1. ENEE631 Digital Image Processing (Spring'04) Introduction to Data Hiding in Image & Video Spring ’04 Instructor: Min Wu ECE Department, Univ. of Maryland, College Park   www.ajconline.umd.edu (select ENEE631 S’04) UMCP ENEE631 Slides (created by M.Wu © 2004) Based on ENEE631 Spring’04 Section 16

2. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [2] Overview and Logistics Last Time: –High-order nonlinear spatial warping –2-D sampling at Rectangular grid –Lattice theory for multidimensional sampling at non-rectangular grid Today: –Finish the discussion on sampling issues => => [ See Slides in Lecture 24 ] –Introduction to data hiding in image and video UMCP ENEE631 Slides (created by M.Wu © 2004)

3. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [3] 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 + Simple embedding;  Fragile to even minor changes even “0” odd “1” pixel value 98 99 100 101 odd-even mapping lookup table mapping 0 1 0 1 … 0 1 1 0 … UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

4. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [4] Example of Replacing LSBs (1) Downloaded from http://www.cl.cam.ac.uk/~fapp2/ steganography/image_downgrading/ UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

5. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [5] Example of Replacing LSBs (2) Replace LSB with Pentagon’s MSB UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

6. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [6] Example of Replacing LSBs (3) Replace 6 LSBs with Pentagon’s 6 MSBs UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

7. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [7] 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)

8. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [8] From Fragile to Robust Watermarking Applications of fragile watermark –Tampering detection –Secret communications => “Steganography” (covert writing) –Convey side information in a seamless way: lyric, director’s notes Situations demanding higher robustness –Protect ownership (copyright label), prevent leak (digital fingerprint) –Desire robustness against compression, filtering, etc. How to make it robust? –Use “quantization” from signal processing => Type-II –Borrow theories from telecommunications => Type-I 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)

9. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [9] 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)

10. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [10] Type-II Relationship Enforcement Embedding Deterministically enforcing relationship –Secondary info. 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 e.g. to hide data in binary image, enforcing # of black pixels per block to odd/even => additional issues in handling uneven embedding capacity even “0” odd “1” feature value 2kQ (2k+1)Q (2k+2)Q (2k+3)Q odd-even mapping lookup table mapping 0 1 0 1 … 0 1 1 0 … UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

11. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [11] Type-II Relationship Enforcement (cont’d) General approach: –Partition host signal space into sub-regions u each region is labeled with 0 or 1 u marked sig. is from a region close to orig. & labeled w/ the bit to hide –Secondary info. carried solely in X’ u difference (X’-X) doesn’t necessarily reflect the embedded data Advanced embedding: –Combining the two types with techniques suggested by information theory mapping { b} data to be hidden X host sig. X’= f( b ) marked copy 1 or 0 UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

12. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [12] 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 imperceptibility and robustness –Limited capacity: host signal often appears as major interferer modulation data to be hidden  X original source X’ = X +  marked copy 10110100... = UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

13. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [13] Spread Spectrum Approach: Cox et al (NECI) Key points –Place wmk in perceptually significant spectrum (for robustness) u Modify by a small amount below Just-noticeable-difference (JND) –Use long random vector as watermark to avoid artifacts (for imperceptibility & robustness) 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)

14. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [14] 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)

15. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [15] 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 conveying just 1-bit {0,1} with O(10 3 ) samples Comment –Must store original unmarked image  “private wmk”, “non-blind” detect. –Perform image registration if necessary –Adjustable parameters: N and  UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

16. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [16] Robustness vs. Capacity Blind/non-coherent detection ~ original copy unavailable Robustness and capacity tradeoff Advanced embedding: quantization w/ distortion-compensation –Combining the two types with techniques suggested by info. theoryRobustnessCapacity Imperceptibility noise stronger noise weaker UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

17. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [17] 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 Upper Layers Uneven capacity equalization Error correction Security …… Lower Layers Imperceptible embedding of one bit Multiple-bit embedding Coding of embedded data Robustness Capacity Imperceptibility UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

18. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [18] More Detailed Discussions => => UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

19. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [19] SS Wmk Detection Based on Hypothesis Testing Optimal detection for On-Off Keying (OOK) –OOK under i.i.d. Gaussian noise {d i } u b  {0,1} represents absence vs. presence of ownership mark u Use a correlator-type detector (recall the review last week) –Need to determine how to choose {s i } Neyman-Pearson Detection [Poor’s book Sec.2.4] –False-alarm ~ claiming wmk existence when nothing embedded –Given max. allowed false-alarm, try to minimize prob. of miss detection u Use likelihood ratio as detection statistic u Determine threshold according to false-alarm prob. UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

20. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [20] Invisible Robust Wmk: Improved Schemes 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 freq. coeff. can be modified imperceptibly u Use  i for each coeff.  finely tune wmk strength –Better tradeoff between imperceptibility and robustness u Try to add a watermark as strong as possible Block-DCT based schemes: –Podichuk-Zeng and Swanson-Zhu-Tewfik –Existing visual model for block DCT: JPEG UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

21. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [21] Compare Cox & Podilchuk Schemes 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)

22. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [22] Compare Cox & Podilchuk Schemes (cont’d) CoxPodilchuk UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

23. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [23] Distortion Compensated Quantization Embedding 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 odd/even mapping 0 1 0 UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

24. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [24] Fragile Watermark for Document Authentication 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)

25. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [25] 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 05101520253035404550 0 0.05 0.1 0.15 0.2 0.25 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)

26. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [26] 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)

27. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [27] “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 UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

28. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [28] 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 http://www.msnbc.msn.com/id/4037016/ Case Study: Tracing Movie Screening Copies UMCP ENEE631 Slides (created by M.Wu © based on Research Talks ’98-’04)

29. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [29] 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)

30. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [30] 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)

31. ENEE631 Digital Image Processing (Spring'04) Lec25 – Data Hiding [31] Summary of Today’s Lecture Sampling and resampling issues in 2-D and 3-D –Frequency-domain interpretation of sampling lattice –Sampling rate conversion: spatial-temporal Basic considerations and techniques of data hiding To explore more on data hiding 1. I. Cox, J. Kilian, T. Leighton, T. Shamoon: “Secure Spread Spectrum Watermarking for Multimedia'', IEEE Transaction on Image Processing, vol.6, no.12, pp.1673-1687, 1997. 2. M. Wu, W. Trappe, Z.J. Wang, and K.J.R. Liu: “Collusion-resistant fingerprinting for Multimedia,” IEEE Signal Proc Magazine, March 2004. 3. M. Wu and B. Liu: “Multimedia Data Hiding,” Springer-Verlag, 2003. 4. I. Cox, M. Miller, and J. Bloom: “Digital Watermarking,” Morgan Kauffman, 2002. UMCP ENEE631 Slides (created by M.Wu © 2004)