1. Advisor：Prof. Chin-Chen Chang Student ：Kuo-Nan Chen
Hiding Secret Information in Image Compression Code and Image Protection Techniques
藏匿機密資訊於影像壓縮碼及影像保護技術
Advisor：Prof. Chin-Chen Chang
Student ：Kuo-Nan Chen

2. Motivation
Security?
Bandwidth?
Internet
Sender
Receiver

3. A New Data Hiding Strategy with Restricted Region Protection

4. Introduction (1/3) Traditional information hiding
Secret Message:
(Cover Image)
(Stego Image)

5. Introduction (2/3) (cover pixels) 50 60 61 78 90 93
100 95
203
175 30
150
179
188
156 89
(cover pixels)
(binary representation of cover pixels)
Secret Message:
(binary representation of stego pixels)

6. Introduction (3/3) Our proposed scheme Secret Message: 110110110011
Protected Region
(Cover Image)
(Stego Image)

7. Procedure Step 1: Select regions to be protected
Step 2: Generate a location map
Step 3: Compress the location map with Huffman coding
Step 4: Embed information in the cover image

8. Example 1  changeable pixel 0  unchangeable pixel Cover Image
200
189
170
192
143
155
156
177 45 50
140
163 63 60 75 65
200
189
170
192
143
155
156
177 45 50
140
163 63 60 75 65 1
Cover Image
Interesting regions
(Step 1)
Location map
(Step 2)

9. Add an additional token “01” with frequency = 11
(Step 3)
Rules for generating tokens
Type 1: (eight 1s)
Type 2: 1…10 (ending with 0)
Type 3: (eight 0s)
Type 4: 00…00 (ending with 0)
(Location map)
Token
Frequency 2
110
000 1 01
Add an additional token “01” with frequency = 1

10. Condensed location map: 110101000．111
Get the Huffman Code
(Step 3)
root 1
Token
Frequency 2
110
000 1 01 :2 4 1
110:2 2 1
000:1
01:1
Location map:
Condensed location map: ．111

11. Condensed location map (L): 110101000．111
(Step 4) 1
Secret message (S):
Condensed location map (L): ．111
(Location map)
(a) the binary representation of cover image, the
underlined bits are used to embed L
(b) the underlined bits are the result after embedding
L to cover image
(c) the underlined bits are used to embed S
(d) the underlined bits are the result after embedding
S to cover image

12. 200
189
170
192
143
155
156
177 45 50
140
163 63 60 75 65
200
189
170
195
169
143
153
157
177
159 45 49
140
161
203 62 63 73 66 60
191
194
188
Cover Image
Stego Image

13. Experimental Results (1/4)

14. Experimental Results (2/4)

15. Experimental Results (3/4)

16. Experimental Results (4/4)
where h and w are the height and width of the image, respectively; and and are the cover pixel value and stego pixel value, respectively

17. A Modification of VQ Index Table for Data Embedding and Lossless Indices Recovery

18. Introduction
Vector Quantization

20. Vector Quantization (2/2)
Decoding

21. Embedding Procedure

22. Index Types Type 1: Carry 1 secret bit (no side effect)
Type 2: Carry bits with an
indicator added in front of it
(n = codebook size)

23. Example Codebook size = 16 Segment of an index table IT
Index appearance frequency histogram of IT

24. high appearance
frequency indices
Possible Type 1
Indices

25. Indicators
F = R
Codebook size = 16
 Each index size is 4 bits

26. Secret bits =
Secret bits =

27. Extracting and Recovering Procedure

28. Experimental Results (1/4)
Six 512×512 test images

29. Experimental Results (2/4)
The VQ images compressed by using the codebook sized 256

30. Experimental Results (3/4)
The VQ images compressed by using the codebook sized 1024

31. Experimental Results (4/4)
[1]
[1] Z. H. Wang, K. N. Chen, C. C. Chang and M. C. Li, “Hiding information in VQ index tables with reversibility,” Proceedings of the Second International Workshop on Computer Science and Engineering, Qingdao, China, pp. 1-6, October 2009.

32. Thanks for your listening

33. Logistic Map

34. where , and r is a positive number.
Chaotic maps
Logistic map ,
where , and r is a positive number.
One of the simplest chaotic maps
Generate unpredictable results
Guite sensitive to their initial conditions (butterfly effect)

35. logistical map
The horizontal axis shows the values of the parameter r and the vertical axis shows the possible long-term values of X.

36. Hamming Code

37. Hamming Code
R. W. Hamming, “Error detecting and error correcting codes,” Bell system technical journal, vol. 26, no. 2, pp , April 1950.
The most widely used Hamming code is (7, 4). D1 D2 D3 D4 P1  P2 P3
註：data (D1, D2, D3, D4), parity check bits (P1, P2, P3)

38. P1 P2 D1 P3 D2 D3 D4
The normal form of the (7, 4) Hamming code D1 D2 D3 D4 P1 P2 P3
The reorganized form of the (7, 4) Hamming code used in our proposed scheme
(a)
(b)