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EE565 Advanced Image Processing Copyright Xin Data Hiding in Image Adapted from ENEE631 UMD ECE by Courtesy of Prof. Min Wu UMCP ENEE631 Slides.

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Presentation on theme: "EE565 Advanced Image Processing Copyright Xin Data Hiding in Image Adapted from ENEE631 UMD ECE by Courtesy of Prof. Min Wu UMCP ENEE631 Slides."— Presentation transcript:

1 EE565 Advanced Image Processing Copyright Xin Data Hiding in Image Adapted from ENEE631 UMD ECE by Courtesy of Prof. Min Wu UMCP ENEE631 Slides (created by M.Wu © 2004)

2 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [2] Problem Formulation How do we turn visible watermark invisible?

3 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [3] Idea 1: Data Embedding by Replacing LSBs Downloaded from steganography/image_downgrading/ UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

4 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [4] LSB Replacement (cont’d) Replace LSB with Pentagon’s MSB UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

5 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [5] Idea 2: LSB Replacement of Higher Bitplanes Replace 6 LSBs with Pentagon’s 6 MSBs UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

6 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [6] Review: Pixel Depth –“Contour” artifacts for low pixel depth at gradual transition areas –Human eyes distinguish about 50 gray levels => 5~6 bits/pixel 8 bits / pixel 4 bits / pixel 2 bits / pixel

7 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [7] Embedding Basics: Two Simple Tries Data Hiding: To put secondary data in host signal (1) Replace LSB (2) Round a pixel value to closest even or odd numbers u Both equivalent to reduce effective pixel depth for representing host image u Detection scheme is same as LSB, but embedding brings less distortion in the quantized case and for higher LSB bitplane + Simple embedding;  Fragile to even minor changes even “0” odd “1” pixel value odd-even mapping lookup table mapping … … UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

8 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [8] How to Improve the Robustness? Introduce quantization to embedding process –Make features being odd/even multiple of Q Tradeoff between embedding distortion and robustness Larger Q => Higher resilience to minor changes => Higher average changes required to embed data Questions: What’s the expected embedding distortion? Relation with distortion by quantization alone? feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Q odd-even mapping lookup table mapping … …

9 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [9] Distortion from Quantization-based Embedding Uniform quantization with step size Q (Assume source’s distribution within each interval is approx. constant) MSE = Q 2 / 12 Odd-even embedding with quantization MSE = ½ * (Q 2 /12) + ½ * (7Q 2 /12) = Q 2 / 3 u MSE equiv. to quantize with 2Q step size! u “Predistort” via quantization to gain resilience [-Q/2, Q/2] feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Q odd-even mapping Q -Q/2 + Q/2 +Q -Q/2 + Q/2 1/Q

10 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [10] Two Views of Quantization-based Embedding From decoder’s view –Partition the signal space into two subsets labeled “0” & “1” –Decode according to which subset a sample belongs to u Embedder picks watermarked sample from the subset labeled with to- be-embedded bit, and tries to minimize the amount of changes From embedder’s view –Design two quantizers “#0”, “#1”: step size 2Q, offset by Q –Embedder perform quantization using the quantizer labeled with to-be-embedded bit => “Quantization Index Modulation (QIM)” u Decoder looks for closest representative feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Q “1” “0”

11 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [11] Tampering Detection by Pixel-domain Fragile Wmk Downloaded from ICIP’97 CD-ROM paper by Yeung-Mintzer UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

12 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [12] Fight Against Forging Tamper-Detection Watermark? If using LSB to embed a fragile watermark for tampering detection, adversary can alter image but retain LSB [Solution 1] Add uncertainty to the embedding mapping –through a random look-up table with controlled run length [Solution 2] Make watermark securely depend on host content E.g. embed a robust/content-base hash of host image feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Q odd-even mapping lookup table mapping … …

13 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [13] Pixel-domain Table-lookup Embedding Pixel-domain Table-lookup Embedding (Yeung-Mintzer ICIP’97) –Simple to implement; be able to localize alteration extracted wmk from altered image

14 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [14] Yeung’s Fragile Watermark for Tampering Detection Basic idea: –enforce certain relationship to embed data –minimize distortion: nearest neighbour, constrained runs –diffuse error incurred to surrounding pixels v’=v+d1+d2: LUT(v’)=b original image marked image lookup table generator LUT( ) seed data to be embedded table lookup test image extracted data LUT( ) visualize &decide embed detect d1: diffused error

15 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [15] From Fragile to Robust Watermark UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

