1. A Self-Reference Watermarking Scheme Based on Wet Paper Coding
Speaker: Wang Xu
Date: /03/21

2. Outline Introduction Related works Self-reference watermarking scheme
Experimental results
Conclusions

3. Introduction (1/2) Fragile watermarking technique
Protect the integrity of image content
Detect and locate the tampered areas
(a) Original image
(b) Tampered image
(c) Detected image

4. Introduction (2/2) Detect and locate the tampered areas
Restore the tampered areas
(b) Tampered image
(c) Detected image
(c) Restored image

5. Related Works — VQ Compression
(16, 200, …, 90) 1
(35, 22, …, 100) 2
(40, 255, …, 59) .
254
(90, 102, …, 98)
255
(145, 16, …, 99) 1 60 61
175 …
100 95
203 .
. . .
Index table
Original image
Codebook

6. Vector Quantization (VQ) Codebook Training
Codebook generation 1 2 .
N-1
Training images
Training set
Separating all training images to vectors

7. Vector Quantization (VQ) Codebook Training
Codebook generation (Ex: codebook size = 256) 1 . 1 .
254
255
N-1
Initial codebook
Training set
Codebook initiation

8. Vector Quantization (VQ) Codebook Training
LBG algorithm
Training 256 codewords each time
K times
Until the difference between every two times is smaller than the threshold

9. An Example of VQ Compression
To encode an input vector
v = (10, 37, …… , 61, 20)
(1) Compute the distance between v with all vectors in codebook
d(v, cw0) = d(v, cw1) = 86.8
d(v, cw2) = d(v, cw3) = 129.1
d(v, cw4) = d(v, cw5) = 78.9
d(v, cw6) = d(v, cw7) = 98.4
d(v, cw8) = ··· d(v, cw255) = 136.3
(2) So, we choose cw8 to replace the input vector v.
Index
Codewords 3 2
··· 60 18 1 79 28 11 34 4 10 66 23 7 16 88 12 20 5 22 15 6 9 8 17 39 50 19
255 25 75
Codebook

10. An Example of VQ Compression

11. Related Works — Wet Paper Coding
Key 1 1 1
Fridrich, J. Goljan, M., Lisonek, P. and Soukal, D.,  “Writing on Wet Paper,” IEEE Transactions on Signal Processing, vol. 53, no. 10, pp ,   

12. An Example of Wet Paper Coding× = ?
21 :
30 :
Random Matrix
Secret Data
LSB of Cover Image
The important area is marked as wet pixel 21 30 30
20 :
30 :
31 :
Cover Image 20 30 31
Stego-image

13. Self-reference watermarking scheme (1/3)
Authentication embedding layer
: wet pixel i
Authentication code :
AC = HASH( ) =
224 96 89
207 94 86 81 80 88 85 84 83 82
215
Original image 13

14. Secret Key LSB1 AC : wet pixel wet paper coding = = ≠ SK·LSB1 = AC
wet paper coding
SK·LSB1 = AC =
SK·LSB1 = ≠
224 96 89
207 94 86 81 80 88 85 84 83 82
215
224 97 89
207 94 86 81 88 85 84 83
214 14

15. Self-reference watermarking scheme (2/3)
Restoration embedding layer
VQ Encoding i 1 2 3
127
255 …
(120,155,…,80)
(100,125,…,150)
(217,135,…,120) 1 16
125 72 98
··· 32 17 65 22 3 4 9 8 12
201
113 54 88
145
119 76
127 43 96 52 73 62 89 r
(49,117,…,25)
Original image
(11,220,…,39)
(72,68,…,113)
Codebook
Index table 15

16. Self-reference watermarking scheme (3/3)
Restoration embedding layer
: wet pixel i 83 87 93 96 86 95 99 84 94
LSB2 r
Original image 1 16
··· 32 17 4 9
Restoration bits:
IDX = 16

17. Secret Key LSB2 IDX : wet pixel wet paper coding = = ≠ SK·LSB2 = IDX
wet paper coding
SK·LSB2 = IDX =
SK·LSB2 = ≠ 81 85 93 96 84 99 98 94 95 83 87 93 96 86 95 99 84 94 17

18. Verification and restoration (1/2)
Verification layer i
Verified authentication code :
AC = HASH( ) =
159
160
155
158 24
150 27
153 26
154 20 22 15 19
Tampered block
Original image 18

19. Non-tampered blcok Tampered block
Secret Key
LSB1 AC
Non-tampered blcok
SK·LSB1 = AC = = ≠
SK·LSB1
Tampered block
SK·LSB1 ≠ AC
159
160
155
158 24
150 27
153 26
154 20 22 15 19 19

20. Verification and restoration (2/2)
Reconstruction layer i 81 85 93 96 84 99 98 94 95
LSB2 r
Original image 20

21. Convert to decimal number:1 2 3
127
255 …
(120,155,…,80)
(100,125,…,150)
Convert to decimal number: 1
(217,135,…,120)
(49,117,…,25)
(11,220,…,39) =
SK·LSB2 =
(72,68,…,113)
Codebook
Repair ○ ✔
Secret Key
LSB2
IDX 21
Original image

22. Experimental Results 22

23. Tampering attack and the detection results (1/3)
For smooth image Airplane
(a) Airplane, PSNR=47.17 dB
(b) Noised image from (a)
(c) Detected result from (a) 23

24. Tampering attack and the detection results (2/3)
For smooth image Lena
(a) Lena, PSNR=47.19 dB
(b) Manipulated image from (a)
(c) Detected result from (a) 24

25. Tampering attack and the detection results (3/3)
For smooth image Pepper
(a) Pepper, PSNR=47.16 dB
(b) Manipulated image from (a)
(c) Detected result from (a) 25

26. Detection and restoration (1/3)
For smooth image Airplane
(a) Enlarged watermarked image Airplane,
PSNR=47.17 dB
(b) Manipulated image with cropping, PSNR=28.64 dB 26
(c) Detection result (marked with black dots)
(d) Restoration result, PSNR=41.35 dB

27. Detection and restoration (2/3)
For smooth image Lena
(a) Enlarged watermarked image Lena, PSNR=47.19 dB
(b) Manipulated image with cropping, PSNR=25.78 dB 27
(c) Detection result (marked with black dots)
(d) Restoration result, PSNR=44.89 dB

28. Detection and restoration (3/3)
For smooth image Pepper
(a) Enlarged watermarked image Lena, PSNR=47.16 dB
(b) Manipulated image with cropping, PSNR=22.46 dB 28
(c) Detection result (marked with black dots)
(d) Restoration result, PSNR=41.96 dB

29. Conclusions Propose a self-reference watermarking approach
Utilize VQ to achieve the reconstruction data with high compression rate
Using wet-paper coding to improve the security
Detect and locate the tampered regions sensitively
Reconstruct the invalid regions with satisfactory quality
Protect the integrity of image content