1. MMC LAB Secure Spread Spectrum Watermarking for Multimedia KAIST MMC LAB Seung jin Ryu 1MMC LAB

2. Contents 1. Introduction 2. Previous Work 3. Watermarking in the Frequency Domain 4. Structure of the Watermark 5. Experimental Results 6. Conclusion 2

3. Introduction  Sudden increasing of digitized media  Need for copyright enforcement schemes  Cryptography provides little protection  Digital watermark  Intended to complement cryptographic processes  Permanently embedded in the data MMC LAB 3

4. Introduction  Characteristics  Unobtrusive Perceptually invisible  Robustness Must be difficult to remove –Common signal processing –Common geometric distortions –Subterfuge Attacks (Collusion and Forgery)  Universality The algorithm should apply to the other data format  Unambiguousness Unambiguous retrieval of the watermark MMC LAB 4

5. Introduction  Building a strong watermark  Watermark structure The watermark placed explicitly in the perceptually most significant components Perceptual capacity that allows watermark insertion without perceptual degradation  Insertion strategy The watermark drawn from a Gaussian distribution N(0, 1) Offering good protection against collusion MMC LAB 5

6. Previous work  Substituting the insignificant bits  Inserting an identification string into a digital audio  Turner  Watermarks which resemble quantization noise  Quantization noise is imperceptible to viewers  Tanaka et al.  DCT of 8 X 8 blocks  A triple of frequencies is selected, modified  Not based on any perceptual significance  Variance between coefficients is small

7.  Frequency-based scheme  spreads the watermark over the whole spatial extent of the image  Processing operations MMC LAB 7 Watermarking in the Frequency Domain Robustness Unobtrusive Unambiguousness

8. Watermarking in the Frequency Domain  Lossy compression  Eliminates high-frequency components  Geometric distortions  Rotation, translation, scaling, cropping, etc.  Leads to a loss of data in the high-frequency spectral regions  Signal distortions  D/A-A/D conversion, resampling, requantization, etc.  Signal transformations to be undone by using the original image MMC LAB 8

9.  Spread spectrum Coding of a Watermark  Spread spectrum communications Frequency domain – communication channel Watermark – signal  Method Watermark is spread over very many frequency bins The energy in any one bin is very small and undetectable Be inserted imperceptibly in the most significant spectral components of the data –To avoid loss of watermark MMC LAB 9 Watermarking in the Frequency Domain

10.  Embedding & Detecting the Watermark MMC LAB 10 Watermarking in the Frequency Domain

11. Structure of the Watermark  Description of the Watermarking Procedure MMC LAB 11 DefinitionNotation DocumentD ValuesV = v 1, …, v n WatermarkX = x 1, …, x n (x i is chosen by N(0, 1) Wartermarked valuesV’ = v 1 ’, …, v n ’ Attacked documentD* Attacked valuesV* Corrupted watermarkX* generated by V* and V

12.  Inserting and Extracting the Watermark  Formulae for computing V’  Determining Multiple Scaling Parameters More or less tolerance to modification is allowed how sensitive the image is?  In this paper (2) with a single parameter α = 0.1 MMC LAB 12 Structure of the Watermark v i ’ = v i + αx i (1) v i ’ = v i (1 + αx i ) (2) v i ’ = v i (e αxi ) (3)

13.  Choosing the Length, n, of the Watermark  The degree to which the watermark is spread out  Altered components are increased, the extent to which they must be altered decreases. MMC LAB 13 Structure of the Watermark v i ’ = v i + αx i V i *= v i ’+ r i (r i is white noise with standard deviation σ)

14. Structure of the Watermark  Evaluating the Similarity of Watermarks  sim(X, X*) is distributed according to N(0, 1) X* X is distributed by N(0, X* X*)  Robust Statistics Postprocessing for X* causes the improved performance –x* i = x* i – E i (X*) –x* i = 0 (if | x* i | > tolerance) –x* i = sign(x* i – E i (X*)) MMC LAB 14 sim(X, X * ) =

15. Structure of the Watermark  Resilience to Multiple-Document Attacks  Average attack  Discrete watermarks Easier to completely eliminate Watermarks of 1 or -1, eliminated all but a 2 1-t Uniformly chosen watermark can be removed by only 5 documents MMC LAB 15

16. Structure of the Watermark  Resilience to Multiple-Document Attacks  Continuous valued watermarks greater resilience to average attacks

17. Structure of the Watermark  Embedding process  Detecting process MMC LAB 17 2D DCTsort v’=v (1+  w) IDCT & normalize Original image N largest coeff. other coeff. marked image random vector generator wmk seed DCT compute similarity threshold test image decision wmk DCTselect N largest original unmarked image select N largest preprocess –

18. Experimental Result  Original and Watermarked images Original imageWatermarked image

19. Experimental Result The response of the watermark detector : 13.4  Image scaling

20. Experimental Result  JPEG compression JPEG with 10% quality and 0% smoothing response of the watermark detector: 22.8 JPEG with 5% quality and 0% smoothing response of the watermark detector: 13.9

21. E xperimental Result The response of the watermark detector : 5.2  Dithering x* i = x* i – E i (X*) : 10.5

22. E xperimental Result  Cropping The response of the watermark detector : 14.6

23. Experimental Result  Cropping with JPEG image JPEG with 10% quality and 0% smoothing response of the watermark detector: 10.6

24. Experimental Result  Print, xerox, and scan The response of the watermark detector : 4.0 x* i = x* i – E i (X*) x* i = sign(x* i – E i (X*)) :7.0

25. Experimental Result  Rewatermarking

26. Experimental Result  Collusion Attack

27. Experimental Result  Environment  Matlab  256*256 grey Lenna image  Experiments  Difference between another watermark  Quantization error MMC LAB 27 + = sim(X, X*) Normal detecting29.852 After quantization29.8461

28. Conclusion  k random numbers N(0,1) as watermark.  Perceptually most significant components.  maximizes the change of detecting the watermark after attacks.  Experiment  largest 1000 DCT coefficients  Attacks  Scaling, JPEG compression, Dithering, Cropping, Printing, xeroxing, and scanning, Rewatermarking, Collusion  The correlation with the real watermark has a peak.

29. MMC LAB 29MMC LAB