1. ECE738 Advanced Image Processing Data Hiding (2 of 3) Curtsey of Professor Min Wu Electrical & Computer Engineering Univ. of Maryland, College Park

2. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 2 Recall: Spread Spectrum Approach Key points –Place wmk in perceptually significant spectrum (for robustness) Modify by a small amount below Just-noticeable-difference (JND) –Use long random vector as watermark to avoid artifacts (for imperceptibility & robustness) Cox’s approach –Perform DCT on entire image and 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” AC coeff. in block-DCT domain –Adjust watermark strength by explicitly computing JND

3. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 3 A Few Comments on Cox/Podilchuk Approaches “1000 largest coeff.” before and after embedding –May not be identical (and order may also changes) –Solutions: use “embeddable” mask to avoid mis-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 “Embedding with Side Info.” ~ will discuss this later Robustness –Very robust against additive noise (seen from detection theory) –Very sensitive to synchronization errors esp. under blind detection and … jitters (line dropping/addition) geometric distortion (rotation, scale, translation) => add registration pattern or embed in RST-invariant domain

4. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 4 Data Hiding Beyond Additive Approach

5. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 5 Examples Odd-even embedding –Round a feature to nearest even# to embed “0” and to odd# to embed “1” –Work in quantized domain to achieve limited robustness (-Q/2, +Q/2) –Err. Correction Codes also help combat errors & improve hiding rate –Equiv. formulation: Quantization Index Modulation (QIM) [Chen et al.] select between two non-overlapped quantizer with relative offset Q’/2 Table look-up embedding: give additional security feature value 23Q 24Q 25Q 26Q lookup table mapping… 0 0 1 0 … 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 …

6. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 6 Type-II Relationship Enforcement Embedding Deterministically enforcing relationship –Partition host signal space into sub-regions each region is labeled with 0 or 1 marked sig. is from a region close to orig. & labeled w/ the bit to hide –Secondary info. carried solely in X’ Difference (X’-X) doesn’t necessarily reflect the embedded data Representative: odd-even embedding –No interference from host signal => High capacity but limited robustness –Robustness achieved by quantization or tolerance zone mapping { b} data to be hidden X host sig. X’= f( b ) marked copy 1 or 0

7. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 7 Hiding Data in Binary Images  How Embedding Mechanisms are Used?  Other Issues Besides Embedding Mechanisms

8. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 8 Binary Image: A Simple yet Important Class –scanned documents, electronic publishing, drawings, signatures Social Security E-Files From Princeton EE201 lab material

9. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 9 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” for cheap, high-volume content ~ newspaper, magazine, E-books possible to remove watermark, but why not just pay a bulk –Embedding may be through additive spread-spectrum or enforcement from http://www.acm.org/~hlb/publications/dig_wtr/dig_watr.html N.F. Maxemchuk, S. Low: “Marking Text Documents”, ICIP, 1997.

10. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 10 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 http://www.whitehouse.gov/ media/gif/bil.gif as of 7/00 (link no longer valid) E-PAD (InterLink Electronics)

11. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 11 “Signature in Signature” –Annotating digitized signature with content info. of the signed document

12. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 12 Binary Image D.H. for Authentication/ Annotation Security against forgery is a primary design requirement –Robustness against unintentional noise is desirable but not critical Challenges –little room for “invisible” changes –uneven distribution of changeable pixels A block-based pixel-domain method –hide a fixed number of bits in each block –extract hidden data without the use of original copy Three issues on data embedding –determine which pixels to flip for invisibility –embed data in each block using flippable pixels –handle uneven embedding capacity via shuffling Robustness is not a major requirement for authentication and annotation applications.

13. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 13 Preserve Visual Quality Assign flippability score to each pixel –Determine how noticeable the flipping of a pixel is –Based on smoothness and connectivity –Hierarchical Sort pixels in each block according to the scores –Flip high-score pixels with high priority (a) (b)

14. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 14 Embedding Mechanism Extracting data without original image –Hard to directly encode data in flippable pixels flippability may change after encoding Embedding via deterministic enforcement –Manipulate flippable pixels to enforce block-based property enforce the total number of black pixels to be odd/even to hide 1 bit / block, or use more general mapping incorporate quantization or tolerance zone for robustness # of black pixel per blk 2kQ (2k+1)Q (2k+2)Q (2k+3)Q odd-even mapping lookup table mapping 0 1 0 1 … 0 1 1 0 …

15. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 15 Pixels with high flippability score are shown in the images. Unevenness in Embedding Uneven distribution of flippable pixels –most are on rugged boundary Embedding rate (per block) –variable: need side info. –constant: require larger blk Random shuffling equalizes distribution –embed more bits –enhance security –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

16. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 16 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 %9.78x10 -5 0 m 1 /N (1 st bin) 1.85%7.77x10 -4 %0 %3.79x10 -4 0 m 2 /N (2 nd bin) 5.56%5.81x10 -3 %5.56x10 -3 %0.0010

17. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 17 Example-1: “Signature in Signature” –Annotating digitized signature with content info. of the signed document Each block is 320- pixel large, 1bit / blk.

18. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 18 Example-2: Annotating Binary Line Drawings original marked w/ “01/01/2000” pixel-wise difference

19. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 19 Summary More on additive spread-spectrum embedding Alternative embedding mechanism via enforcement Data hiding in binary image –Manipulate spacing for copyright protection of text document –Pixel-domain enforcement for annotation  Next time –Data hiding for authentication –Embedding Capacity and Advanced “Hybrid” embedding mechanism

20. ECE738 Advanced Image Processing Min Wu @ U. Maryland 2002 20 Suggested Reading –Wu-Liu: 2-Part, binary wmk –Maxemchuk-Low: Doc. wmk –Yeung-Mintzer: Authentication –Lin-Chang: Semi-Fragile wmk –Lin-Delp: Authentication survey  See the reading list in course web page