Study of Vector Quantization and its Applications to Information Hiding

1. The Study of Vector Quantization and Its Applications to Information Hiding向量量化技術之研究及其在訊息隱藏之應用 Advisor: Chin-Chen Chang1, 2 Student: Wen-Chuan Wu2 1 Dept. of Information Engineering and Computer Science, Feng Chia University 2 Dept. of Computer Science and Information Engineering, National Chung Cheng University

2. The Fields of Vector Quantization • “VQ": Block-based quantizer • Applications: • Signal compression (i.e. Image, Speech, …) • Feature recognition • Information security • Video-based event detection • Anomaly intrusion detection • …

3. Outline • Part I: Design and Analysis of VQ- Based Algorithms • Part II: VQ Applications to Information Hiding • Fixed Embedding • Adaptive Embedding • Reversible Embedding

4. Part I: Design and Analysis of VQ-Based Algorithms • Fast Planar-Oriented Ripple Search Algorithm for Hyperspace VQ Codebook

5. i Squared Euclidean distance 16 rounds of “-” 15 rounds of “+”16 rounds of “×” Issue: How to speed up the search? VQ • Overview: X 512

6. The sorted projected points in DTPC method[8] • Related works • Full-search equivalents • Rough distortion elimination to filter impossible codewords • Partial-search methods • Organize the codebook by some data structures to label a local search domain (Array, Binary Tree, …)

7. Comparison • Full-search equivalents • To get the best result. • To consume much computation time on-line for a rejection test. • Partial-search methods • Some operations are off-line. (Fast searching) • To need extra memory space. • To get a closer result.

8. Only one point in a cell • PX Planar-Oriented Ripple Search(Planar Voronoi Diagram Search; PVDS [15]) • How to construct a Voronoi diagram: Perpendicular bisector

9. The adjacency list of one ripple Planar-Oriented Ripple Search(Planar Voronoi Diagram Search; PVDS [15]) An example of planar Voronoi diagram with 13 points

10. Experiments (Without LUT operation) Codebook size: 256

11. Experiments (Without duplication) Codebook size: 256

12. Experiments (With duplication) Codebook size: 256

13. Experiments

14. Part II: VQ Applications to Information Hiding • Hiding secrets in VQ (SMVQ) codes • Adaptive embedding • Reversible data hiding

15. Data Hiding Compressed codes: 1011101111….. 1101011001….. Information (187)10 (214)10 Internet Sender Receiver Information

16. Pair Data Hiding in VQ Codes (Jo and Kim [29]) 18 46 46 18 19

17. Seed Block SMVQ(PSNR=31.27) VQ(PSNR=29.11) Block artifacts Seed Block Residual Block Side Match VQ (SMVQ) • Assumption: Neighboring pixel intensities in an image are prettysimilar.

18. X = (81, 15, 53, 34, 51,?, ?, ?, 91, ?, ?, ?, 49,?, ?, ?) Codebook(512) State codebook(16)

19. indicator (a) (b) (c) 3 Data Hiding in SMVQ and VQ Codes [12] 1 1 THSMVQ THVQ Bit=1

20. Experiments Codebook size: 256State codebook size: 16 SMVQ: 9253VQ: 6473No secrets: 403

21. Experiments

22. Data Hiding in VQ Codes • Not every image block has the same capacity. MELG(mean value) PNNE(Euclidean distance) ACE [20] (Cartesian product) Codebook

23. ACE method (Du and Hsu, 2003) Secret data = (0011110)2 = (30)10 Clustering result Modified index table:

24. Adaptive Embedding (1) • Data reuse: [13] To avoid the codeword waste in a group. s2 s1 s3 s2 s1 00 0 00 Index table 0 01 1 01 1 10 0 10 0 11 11 Secret data = (001111)2 Clustering result Modified index table:

25. Experiments Codebook size: 512

26. Experiments

27. Experiments Codebook size: 512 Non-reuse reuse

28. Experiments PSNR results at different embedding capacities for the “Lena” image

29. Experiments Local results in the “Lena” image produced by different hiding methods (capacity = 16 kilobit)

30. Adaptive Embedding (2) • Codeword movement: [17] To increase payload capacity

31. Adaptive Embedding (2) • Adaptive alternatives: [17] To hide the secret bits in SMVQ codes

32. Experiments Utility rate of codewords in the sorted state codebook by SMVQ

33. Experiments Codebook: 512State codebook: 16

34. Experiments PSNR results at different embedding capacities for the “Lena” image

35. Reversible Data Hiding Compressed codes: 1011101111….. 1101011001….. Information Internet Sender Original codes 1011101111….. Information Receiver

36. Reversible Data Hiding • Clustering of codeword-trios Embeddable indicator × ×

37. Two-bit extendable embedding

38. Experiments Codebook size: 256

39. Experiments Codebook size: 256

40. Experiments

41. Future Research Directions • Fast VQ codebook search • Other projection + Voronoi diagram = full-search equivalent • SMVQ efficiency • Reversible data hiding • Construct a unique relation of one-to-one mapping • Apply to other codes (SOC, STC, …) • Other VQ applications

42. Thanks for your attention

43. Miss • HOSM scheme (Wang and Yang [56], 2005) • Hierarchy-Oriented Searching Method • Use the iterated-clustering concept to put the hierarchical structure together in order to create representative virtual codewords (non-leaf nodes) in a Tree structure.

44. SCS scheme(Tai et al. [51], 1996)

45. Shie et al.’sscheme(2006) Block diagram of the embedding procedure in [46]

46. GoldHill