16 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [16] From Fragile/Semi-Fragile to Robust Watermark Applications of fragile/semi-fragile watermark –Tampering detection –Secret communications => “Steganography” (covert writing) –Convey side info. in a seamless way: lyric, director’s notes Situations demanding higher robustness –Protect ownership (copyright label), prevent leak (digital fingerprint) –Desired robustness against compression, filtering, etc. How to make it robust? –Use “quantization” from signal processing –Use error correcting coding –Borrow theories from signal detection & telecommunications u “Spread Spectrum Watermark”: use “noise” as watermark and add it to the host signal for improved invisibility and robustness UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

17 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [17] Spread Spectrum Watermark: Cox et al (NECI) What to use as watermark? Where to put it? –Place wmk in perceptually significant spectrum (for robustness) u Modify by a small amount below Just-noticeable-difference (JND) –Use long random noise-like vector as watermark u for robustness/security against jamming+removal & imperceptibility Embedding v’ i = v i +  v i w i = v i (1+  w i ) –Perform DCT on entire image and embed wmk in DCT coeff. –Choose N=1000 largest AC coeff. and scale {v i } by a random factor 2D DCTsort v’=v (1+  w) IDCT & normalize Original image N largest coeff. other coeff. marked image random vector generator wmk seed UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

18 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [18] Watermarking Example by Cox et al. Original Cox Difference between whole image DCTmarked and orig. Embed in 1000 largest coeff. UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

19 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [19] Cox et al’s Scheme (cont’d): Detection  Subtract original image from the test one before feeding to detector (“non-blind detection”)  Correlation-based detection u a correlator normalized by |Y| in Cox et al. paper DCT compute similarity threshold test image decision wmk DCTselect N largest original unmarked image select N largest preprocess – – orig X test X’ X’=X+W+N ? X’=X+N ? To think UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

20 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [20] Performance of Cox et al’s Scheme Robustness –(claimed) scaling, JPEG, dithering, cropping, “printing-xeroxing- scanning”, multiple watermarking –No big surprise with high robustness u equiv. to sending just 1-bit {0,1} with O(10 3 ) samples Comment –Must store orig. unmarked image  “private wmk”/“non-blind” detection –Perform image registration if necessary –Adjustable parameters: N and  UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

21 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [21] Comments on Cox et al’s Schemes “1000 largest coeff.” before and after embedding –May not be identical (and order may also changes) –Solutions: use orig. as ref; “embeddable” mask to maintain synch. Detection without using original/host image –Treat host image as part of the noise/interference ~ Blind detection u 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.” H0: H1: = vs. H0: = H1: =

22 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [22] Improve 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) u JND ~ max amount each coefficient can be modified invisibly u Employ human visual model: freq. sensitivity, masking, … –Use more localized transform => fine tune wmk for each region u block-based DCT; wavelet transform Improve robustness: detection performance depends on ||s|| /  d u Add a watermark as strong as JND allows u Embed in as many “embeddable” coeff. => improve robustness Block-DCT schemes: Podichuk-Zeng; Swanson-Zhu-Tewfik ’97 –Leverage existing visual model for block DCT from JPEG UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

23 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [23] Perceptual Comparison: Cox vs. Podilchuk OriginalCox Podilchuk whole image DCT block-DCT Embed in 1000 largest coeff. Embed to all “embeddables” UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

24 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [24] Compare Cox & Podilchuk Schemes (cont’d) CoxPodilchuk Amplified pixel-wise difference between marked and original (gray~0) UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

25 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [25] Comments on Cox & Podilchuk’s SS Wmk Robustness –Very robust against additive noise (seen from detection theory) –Sensitive to synchronization errors, esp. under blind detection u jitters (line dropping/addition) u geometric distortion (rotation, scale, translation) Question: How to improving synchronization resilience? => add registration pattern; embed in RST-invariant domain; …

26 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [26] Localized Embedding: Double-Edge Sword “Innocent Tools” exploited by attackers: block concealment 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 UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

27 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [27] Block Replacement Attack

28 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [28] Attack effective on block-DCT based spread-spectrum watermark marked original (no distortion)JPEG 10%after proposed attack 512x512 lenna Threshold: 3 ~ 6 Recall: claimed high robustness&quality by fine tuning wmk strength for each region

29 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [29] 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, a general should know both his troop and the opponent’s as well as possible. -- Sun Tzu, The Art of War, 500 B.C. UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

30 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [30] Summary: Spread Spectrum Embedding Main ideas –Place wmk in perceptually significant spectrum (for robustness) u 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 = v i +  v i w i = v i (1+  w i ) –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

