1. Robust Motion Watermarking based on Multiresolution Analysis Tae-hoon Kim Jehee Lee Sung Yong Shin Korea Advanced Institute of Science and Technology

2. Introduction Watermarking Embedding signature into the media data Applications of watermarking Ownership protection (robust watermarking ) Data authentication Fingerprinting Secret data hiding ………

3. Objectives Robust watermarking for motion data Imperceptible Non-invertible Robust to attacks smoothing, cropping, scaling, type conversion, quantization, adding noise, adding another watermark, …

4. Ownership Protection with Watermark insertion watermark registration extracted watermark extraction + - analysis of similarity original motion watermarked motion registered suspect motion

5. Previous Work [Schyndel et al. 1994] Modifying the least significant bits [Tanaka et al. 1990] Embedding noise-like watermarks [Cox et al. 1997] Introducing spread-spectrum for images [Praun et al. 1999] Employing spread-spectrum for 3D meshes

6. Spread Spectrum Watermarking Embedding a watermark with redundancy original signal insertion + watermarked signal watermark signal original signal insertion + watermarked signal watermark signal Properties of spread spectrum:  JR (jam resistance)  LPI (low probability of intercept)

7. Spread Spectrum Approaches Images [Cox et al. 1997] Discrete cosine transform Modifying the most important coefficients image watermarked image frequency domain

8. Spread Spectrum Approaches 3D meshes [Praun et al. 1999] Multiresolution analysis 3D meshbasis functions watermarked mesh basis function

9. Our Approach Spread spectrum watermarking for motion motion signal … motion data … Motion data = bundle of motion signals of position or orientation

10. Our Approach Problem: Difficult to obtain frequency information from the motion data due to complication caused by orientations Solution: Extracting frequency information from multiresolution representation

11. Multiresolution Representation Representing at multiple resolutions Hierarchy of successive smoother and coarser signals Hierarchy of displacement maps

12. Decomposition Reduction : smoothing, followed by down-sampling Expansion : up-sampling, followed by smoothing Both of them can be realized by spatial masking [Lee2000] ReductionExpansionReductionExpansionReductionExpansion

13. Representation and Reconstruction Representation … … Reconstruction … …

14. Motion Watermarking Based on multiresolution analysis Watermark insertion Watermark extraction Analysis of similarity between inserted and extracted watermarks

15. Watermark Insertion Decomposing motion signal original signal Multiresolution Representation … coarse base signal detail coefficients

16. Watermark Insertion Perturbing the largest coefficients original signal … coarse base signal detail coefficients the i-th largest coefficient coarse base signal detail coefficients … altered coefficient scaling parameter watermark coefficient

17. Watermark Insertion Reconstructing the motion signal original signal coarse base signal detail coefficients … watermarked signal

18. Watermark Insertion Perturbation of coefficient  Embedding watermark into wide range original motion + watermark signal watermarked motion

19. Watermark Extraction Registering original and suspect motion Using dynamic time warping [Bruderlin1995] dynamic time warping resampling original signal suspect signal original signal registered suspect signal

20. Watermark Extraction Decomposing motion signals original signal suspect signal … coarse base signal detail coefficients coarse base signal detail coefficients …

21. Watermark Extraction Comparing watermarked coefficients … coarse base signal detail coefficients coarse base signal detail coefficients … comparing

22. Watermark Extraction Extracting suspect watermark Obtaining from scaling parameter

23. Analysis of Similarity Computing false-positive probability False-positive probability (P fp ): Probability of incorrectly asserting that the data is watermarked when it is not Using Student’s t-test From correlation

24. Experimental Results Data A Data B Data C Data D

25. Experimental Results Original Motion and Watermarked Motion Fly Spin Kick

26. Experimental Results Original Motion and Watermarked Motion Blown Back

27. Experimental Results Results for various attacks Adding noise attack on Fly Spin Kick

28. Experimental Results Results for various attacks Adding the second watermark on Fly Spin Kick

29. Experimental Results Results for various attacks Smoothing attack on Blown Back

30. Experimental Results Results for various attacks Time warping attack on Blown Back

31. Experimental Results

32. Conclusion and Future Works Watermarking schemes for motion data Spread spectrum approach Using multiresolution motion analysis Robust to attacks Future works Consideration for other attacks Blind detection Watermark extraction from rendered images

34. Q/A : False-negative Probability False-negative Probability Probability of failing to detect watermarked data lesser important than false-positive probability More difficult to analyze since it depends on the type and magnitude of attacks

35. Q/A : Non-invertible Watermark Generating non-invertible watermark randomly selected from seeded by cryptographic hash function with (original data + owner’s key) original data owner’s key hashed value random numbers