1. 指導教授： Chang, Chin-Chen (張真誠)
多種影像格式的資訊隠藏 技術之研究 Some Information Hiding Schemes for Various Image Types
指導教授： Chang, Chin-Chen (張真誠)
研究生： Wu, Ming-Ni (吳明霓) 　　
Dept. of Computer Science and Information Engineering, National Chung Cheng University

2. Outline Introduction Spatial Domain
LSB Substitution Method for Gray-Level and Color images
Weight Matrix Hiding Method for Two-Color Images
Compressed Domain
VQ-based Hiding Scheme Using Voronoi Diagram
VQ-based Hiding Scheme Based upon Block Prediction
Reversible Hiding Scheme Using SSP
Conclusions and Future Works

3. Introduction : Information Hiding
secret information
cover image
stego-image + or
010010…

4. Introduction : Spatial Domain
pixel value
embed
cover image
stego-image or
010010…
secret information

5. Introduction : Compressed Domain
embed
de-compress
compress or
010010…
stego-image
secret information
cover image

6. Introduction : Research Scope
Part I : Spatial Domain
Part II: Compressed Domain

7. Part I: Spatial Domain
LSB Substitution Method for Gray-Level and Color images: LSB Substitution Oriented Image Hiding Strategies Using Genetic Algorithm for Gray and Color images (LSB-SOIH)

8. LSB-SOIH
secret information
cover image
stego-image
embed
embed

9. LSB-SOIH : Least Significant Bit (LSB)
stego-image
cover image
secret image
205
151
149
130
201
204
154
135
202
203
156
138
102
159
120
183
143
156 90
170
125
135 6 4
processed secret image 6 4 6 11 8 9 4 7 15 5 10 13

10. LSB-SOIH : Least Significant Bit (LSB)
processed secret image : S
cover image
stego-image
205
151
149
130
201
204
154
135
202
203
156
138
102
159 6 11 8 9 4 7 15 5 10 13
198
0110
198

11. LSB-SOIH: Proposed Method
pixel substitution table
processed secret image: S
after substitution: S’ 3 1 2 3 1 2 1 2 3
Try to embed S’ to host image and get the different PSNR.
If we can find the best PSNR by using one S’ then it is optimal solution.

12. LSB-SOIH : Genetic Algorithm (GA)
1. Initialize the chromosome Pool
2.Evaluation Process
Best Chromosome
3. Selection Process
4. crossover Operator
5. Mutation Operator

13. LSB-SOIH: Proposed Method
Change the embedded location
secret image
cover image

14. LSB-SOIH - Global Strategy
processed secret image: S
block mapping table
after mapping : S’ 3 1 2 1 2 3 1 2 3
Block 0
Block 1
Block 2
Block 3
pixel substitution table
after substitution: S” 3 1 2 2 1 3

15. LSB-SOIH - Local Strategy
processed secret image: S
block mapping table
after mapping: S’ 3 1 2 1 2 3 1 2 3
Block 0
Block 1
Block 2
Block 3
after substitution: S”
pixel substitution table 1 2 3 3 1 2 3 1 2 2 1 3 2 3 1
Block 0
Block 1
Block 2
Block 3

16. LSB-SOIH : Experiments
The results from embedding secret images into the host image Lena
Host image is “Lena”
Secret images
Simple LSB
Wang et al.’s method
Our global method
Our local method
Airplane
Pepper
Barbara

17. LSB-SOIH : Experiments
The embedding results from the secret image Airplane and the host image Lena which were obtained using the four methods:
(a) LSB substitution method
(b) Wang et al.’s method
(c) our global method
(d) our local method

18. Part I: Spatial Domain
Weight Matrix Hiding Method for Two-Color Images: High Quality Perceptual Data Hiding Technique for Two-Color Images (HQPDH)