31 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [31] Summary: Type-I Additive Embedding Add secondary signal in host media Representative: spread spectrum embedding –Add a noise-like signal and detection via correlation –Good tradeoff between security, imperceptibility & robustness –Limited capacity: host signal often appears as major interferer modulation data to be hidden  X original source X’ = X +  marked copy = UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

32 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [32] Type-II Relationship Enforcement Embedding 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 u “Quantization Index Modulation” –Robustness achieved by quantization or tolerance zone –High capacity but limited robustness even “0” odd “1” feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Q odd-even mapping lookup table mapping … … UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

33 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [33] Robustness 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. theoryRobustnessPayload Imperceptibility noise stronger noise weaker UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

34 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [34] Data Hiding in Binary Image UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

35 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [35] Binary Image: A Simple yet Important Class –Scanned documents, drawings, signatures Social Security E-Files From Princeton EE201 lab material E-PAD (InterLink Electronics)

36 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [36] 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” u for cheap, high-volume content ~ newspaper, magazine, E-books u possible to remove watermark, but why not just pay a bulk –Embedding may be through additive or enforcement methods from N.F. Maxemchuk, S. Low: “Marking Text Documents”, ICIP, 1997.

37 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [37] 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 media/gif/bil.gif as of 7/00 E-PAD (InterLink Electronics)

38 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [38] Challenges on Hiding Data in Binary Images Only two levels are available –Black-white flipping –Minor tuning on the color is not available Little room for “invisible changes” –What places can be changed and what cannot Uneven distribution of changeable pixels Related to authentication –Extract hidden data without the use of original copy

39 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [39] Identify Flippable Pixels Flippability score –Take the human perception into account u Based on smoothness and connectivity –0~1, with 0 indicating the pixels that should not be flipped flip-score # of pixels (a) (b)

40 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [40] Pixels with high flippability score are shown in the images. Unevenness in Data Hiding (Binary Image Example) Uneven distribution of flippable pixels –most are on rugged boundary Embedding rate (per block) –variable: often need side info. u worthwhile if such overhead is relatively small –constant: require larger block Random shuffling equalizes distribution –embed more bits –enhance security u a key to generate shuffle table –con: sensitive to jitter and mis-alignment embeddble coeff. # per block (signature img) portion of blocks before shuffle after shuffle Important ! image size 288x48, red block size 16x16 UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

41 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [41] Uneven Distribution of Flippable Pixels Most on rugged boundary Multi-bit embedding via spatial division –Partition the image into non-overlapping blocks Embedding rate (per block) –variable: need side info. –constant: require larger blocks Two advanced mechanisms to equalize the distribution –Random shuffling –Recent generalized approach: Wet paper codes UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04) image size: 288x48 red block size: 16x16

42 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [42] Shuffling and Block-based Embedding Shuffling to equalize distribution of flippables (54 blocks) Divide the image into blocks and hide one bit in each block –Manipulating pixels with the highest flippability scores in the block –Odd-even embedding u To embed a “0”: even number of black pixels u To embed a “1”: odd number of black pixels

43 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [43] Shuffling-based Embedding and Extraction Data embedding Data Extraction Marked binary image Shuffling Block- based embedding Inverse shuffling Original binary image Data to be embedded Key Key Computeflippability Shuffling Shuffling Block- based extraction Test binary image Key Key Extracted data Enhance security Simple

44 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [44] before 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 shufflestd after shuffle analysissimulationanalysissimulation m 0 /N (0 th bin) 20.37%5.16x10 -5 %0 %9.78x m 1 /N (1 st bin) 1.85%7.77x10 -4 %0 %3.79x m 2 /N (2 nd bin) 5.56%5.81x10 -3 %5.56x10 -3 %0.0010

45 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [45] Application: “Signature in Signature” –Annotating digitized signature with content info. of the signed document Each block is 320- pixel large, 1bit / blk.

46 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [46] Application: Annotating Binary Line Drawings u 10 characters (~ 70bits) are embedded original marked w/ “01/01/2000” pixel-wise difference

47 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [47] Fragile Watermark for Tamper Detection of Document u Embed pre-determined pattern or content features beforehand u Verify hidden data’s integrity to decide on authenticity (f) alter (a) (b) (g) after alteration (e) (c) (d) UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

48 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [48] Robust Wmk Application for Tracing Traitors Leak of information as well as alteration and repackaging poses serious threats to government operations and commercial markets –e.g., pirated content or classified document Promising countermeasure: robustly embed digital fingerprints –Insert ID or “fingerprint” (often through conventional watermarking) to identify each user –Purpose: deter information leakage; digital rights management(DRM) –Challenge: imperceptibility, robustness, tracing capability studio The Lord of the Ring Alice Bob Carl w1 w2 w3Sell UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

