1. Multiple watermarking Wu Dan 2007.10.10

2. Introduction (I) Multipurpose watermarking Ownership watermarks (very robust) Captioning watermarks ( robust) Verification watermarks( fragile) Multi-user watermarking The difficulty of multiple watermarking is the order.

3. Introduction (II) The basic method of watermarking SS (spread spectrum) x ’ =x+αw QIM (quantization index module) Odd even odd even 0 1 0 1

4. Multipurpose Watermarking for Image Authentication and Protection Chun-Shien Lu, Member, IEEE, Hong-Yuan Mark Liao, Member, IEEE IEEE TRANSACTIONS ON IMAGE PROCESSING, OCTOBER 2001

5. I ） cocktail watermarking scheme Bipolar watermarking Complementary modulation Use of a wavelet-based human visual system to control the hiding strength

6. II) Proposed multipurpose algorithm Wavelet transform

7. Quantization of wavelet coefficient S: scale o: orientation (x,y): position MTU: masking threshold units

8. Negative modulation Positive modulation

9. q(|p(x,y)|) is regarded as the embedded watermark values. Negative modulation positive modulation

10. Host image recovery The difference between a recovered wavelet coefficient and its corresponding original wavelet coefficient Watermark detection

11. Compare the hidden watermark (K) and the extracted one ( ) Detection of robust watermark

12. Detection of fragile watermark

17. A novel blind multiple watermarking technique for images Peter H. W. Wong, Member, IEEE, Oscar C. Au, Senior Member, IEEE, and Y. M. Yeung IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, AUGUST 2003

18. I)SWE (single watermarking embedding) Select image pixels or transform coefficients Watermark host vector: Watermark: A pseudorandom bit sequence:

19. The first key: a set of N pseudorandom positive real numbers The second key: being zero-mean Gaussian with variance Split K and Y into N subvectors of equal length

21. Force the projection of Y i to be the center of the nearest cell of the desired watermark bits Decode the watermark of SWE

22. II) MWE Embed Q bits simultaneously in each subvector Y i. The first key: The second key:

24. Direct approach

25. Iterative approach Decode and detection

26. III) IWE in JPEG compressed domain Problem: when the original image for the proposed watermarking algorithm is a JPEG-compressed image and the watermarked image needs to be JPEG recompressed to produce another.jpg. Would the watermark still be decodable?

27. Watermark host vector: Y 1 =(f 1 (0,1),f 2 (0,1), …… f 32*32 (0,1)) Y 2 =(f 1 (1,0),f 2 (1,0), …… f 32*32 (1,0)) …… in zigzag order. Y ’ i =Y i +N i

33. Near optimal watermark estimation and its countermeasure: antidisclosure watermark for multiple watermark embedding Chun-Shien Lu, Member, IEEE, and Chao-Yung Hsu 2007.4 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,

34. I) Watermark estimation X: the original image; : the watermarked image. :the attacked image Conventional attacks: The collusion attack

35. Copy attack: the estimated watermark can be inserted into unwatermarked media data to produce a counterfeit watermark data.

36. Compare denoising attack and copy attack X: the original image; : the watermarked image. :the estimated watermark is the watermark extracted from if BER(, ) >threshold, the denosing attack succeed.

37. Z: the faked original image; : the faked watermarked image. is the watermark extracted from if BER(, ) <threshold, the copy attack succeed. A smaller threshold resist copy attack. A larger threshold resist denosing attack.

38. II) Optimal watermark estimation

39. Necessary Condition for Optimal Watermark Estimation { } Sufficient and Necessary Condition for Optimal Watermark Estimation Perfect cover data recovery

40. A near-perfect cover data recovery algorithm For each embedding unit with index q. We adopt Weiner filtering for denosing purpose to get an estimation.

41. Collusion Estimation of Watermark Sign: Estimation of Watermark Magnitude via Visual Model for Complete Removal: the wavelet coefficient for the recovered image is:

44. III) Content dependent watermark Media hash (MH) The magnitude relation ship between two AC coefficient at blocks u and v. This feature value is verified to be robust because this magnitude relationship can be mostly preserved under incidental modifications (e.g., compressions, filtering, and denoising).

45. CDW (content-dependent watermark) Resistant collusion attack

46. Resistant copy attack

49. Conclusion Non-uniform quantization Design the perfect CDW

50. Thanks!