997.
2. M. M. Yeung, F. Mintzer: “An Invisible Watermarking Technique for Image Verification", Proc. of the IEEE Int’l Conf. on Image Processing (ICIP), Oct. 1997.
3. M. Wu and B. Liu: "Data Hiding in Image and Video: Part-I -- Fundamental Issues and Solutions", IEEE Trans. on Image Proc., vol.12, no.6, pp.685-695, June 2003.
4. M. Wu, W. Trappe, Z.J. Wang, and K.J.R. Liu: “Collusion-resistant fingerprinting for Multimedia,” IEEE Signal Proc Magazine, March 2004.
5. M. Wu and B. Liu: Multimedia Data Hiding, Springer-Verlag, 2003.
6. I. Cox, M. Miller, and J. Bloom: Digital Watermarking, Morgan Kauffman, 2002.
And the related references cited by these publications.UM
– Add a noise-like signal and detection via correlation– Good tradeoff between security, imperceptibility & robustness– Limited capacity: host signal often appears as major interferer
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
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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)
Distortion compensation technique– Increase quantization step by a factor for higher robustness– Compensate the extra distortion by dragging the enforced feature
toward the original feature value
Overall embedding distortion unchanged
Choose alpha to maximize a distortion-compensation SNR
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Improved Robustness by Distortion CompensationImproved Robustness by Distortion Compensation
– ICS (ideal Costa’s scheme)– SS (spread spectrum additive
embedding)– binary DM (odd-even quantized
embedding)– binary SCS (odd-even quantized
embedding with distortion compensation)
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Issues to AddressIssues to Address
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
– Tailor to media characteristics for robustness & imperceptibility
Interaction between choices of fingerprint construction, embedding, and detection
– esp. to combat collusion attacks– Analogous to “cross-layer” methods in communications
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Road Map on Media Fingerprinting ResearchRoad Map on Media Fingerprinting Research
Robust EmbeddingOrthogonal Fingerprints
Represent how many users?Resist how many colluders?
“most effective” collusions?
Amount of resources used?
Group-based FP to exploit Attacker Behavior
Coded FP
Joint Coding-Embedding Framework
overcome prior work’s problems of long code length, low resilience, and limited scalability
adapt to media characteristics
Combinatorial codes + CDM
Error correcting codes + TDM
Correlated Fingerprints
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Example: Orthogonal Fingerprint for Curves/GraphicsExample: Orthogonal Fingerprint for Curves/Graphics
Use (approx.) orthogonal sequences as FPs for different users
– Detection by looking for high correlation result
Embed in parametric modeling domain of curve
– Perturb B-spline parameters according to spread spectrum sequences
Detection Statistics
Typical threshold is 3~6 for false alarm of 10-3 ~ 10-9
Original Curve(captured by TabletPC)
Fingerprinted Curve(100 control points)
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Fingerprinting Topographic MapFingerprinting Topographic Map– Traditional protection: intentionally alter geospatial content– Embed much less intrusive digital fingerprint for a modern protection
• 9 long curves are marked; 1331 control points used to carry the fingerprint
1100x1100 Original Map Fingerprinted Map
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Collusion-Resistant Fingerprinting of MapsCollusion-Resistant Fingerprinting of Maps
2-User Interleaving Attack5-User Averaging Attack
. . .
Can survive combined attacks of collusion + print + scan
Can extend to 3-D Digital Elevation Map
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16-bit ACC for Detecting 16-bit ACC for Detecting 3 Colluders Out of 20 3 Colluders Out of 20
Embed fingerprint via HVS-based spread spectrum embedding in block-DCT domain
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Suggested ReadingsSuggested Readings
1. I. Cox, J. Kilian, T. Leighton, T. Shamoon: “Secure Spread Spectrum Watermarking for Multimedia'', IEEE Trans. on Image Proc., vol.6, no.12, pp.1673-1687, 1997.
2. M. M. Yeung, F. Mintzer: “An Invisible Watermarking Technique for Image Verification", Proc. of the IEEE Int’l Conf. on Image Processing (ICIP), Oct. 1997.
3. M. Wu and B. Liu: "Data Hiding in Image and Video: Part-I -- Fundamental Issues and Solutions", IEEE Trans. on Image Proc., vol.12, no.6, pp.685-695, June 2003.
4. M. Wu, W. Trappe, Z.J. Wang, and K.J.R. Liu: “Collusion-resistant fingerprinting for Multimedia,” IEEE Signal Proc Magazine, March 2004.
5. M. Wu and B. Liu: Multimedia Data Hiding, Springer-Verlag, 2003.
6. I. Cox, M. Miller, and J. Bloom: Digital Watermarking, Morgan Kauffman, 2002.
And the related references cited by these publications.UM