19. HQPDH
secret information
cover image
stego-image
embed
010010…

20. HQPDH : Embedding Procedure
secret data = K= 1
W = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
r=3 F1 F2 F= 1 F3 F4
F1 = 1
F1 K = 1
IF1 = 1
IF1 K = 1
SUM((F1 K)  W) mod 2r+2
=( ) mod 32
=22
(SUM(IF1 K)  W ) mod 2r+2
=( ) mod 32
= 3
F1 = 1
00100 – 22 =4 – 22 = -18 or +14
00110 – 3 = 6 – 3 = +3 or -29

21. HQPDH : Embedding Procedure
secret data = K= 1
W = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
r=3 F1 F2 F= 1 F3 F4
F2 = 1
F2 K = 1
IF2 = 1
IF2 K = 1
(SUM(F2 K)  W) mod 2r+2
=( ) mod 32 =4
(SUM(IF2 K)  W ) mod 2r+2
=( ) mod 32
= 21
F2 = 1
11100 – 4 =28 – 4 = +24 or -8
11110 – 21 = 30 – 21 = +9 or -23

22. HQPDH : Embedding Procedure
secret data = K= 1
W = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
r=3 F1 F2 F= 1 F3 F4
F3 = 1
F3 K = 1
IF3 = 1
IF3 K = 1
(SUM(F3 K)  W) mod 2r+2
=( ) mod 32
=17
(SUM(IF3 K)  W ) mod 2r+2
=( ) mod 32
= 8
F3 = 1
11000 –17 =24 – 17 = +7 or -25
11010 – 8 = 26 – 8 = +18 or -14

23. HQPDH : Embedding Procedure
secret data = K= 1
W = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
r=3 F1 F2 F= 1 F3 F4
It is a black block, there is no data will be embedded.

24. HQPDH : Extracting Procedure
F1
F2
F= 1
F3
F4 K= 1
W = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
r=3
F1  = 1
F1  K = 1
stego-image
IF1  = 1
IF1  K = 1
SUM((F1  K)  W) mod 2r+1
= mod 32
= 68 mod 32 = 4 =
SUM((IF1  K)  W) mod 2r+1
= mod 32
=53 mod 32 = 21 =
The secrets is 001

25. HQPDH : Extracting Procedure
F1
F2
F= 1
F3
F4 K= 1
W = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
r=3
F2  = 1
F2  K = 1
stego-image
IF2 = 1
IF2  K = 1
SUM((F2  K)  W) mod 2r+1
= mod 32
= 59 mod 32 = 27 =
SUM((IF2  K)  W) mod 2r+1
= mod 32
=62 mod 32 = 30 =
The secrets is 111

26. HQPDH : Extracting Procedure
F1
F2
F= 1
F3
F4 K= 1
W = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
r=3
F3  = 1
F3  K = 1
stego-image
IF3 = 1
IF3  K = 1
SUM((F3  K)  W) mod 2r+1
= mod 32
= 56 mod 32 = 24 =
SUM((IF3  K)  W) mod 2r+1
= mod 32
=65 mod 32 = 1 =
The secrets is 110

27. HQPDH : Extracting Procedure
F1
F2
F= 1
F3
F4 K= 1
W = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
r=3
stego-image
Since it is a black block, there is no data has been embedded here.

28. HQPD : Experiments (a) the cover image of “Mickey”
(b) the stego-image produced by TP method
(c) the stego-image produced by the proposed method

29. HQPD : Experiments (a) the cover image of a scanned text
(b) the stego-image produced by TP method
(c) the stego-image produced by the proposed method

30. HQPDH : Experiments
Comparisons of the average number of changing bit for embedding one bit data.
Test image
Method
m=n=4
m = n = 8
m = n = 16
r=2
r=3
r=4
r=6
Mickey TP
0.56
0.40
0.37
0.36
0.30
0.29
Our method
0.45
0.33
0.28
0.26
0.21
Scanned text
0.38
0.34
0.27
0.23

31. Part II: Compressed Doman
VQ-based Hiding Scheme Using Voronoi Diagram : Hiding Secret Information in VQ Compressed Images Using Voronoi Diagram (HSIVD)

32. compressedstego-image
HSIVD
cover image
compressed image
compressedstego-image
compress
produce
embed
010010…
secret information

33. HSIVD: Vector Quantization (VQ)1 2
(132, 21,…,76) 3
254
255
100
251 98 15 76
123
247 2
Index table
Image
codebook

34. HSIVD: Principle Component Analysis (PCA)
Given a set of points Y1, Y2, …, and YM where every Yi is characterized by a set of variables X1, X2, …, and XN. We want to find a direction D = (d1, d2, …, dN), where such that the variance of points projected onto D is maximized.