49 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [49] Potential civilian use for digital rights management (DRM) u u 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 CaridiRussellfriends … Internet w1 Last Samurai Hollywood studio traced pirated version Case Study: Tracing Movie Screening Copies UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

50 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [50] 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 UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

51 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [51] Embedded Fingerprinting for Multimedia Embedded Finger- printing Multi-user Attacks Traitor Tracing UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

52 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [52] Appendix 1: Quick Review on Signal Detection Hypothesis testing –Error-free case: for observation values where only one hypothesis having non-zero probability (or prob. density)

53 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [53] Quick Review on Signal Detection (cont’d) Minimize probability of detection error –Equal probable prior: maximum likelihood (ML) detection decision rule:arg j max P j (y) –General prior probability: scale by prior probability decision rule: arg j max P(H j ) P j (y) = arg j max P(H j | y) u Maximum A Posteriori (MAP) detection u Likelihood ratio test with proper threshold

54 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [54] Review on Detection of Signal Sequence in Noise Formulation u Known signals; noise values unknown but statistics known Under equal prior probability and AWGN noise –Equivalent to minimum distance detection –For equal-power signals, equiv. to maximum correlation detection (1) Antipodal signaling: s 1 =  s 0 = s (2) On-Off signaling: s 0 = 0; s 1 = s u detection performance proportional to the square root of ratio between signal energy and noise power

55 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [55] Conveying One-bit Info. Through Noisy Channel [ Known signal s; noise values unknown but statistics known ] Optimal detection ~ minimize prob. of error MAP ~ maximize posterior probability …P(H ? |y) => ML ~ maximum likelihood detector [for equal prior] … P(y|H ? ) => Minimum distance detector [for i.i.d. Gaussian noise] … ||y – bs|| => Maximum correlation detector [for equal-energy sig.] Detection statistic (under i.i.d. Gaussian noise) [correlator]  i y i s i  normalize variance prob. distribution under each hypothesis ~ N(  ||s|| 2, ||s|| 2  d 2 ) T b=1 b=  1

56 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [56] Illustration: Anti-podal Signaling & Detection Project onto the deterministic signal and check correlation –Positive high correlation => (s1, s2) being sent with high confidence; –Negative high correlation => (-s1, -s2) being sent Use long signal to boost SNR –Each component may be subject to low power constraints, but overall signal energy can be large by increasing sig. length => two sig. point (red dots) can be widely separately, thus reliably detected We can analyze on-off keying similarly.

57 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [57] Hypothesis Testing for Ownership Watermark Optimal detection for On-Off Keying (OOK) –under i.i.d. Gaussian noise {d i } u use a correlator-type detector u b  {0,1} represents absence vs. presence of ownership mark –Need to determine how to choose watermark signal {s i } Neyman-Pearson Detection [Poor’s book Sec.2.4] –False-alarm: claiming wmk existence when nothing embedded –Given max. allowed false-alarm, maximize detection probability u Use likelihood ratio as detection statistic u Determine threshold based on false-alarm prob. => Q(3)=10 -3 ; Q(6)=10 -5 –Pd vs. Pfp tradeoff ~ ROC curve UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04) T presence absence

58 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [58] Appendix II: Introduction to Patent Invention F. Mintzer and M.M. Yeung: “Invisible Image Watermark for Image Verification,” U.S. Patent 5,875,249, issued Feb 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”) u 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 u Online workshop on Patent 101 (see also the patent handout) u US Patent Office (full-text patent search and patent doc. images)

59 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [59] A 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 Three classifications in US Patent laws: –Utility patents  of most interest to ECEer u A term of 20 years from the date the patent application was filed u Granted to anyone who invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof –Design patents u new, original & ornamental design for 14 years’ protection –Plant patents

60 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [60] Patent 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 nece.

61 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [61] Structure 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

62 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [62] Useful 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

63 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [63] Claims: 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 u Claim Tree: useful tree visualization to illustrate relations between claims

64 EE565 Advanced Image Processing Copyright Xin Lec A.1 – Data Hiding [64] Reading Assignment (1) M. M. Yeung, F. Mintzer: “An Invisible Watermarking Technique for Image Verification", Proc. of the IEEE Int’l Conf. on Image Processing (ICIP'97), Oct (2) F. Mintzer and M.M. Yeung: “Invisible Image Watermark for Image Verification,” U.S. Patent 5,875,249, issued Feb For further exploration (3) M. Wu and B. Liu: "Watermarking for Image Authentication", ICIP'98, vol.2. (4) 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 , August (see ICIP’03 for shorter conf. version)


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