1. A message-based cocktail watermarking system
Source: Pattern Recognition Vol. 36, 2003, pp
Authors: Gwo-Jong Yuam, Chun-Shien Lub, Hong-Yuan Mark Liaob
Speaker: Yeh Jun-Bin
Date:

2. Outline Wavelet Code generation Cocktail Watermark Inexact matching
Experimental results
Conclusions

3. Flowchart of proposed method

4. Wavelet
1” Wavelet
LL2
HL2
LH2
HH2
2” Wavelet
LL1
HL1
LH1
HH1

5. Wavelet(cont.) Phase 1) Horizontal: Phase 2) Vertical: A B C D E F G HJ K L M N O P
A+B
C+D
A-B
C-D
E+F
G+H
E-F
G-H
I+J
K+L
I-J
K-L
M+N
O+P
M-N
O-P ㄅ ㄆ ㄇ ㄈ ㄉ ㄊ ㄋ ㄌ ㄍ ㄎ ㄏ ㄐ ㄑ ㄒ ㄓ ㄔ
Phase 2) Vertical: ㄅ ㄆ ㄇ ㄈ ㄉ ㄊ ㄋ ㄌ ㄍ ㄎ ㄏ ㄐ ㄑ ㄒ ㄓ ㄔ
ㄅ+ㄉ
ㄆ+ㄊ
ㄇ+ㄋ
ㄈ+ㄌ
ㄍ+ㄑ
ㄎ+ㄒ
ㄏ+ㄓ
ㄐ+ㄔ
ㄅ-ㄉ
ㄆ-ㄊ
ㄇ-ㄋ
ㄈ-ㄌ
ㄍ-ㄑ
ㄎ-ㄒ
ㄏ-ㄓ
ㄐ-ㄔ

6. Wavelet(cont.) Phase 1) Horizontal : Example Phase 2) Vertical :20 15 30 17 16 31 22 18 25 21 19 35 50 5 10 33 53 1 9 42 -3 -8 43 37 -1 35 50 5 10 33 53 1 9 42 -3 -8 43 37 -1
Phase 2) Vertical : 35 50 5 10 33 53 1 9 42 -3 -8 43 37 -1 68
103 6 19 76 79 -4 -7 2 -3 4 1
-10 5 -2 -9
326
-38 6 19 16
-32 2 -7 -3 4 1
-10 5 -2 -9
(Level one is done)
(Level two is done)

7. Flowchart of proposed method

8. Code generation Hadamard code guarantee the maximal hamming distance
Map ASCII code to Hadamard code
Code generate:
H1 = (1)
H2n = Where –Hn is complement of Hn
Ex: H2 = , H4 =

9. Code generation (cont.)
A: B: C: D: E: F: G: H:

10. Flowchart of proposed method

11. Attack types
320
240
DWT 80 20 10 40 15
sharp
compress

12. Cocktail Watermark - Encode
Note:
PM and NM are the form of these formula, but not equal.
For more detail, please read the original paper.
Original image
326+1*1=327
-38+1*1= -37
326
-38 6 19 16
-32 2 -7 -3 4 1
-10 5 -2 -9
Gaussian Distribution P N 1 2 -1 3 -2 -3 4 -4 5 -5 6 -6 7 -7 -8 8
Positive watermark
Negative watermark
mapping
327
-37
Watermarked image

13. Cocktail Watermark – Encode(Cont.)
Original image
Note:
PM and NM are the form of these formula, but not equal.
For more detail, please read the original paper.
326
-38 6 19 16
-32 2 -7 -3 4 1
-10 5 -2 -9
6 - 1*1=5
19+(-1)*1=18
Gaussian Distribution P N 1 2 -1 3 -2 -3 4 -4 5 -5 6 -6 7 -7 -8 8
Positive watermark
Negative watermark
mapping
327
-37 5 18 15
-31 1 -6 3 -4
-11 6 -1
-10
Watermarked image

14. Cocktail Watermark – Decode
Attacked image
Original image
B( ) = 0
B(-37-(-38)) = 1
B(6-3) = 1
B(18-19) = 0
326
-38 6 19 16
-32 2 -7 -3 4 1
-10 5 -2 -9
325
-37 3 18 15
-31 1 -6 -4 5
-11 6 -1
-10
Positive watermark 1
Negative watermark 1 2 -1 3 -2 -3 4 -4 5 -5 6 -6 7 -7 -8 8 1
We (x,y) = B(Wa (x,y) – Wo (x,y)) for positive We (x,y) = B(Wo (x,y) - Wa (x,y)) for negative B(z) = 1 if z >= if z < 0
mapping

15. Flowchart of proposed method

16. Inexact matching
Inexact matching is used to recover the codeword and map the codeword to ASCII code
Back Propagation Neural Network(BPNN) algorithm is used in Inexact matching stage

17. Inexact matching(Cont.)
(Input, output) (右手,跑) (左手,站起來)
當我伸右手,就跑,伸左手就站起來!

18. Inexact matching(Cont.)

19. Inexact matching(Cont.)
Step 1: Training
Fed Hadamard code as input, ASCII code index as output
train the weights between input and hidden, hidden and output.
All Hadamard codes
All ASCII codes index
Neural network

20. Inexact matching(Cont.)
2 training sample: (11, 10), (10, 01)
Step 1 example: 1 3 4 2 5
X1=1
w13
w24
w23
w14
O5=1 6
w35
w46
w45
w36
X2=1
O6=0

21. Inexact matching(Cont.)
Step 2: Inexact matching
Fed output of Cocktail as input (one codeword at a time)
use the weights trained at step1
The output is ASCII code index
A: B: C: D: E: F: G: H:
Neural network

22. Experimental results

23. Conclusions
Hadamard code and cocktail watermark make message type watermark more reliable