Chapter 2.ppt
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Chapter 2
資訊隱藏相關技術
資訊隱藏相關技術
取代式系統 (Substitution System)
頻率域 (Frequency Domain) 技術
展頻 (Spread Spectrum) 技術
統計方法
失真技術
隱蔽媒體產生技術
取代式系統 (Substitution System)
同位位元取代法 (Parity Bit Replacement)
與LSB取代法類似
同位位元 (Parity Bit) 取代法
將整張隱蔽影像分成許多不重疊的區塊
每一個區塊用來藏入一個位元
00000101
00001011
00001010
00001101
00010100
000111110
00101001
00110011
00011011
00001010
00010001
00101100
00100101
01010101
00001110
00100011
3 = 1 + 1 + 0 + 1 (奇數)
取代式系統 (Substitution System)
同位位元取代法 (Parity Bit Replacement)
資訊藏量較一般LSB取代法少
偽裝影像破壞程度小
可利用金鑰產生亂數序列來決定要更改區塊中那
一個像素的LSB 資訊安全性
取代式系統 (Substitution System)
LSB匹配 (LSB Matching) 隱藏法
隨機增加或減少像素的值，讓整體的像素維持在
一定的平衡上
以二個像素為單位，每次藏入二個機密訊息
像素A, B 藏入m1 及m2
藏入過程中，使用一個二元函式 (Binary
Function) 用來判斷該如何修改像素
A
F ( A, B) LSB( B)
2
A m1
:
LSB(A) = m1
B m2
：
F(A,B) = m2
H = 110010100111
15
34
20
24
255
255
20
34
0
17
45
0
A=34，B=45，藏入訊息 m1=1和 m2=0
LSB(A) 0 m1 1
33
F(34 - 1,45) LSB 45 1
2
C 34 1 35
D 45
C=35，B=45
m1 LSB C
C
m2 F C , D LSB( D )
2
m1 LSB 35 1
35
m2 F 35,45 LSB( 45 ) 0
2
LSB匹配 (LSB Matching)
5
20
27
37
10
31
10
85
11
40
17
15
12
51
45
34
5
20
11
31
10
41
12
51
27
37
11
85
17
14
46
34
Stego Image
Image
H 1101011110101101
PSNR =
Payload =
52.39
1
for(int i=0;i<imgSize;i++)
{
for(int j=0;j<imgSize;j+=2)
{
arr [i][j] = (int) in.readUnsignedByte();
int A = arr[i][j];
arr[i][j+1] = (int) in.readUnsignedByte();
int B = arr[i][j+1];
int LsbA = returnLSB(A); //呼叫函式取得 A 的LSB
int m1 = (int) (Math.random()*2);
int m2 = (int) (Math.random()*2);
payload += 2;
if (LsbA == m1)
{
int F =returnLSB( ((int)(A/2)) + B);
Stego[i][j] = A;
if (F == m2)
Stego[i][j+1] = B;
else
{
if ( (B + 1) < 256 )
{ Stego[i][j+1] = B +1 ;}
else
{ Stego[i][j+1] = B -1 ;}
mse += ((B - Stego[i][j+1]) * (B - Stego[i][j+1]));
}
}else
{
int F =returnLSB( ((int)((A-1)/2)) + B);
Stego[i][j+1] = B;
if (F == m2)
Stego[i][j] = A - 1;
else
Stego[i][j] = A + 1 ;
mse += ((A - Stego[i][j]) * (A - Stego[i][j]));
}
}
}
}
public static int returnLSB (int num)
{
String s = Integer.toBinaryString(num);
int Lsb = ((int) s.charAt(s.length()-1)) - 48;
return Lsb;
}
模數函式藏匿法
機密資訊每 log 2 2n 1 個位元
形成一個機密符號
一個機密符號藏到 n 個像素中
n4
log 2 2n 1 log 2 2 4 1 3
(101)2 = (5)9
2n 1 9
模數函式藏入法
110 120 130 140
5 11 10 12
20 31 41 51
1
0
0
1
255 255 255 255
Image
H 101011100011100
Stego Image
模數函式藏匿法
m=5
n
F p1 , p 2 , , p n pi i mod 2n 1
i 1
F 110,120,130,140 110 1 120 2 130 3 140 4 mod 2 4 1 4
F p1 , p2 , , pn m
s m F p1 , p2 , , pn mod 2n 1 5 4 mod 9 1
模數函式藏匿法
s m F p1 , p2 , , pn mod 2n 1 5 4 mod 9 1
sn
p s p s 1
p1 p1 1 110 1 111
111
sn
p2 n 1 s p2 n 1 s 1
模數函式藏匿法 – 取出
p1 , p2 , p3 , p4 111, 120, 130, 140
F 111, 120, 130, 140 5
(5)9 = (101)2
模數函式藏匿法
p1 , p2 , p3 , p4 0, 0, 0, 0
m5
p1 , p2 , p3 , p4 0, 0, 0, 1
F 1, 0, 0, 1 5 m
5
20
27
37
10
31
10
85
11
40
17
15
12
51
45
34