35. HSIVD: Voronoi Diagram (VD)p4 p0 z p7 p5 p3 p6 p2 p1

36. HSIVD – Preprocess codebook VD and attribute assign c0 p0 c1 p1 p4 c21 p0 p1 p2 p3 p4 p5 p6 p7
PCA
l dimensions
2 dimensions

37. HSIVD – Compress and Embed
cover image
index table I pk k 1 p0 p1 p2 p3 p4 p5 p6 p7
search
PCA pj zi
xi yi
a secret bit

38. HSIVD – Decompress and Extract
codebook
image c0 c1 c2 c3 c4 c5 c6 c7
index table I 3 1 p0 p1 p2 p3 p4 p5 p6 p7
secret = 1

39. Image quality (PSNR) comparison with different search levels
HSIVD : Experiments
Image quality (PSNR) comparison with different search levels
Searching levels
Boat
Lena
Peppers 1
28.13
29.60
29.14 2
28.67
30.49
29.54 3
28.72
30.67
29.73 4
28.75
30.78
29.81 8
28.77
30.81
29.91 16
28.78
30.83
29.93

40. Proposed scheme with SL=1
HSIVD : Experiments
Image quality (PSNR) comparisons of three schemes with secret bits (SL means searching level)
Images
VQ compressed only
MGLE
DHPCA
Proposed scheme with SL=1
Airplane
32.24
24.46
25.55
29.60
Barbara
25.75
21.53
22.16
25.03
Boat
29.32
22.34
23.61
28.13
Lena
30.71
22.95
24.21
Peppers
30.52
25.28
25.47
29.14
Toys
29.86
23.78
24.61
27.85

41. Embedded results for different hiding schemes
HSIVD : Experiments
Embedded results for different hiding schemes
(a) MGLE; PSNR=22.95
(b)DHPCA; PSNR=24.21
(c) HSIVD; PSNR=29.14

42. Part II: Compressed Doman
VQ-based Hiding Scheme Based upon Block Prediction: Information Hiding Based on Block Prediction (IHSBP)

43. IHSBP : Side Match VQ (SMVQ)

44. IHSBP: Embedding process

45. IHSBP: Embedding Process

46. IHSBP: Extracting Process

47. IHSBP: Experiments
Image quality (PSNR) for different hiding capacity and different cover images with our proposed scheme
Table 6.1 Image quality (PSNR) for different hiding capacity and different cover images with our proposed scheme
Capacity
Lena
Airplane
Boat
Peppers
16K
31.45
30.85
30.52
29.54
32K
31.21
30.60
30.33
28.93
48K
31.07
29.98
30.09
28.25
64K
29.43
28.74
29.18
27.44
80K
27.14
26.63
27.63
27.16

48. IHSBP: Experiments
Image quality (PSNR) comparisons of three schemes with image “Airplane”
Capacity
MGLE
DHPCA
Du and Hsu’s scheme
Shie et al.’s scheme
Proposed scheme
16K
25.26
29.01
30.59
30.79
30.85
32K
26.23
30.40
30.57
30.60
48K
25.12
29.90
30.06
29.98
64K
24.09
28.51
28.39
28.74
80K
21.76
26.38
26.41
26.63

49. IHSBP: Experiments (a) MGLE; PSNR=24.96; capacity=16K
(b) DHPCA; PSNR = 25.64; capacity= 48K
(c) Du and Hsu’s scheme; PSNR=28.16; capacity=48K
(d) Shie et al.’s scheme; PSNR=29.81; capacity=48K
(e) Proposed scheme; PSNR=31.07; capacity=48K

