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 …
一種區塊的量化技術; Feature: face, speech

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

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, …)
The sorted projected points in DTPC method [8]

7. Comparison Full-search equivalents Partial-search methods
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. Perpendicular bisector
Planar-Oriented Ripple Search (Planar Voronoi Diagram Search; PVDS [15])
How to construct a Voronoi diagram:
Perpendicular bisector
Only one point in a cell PX

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

10. Experiments (Without LUT operation)
Ripples Images 1 2 3 4 5
DTPC
FSVQ
Lena
PSNR (dB)
26.910
30.920
31.254
31.313
31.334
31.336
30.623
31.338
Time (sec.)
0.111
0.233
0.623
1.298
2.140
3.687
0.375
9.937
Count
6.9
19.2
37.5
62.4
95.2
5.7
256
Boat
24.990
28.853
29.227
29.295
29.308
29.314
28.438
29.321
0.142
0.265
0.597
1.232
2.173
3.766
0.391
9.922
6.8
18.8
37.3
62.9
95.1
6.1
Jet
25.797
30.085
30.438
30.504
30.513
30.515
29.529
30.517
0.141
0.250
0.531
1.141
1.936
3.332
0.343
9.891
6.3
17.9
34.7
56.3
86.4
5.1
Codebook size: 256

11. Experiments (Without duplication)
Methods Images
TSVQ [25]
CPTSVQ
[4]
SCS [51]
DTPC [8]
HOSM [56]
PVDS [15]
FSVQ
Lena
PSNR (dB)
28.238
29.225
30.658
30.623
30.005
31.254
31.338
Time (sec.)
1.656
1.797
0.647
0.375
0.687
0.623
9.937
Count 52 56
17.2
5.7
17.3
19.2
256
Boat
26.539
27.436
28.493
28.438
28.246
29.227
29.321
1.781
0.619
0.391
0.671
0.597
9.922
11.8
6.1
18.8
Jet
27.532
28.494
29.671
29.529
29.448
30.438
30.517
0.621
0.343
0.640
0.531
9.891
12.0
5.1
16.1
17.9
Codebook size: 256

12. Experiments (With duplication)
Methods Images
SCS [51]
HOSM [56]
PVDS [15]
FSVQ
Lena
PSNR (dB)
31.338
Time (sec.)
0.971
1.448
0.910
9.937
Count
28.2
38.5
29.2
256
Pepper
30.596
30.707
30.708
30.712
0.866
1.433
0.950
9.890
25.0
37.8
27.5
Baboon
24.038
24.068
24.078
24.104
1.501
1.278
9.907
28.3
39.4
32.9
Codebook size: 256

13. Experiments Methods TSVQ CPTSVQ SCS DTPC HOSM PVDS FSVQ
Encoding complexity
O(k× log2N)
O(log2GD + NG)
O(TH×k + log2N)
O(log4N + NH)
O(log2N + NV)
O(k×N)
Duplication ability No
Yes

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

15. Data Hiding Compressed codes: 10111011 11….. 11010110 01…..
Information
(187)10
(214)10
Internet
Sender
Receiver
Information

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

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

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

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

20. Experiments SMVQ: 9253 VQ: 6473 No secrets: 403 Methods Images
Jo and Kim’s method [29]
Our method [12]
Secrets
PSNR
Bit Rate
Lena
14905
28.39
0.5
15735
28.67
0.44
Boat
13893
27.74
15649
28.38
0.43
Jet
14288
28.81
15676
29.61
0.41
Pepper
15023
28.72
15726
29.37
0.42
Baboon
12162
23.54
13891
23.77
0.55
Sailboat
13863
27.50
15756
27.76
0.45
Codebook size: 256 State codebook size: 16
SMVQ: 9253 VQ: 6473 No 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 = (001 1110)2 = (30)10
Clustering result
Modified index table: 5 3 8 6

24. Adaptive Embedding (1)
Data reuse: [13] To avoid the codeword waste in a group. 6 1 8 5 2 s2 s1 s3 s2 s1
Index table 5 6 7 00 1 2 3 4 8 5 6 7 00 1 2 3 4 1 01 1 01 10 10 11 11
Secret data = ( )2
Clustering result
Modified index table: 7 2 8 6 4

25. The number of clustering groups
Experiments TH
The number of clustering groups
Lena
Jet
Pepper
Capacity (bit)
PSNR (dB) 9
475
5926
32.16
11422
31.446
4689
31.339 12
449
10286
32.028
13014
31.382
9046
31.225 20
389
19658
31.532
19843
31.133
18623
30.798 35
287
32212
30.285
37244
29.869
29178
29.786 60
154
51444
27.664
45815
27.785
47339
27.409 90 73
65623
25.153
67842
25.258
66944
24.541
130 28
78935
21.798
73487
23.125
76689
21.894
Codebook size: 512

26. Experiments

27. Average payload (bit/block)
Experiments
VQ compressed image
Payload (kb)
Average payload (bit/block)
PSNR (dB)
MGLE
PNNE
ACE
Our method
Lena (PSNR = dB) 4
0.25
28.834
31.053
31.487
32.185
32.189 8
0.5
26.866
30.178
30.492
32.039
32.078 16 1
24.555
28.778
29.817
31.683
31.762 32 2 -
28.588
29.942
30.311 48 3
27.613
26.994
27.867 64
26.401
24.220
25.153 80 5
24.610
18.641
21.968
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: 512 State codebook: 16

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

35. Reversible Data Hiding
Compressed codes:
…..
…..
Information
Internet
Sender
Original codes
…..
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 Methods Images VQ (0.5 bpp)
Jo and Kim’s method
Our method (1 bit / index)
Our method (2 bits / index)
PSNR
PNSR
Payload
Rate
Lena
31.338
28.514
15650
12016
0.54
23884
0.67
Jet
30.517
28.270
15388
13163
0.53
26170
Toys
29.862
26.520
15344
14316
0.51
28606
0.68
Pepper
30.712
27.758
15803
13248
26284
GoldHill
28.803
26.694
15656
9803
0.57
19420
Zelda
32.869
28.826
16115
12012
23754
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. 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.
Miss

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

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

46. GoldHill