Stego Image
Image
H 1101011110101101
PSNR =
Payload =
log 2 2n 1
CP
Capacity
n
log 2 2 4 1
CP
0.75
n
4
# message size
EE
# chanaged cov er
n 個像素會被修改的機率為
2n
2n 1
Embedding Efficiency
log 2 2n 1 2n 1 log 2 2n 1
EE
2n
2n
2n 1
8
7
EE
6
5
4
3
2
0
0.2
0.4
0.6
CP
0.8
1
1.2
奇偶藏入法 (Odd-Even Embedding)
奇偶藏入法
將所有的像素值分為二個不同的子集合 (Subset)，
每個子集合分別利用函式對應到欲藏入的資訊
P = 161
H=0
奇數
偶數
P’ = 158
量化導向隱藏法 (Quantization-based Embedding)
量化導向隱藏法
將像素的值域切割成若干等分
每一等分對應一個機密訊息
量化間距 (Quantization Step Size; q)決定要切割成
多少等分
Quantization-based Information Hiding Scheme
5
6
20
27
37
10 12
11 12
31 40 51
10 17 45
85 15 34
Information
Image
H 1101011110101101
K 1101011000011010
S 0000000110110111
6
20
27
38
10
32
10
85
12
40
17
15
12
51
45
35
Modified Image
0
q 8
1
2
1
0
8
6
11
20
27
38
10
32
10
85
12
40
17
15
3
1
16
12
51
45
35
4
0
24
32
240
1
255
31
32
1
0
248
0
1
6
0
Modified Image
S 0000000110110111
8
16
24
A
6
A 1 1 1
8
q
A 1 q A
1 1 8 6
A A
6
11
2
2
0
q 8
1
2
1
0
8
11 13
10 12
20 32 40
27 10 17
38 85 15
3
1
16
12
51
45
35
Modified Image
S 0000000110110111
4
0
24
32
240
1
255
31
32
1
0
248
0
1
10
0
8
16
24
A
10
A 1 1 2
8
q
A q A
2 8 10
A A
10
13
2
2
Quantization-based Information Hiding Scheme
- Extraction Process
11
26
33
43
13
40
13
86
14
44
20
19
14
53
50
37
Stego Image
PSNR = 34.91
Payload = 1 bpp
0
q 8
1
2
1
0
8
S
3
1
16
255
4
31
32
0
1
0
24
32
240
248
0000000110110111
K 1101011000011010
H 1101011110101101
0
1
2
1
0
8
11
13
6 10
26 40
33 13
43 86
3
1
16
4
7
0
1
24
48
255
31
32
1
0
56
248
S 0000000110110111
14 14
44 51
53
20 50
19 37
if si 0
2 A A q ,
A
2 A A q 1, otherwise.
Stego Image
11
A 2 A A q 2 11 1 8 6
8
*
13
A 2 13 1 8 10
8
*
53
A 2 53 1 8 1 51
8
*
Quantization-based Information Hiding Scheme
-Repairing Process
11
26
33
43
13
40
13
86
14
44
20
19
14
53
50
37
Stego Image
6
20
27
38
10
32
10
85
12
40
17
15
12
51
45
35
Repaired Image
PSNR = 34.91
PSNR = 53.18
Payload = 1 bpp
Payload = 1 bpp
Quantization-based Information Hiding Scheme
-Robustness
11
26
33
43
13
40
13
86
q 8
0 1
2
1
0
8
14
44
20
19
3
1
16
31
32
0
1
0
24
14
53
50
37
Stego Image
S 0000000110110111
255
4
240
32
9
29
33
43
13
40
13
86
248
14
44
18
22
10
53
50
37
Stego Image
S * 0000000110110111
q4
0
6
20
27
38
10
32
10
85
12
40
17
15
12
51
45
35
255
?
Modified Image
S 000000011011011110111000110001
Experimental Results
Modified Images
Stego Images
q4
Experimental Results
q
8
4
PSNR
Lena
Peppers
Airplane
Baboon
Modified Image
51.14
51.13
51.14
51.15
Stego Image
34.79
34.80
34.86
34.81
Repaired Image
51.14
51.13
51.14
51.15
Modified Image
51.15
51.13
51.14
51.12
Stego Image
40.71
40.72
40.74
40.73
Repaired Image
51.15
51.13
51.14
51.12
Payload: 1 bpp
Wu 及 Tsai 像素值差異擴張技術
取出
(75, 105)
D’ = |75 – 105| = 30
30 – 24 = 14
14 = (1110)2
Wu 及 Tsai 像素值差異擴張技術
(100, 200)
H= 10001011000010
?
5
20
27
37
10
31
10
85
11
40
17
15
12
51
45
34
?