50. Part II: Compressed Doman
Reversible Hiding Scheme Using SSP: A Novel High Capacity Reversible Information Hiding Scheme Based on Side-Match Prediction and Shortest Spanning Path (RIHSSP)

51. RIHSSP: Shortest Spanning Path (SSP)f f b b c c e e d d a f b c e d

52. RIHSSP: Codebook Rearrangement

53. RIHSSP: Embedding Procedure

54. RIHSSP: Extracting Procedure

55. Proposed scheme (Recovered)
RIHSSP: Experiments
Comparison of image quality (PSNR) of four schemes for six images
Images
VQ compressed only
MGLE
Chang and Lu’s scheme
(Recovered)
Proposed scheme (Recovered)
Airplane
32.24
24.46
31.05
Barbara
25.75
21.53
24.47
Boat
29.32
22.34
28.61
Lena
30.71
22.95
29.14
Peppers
30.52
25.28
28.94
Baboon
29.86
23.78
28.21

56. Proposed scheme (4 segments) Proposed scheme (16 segments)
RIHSSP: Experiments
Comparison of hiding capacity of three schemes for six images
Images
MGLE
Chang and Lu’s scheme
Proposed scheme (4 segments)
Proposed scheme (16 segments)
bits
bpp
Airplane
16,384
0.56
14,295
0.29
32,253
0.57
64,516
0.63
Barbara
14,357
0.39
32,258
0.62
0.72
Boat
14,272
0.40
0.58
0.65
Lena
14,851
0.34
0.64
Peppers
14,626
Baboon
12,936
0.46
0.83

57. Conclusions Spatial Domain Compressed Domain
Better image quality and hiding capacity for gray-level and color images
High capability and good perceptual quality for two-color mages
Compressed Domain
Two VQ-based hiding methods provide excellent performance
A Reversible hiding method using SSP technique

58. Future works More image types, such as halftone image
The adaptive hiding method to embed more secret bits to compressed images
Reversible hiding methods for other image types

59. Thank You

60. PCA
Algorithm of PCA
Start by coding the variables Y = (Y1, Y2, …YN) to have zero means and unit variances.
Calculate the covariance matrix C of the samples.
Find the eigenvalues λ1, λ2, …, λN, for C, where λi λi+1, i = 1, 2, …, N-1. Let D1, D2, … DN denote the corresponding eigenvectors.
D1 is the first principal component direction, D2 is the second principal component direction, … , DN is the Nth principal component direction

61. PCA Let A be an n*n covariance matrix.
is an eigenvalue of A, and x is an eigenvector associated with the eigenvalue
x = Ix, where I is an n*n identity matrix
the characteristic polynomial of the matrix

62. PCA
For example, Let A be a 2*2 matrix.

63. PCA Example 1: 40 samples with 2 variables, X1 and X2
Covariance matrix
λ1 =
λ2 =36.780

64. PCA
D1 = [ ]
D2 = [ ]

65. VQ Vector Quantization (VQ)
An image is separated into a set of input vectors
Each input vector is matched with a codeword of the codebook

66. VQ Definition of vector quantization (VQ):
, where Y is a finite subset of Rk.
VQ is composed of the following three parts:
Codebook generation process,
Encoding process, and
Decoding process.

67. Vector Quantization (VQ)1 2
(132, 21,…,76) 3
254
255
100
251 98 15 76
123
247 2
Index table
Image
codebook

68. Separating the image to vectorsVQ
Codebook generation 1 .
N-1 N
Training Images
Training set
Separating the image to vectors

69. VQ : A example
To encode an input vector, for example, v = (150,145,121,130)
(1) Compute the distance between v with all vectors in codebook
d(v, cw1) = d(v, cw2) = d(v, cw3) = 112.3
d(v, cw4) = d(v, cw5) = d(v, cw6) = 235.1
d(v, cw7) = d(v, cw8) = 63.2
(2) So, we choose 8 to replace the input vector v.

70. PSNR

71. YUV
Y=0.299R+0.587G+0.114B
U=B-Y
V=R-Y