Image
H 000000011011011110111000110001
2004 年 Chang 和 Tseng
U L 130 140
135
2
2
D A 135 110 25
m
A D 135 28 107
D
U L
140 130
A
107 28
2
2
n log D log 28 4
n
4
m D 2 28 2 12 1100 2
2004 年 Chang 和 Tseng
150
80
H= 10001011000010
60
?
5
20
27
37
10
31
10
85
11
40
17
15
12
51
45
34
?
Image
H 000000011011011110111000110001
2006 年Chang 等教授
BRL (Hiding Bitmap Files by Run-Length)
GRL (Hiding General Date Files by RunLength)
BRL
利用變動長度編碼法(Run Length Encoding)進
行壓縮，形成 (,) 的配對
A
 k
k
A Â RC 1
RC A mod k 1
RV LSBB
變動長度編碼法(Run Length Encoding)進
行壓縮
1110000011
(3, 1) (5, 0) (2, 1)
每二個像素為一組形成像素配對 (A, B)
RC 藏入 A 中
K = 5 最多 5 個連續的位元
A
 k
k
A Â RC 1
(A, B) = (8, 12)
(RC, RV) = (3, 1)
8
 5 5
5
A 5 3 1 7
B 12 000011002
RV LSB 取代 B 000011012 13
5
20
27
37
10
31
10
85
11
40
17
15
12
51
45
34
?
Image
H 000000011011011110111000110001
(7, 13)
RC A mod k 1 7 mod 5 1 3
RV LSBB 1
GRL
n 1
f s s i C Ni i
i 1
i i 1 i k 1
C
k k 1 1
i
k
s 101001
A
A n 1
n
1
B
B f s C n
C
n
s 101001
藏入的像素配對為 (A, B) = (8, 12)
1的個數
A
8
A n 1
3 6 1
10
n 1
6 1
B
12
6
B f s C n 13 C3 6 13
C
C3
n
A mod n 1
f s B mod C
(10, 13)
n
10 mod (6+1) = 3
6
f
s
C
= 13 mod 3
f
f 1 num , n , f
f
1
num , n 1, ' 0' ,
if num C n 1 ,
1
num C n 1 , n 1, 1 '1' , if num C n 1 and n 1 ,
1
num , n 1, 1'1' ,
if n 1 ,
f
f 1 num , n , f
f
f
f
1
1
1
num , n 1, ' 0' ,
if num C n 1 ,
1
num C n 1 , n 1, 1 '1' , if num C n 1 and n 1 ,
1
num , n 1, 1'1' ,
if n 1 ,
13, 6, 3
13 C
6 1
3
13 C 361 and 6 1 3 ‘1’
, 6 1, 3 1
Information Hiding
影像資訊隱藏技術
空間域 (Spatial Domain)
頻率域 (Frequency Domain)
空間域：LSB、Quantization-based、
Difference Expansion等
頻率域： Discrete Cosine Transform (DCT)、
Wavelet、Fast Fourier Transform
頻率域技術
離散傅立葉轉換 DFT
1
F u
N
e
x
N 1
f x e
j 2ux
N
x 0
cos x sin
1
F u
N
f(x)
f x e
j 2ux
N
x 0
10
1
F 0 10 e
4
1
F 1 10 e
4
F(u)
N 1
20
1200
4
1210
4
100
20 e
20 e
-20+20i
30
1201
4
1211
4
40
40 e
40 e
-20
1203
4
1213
4
-20-20i
頻率域技術
反轉離散傅立葉函式 (Inverse Discrete
Fourier Transform, IDFT)
f x F
1
N 1
u F u e
j 2 ux
N
u 0
F(u)
100
f 0 100 e
1200
4
f 1 100 e
1201
4
-20+20i
20 20i e
20 20i e
1210
4
1211
4
-20
-20-20i
20 20i e
20 20i e
1230
4
1231
4
二維影像的轉換
N 1
1
F u f x e
N x 0
1
F (u, v)
NM
N 1 M 1
j 2 ux
N
ux vy
ux vy
f ( x, y) cos( 2 ( )) j sin( 2 ( ))
N M
N M
x 0 y 0
What is DCT
The discrete cosine transform (DCT)
將原訊號經過一個轉換變成另一種表示法
轉換後的表示法之能量較原始訊號來得集中
而且可以經由逆轉換(reverse transformation)回
復成原始訊號
頻率域技術
離散餘弦轉換 DCT
F (u, v)
(2 x 1)u (2 y 1)v
C (u)C (v) f ( x, y) cos
cos
NM
2N
2M
x 0 y 0
N 1 M 1
2
1
C(u ), C( v) 2
1
,u0
, otherwise
DCT正轉換和IDCT反轉換公式
空間域頻率域
(FDCT)
頻率域空間域
(IDCT)
F( i, j )
N 1 N 1
1
( 2 x 1 )i
( 2 y 1 ) j
C( i )C( j ) f ( x, y )cos
cos
2N
2N
2N
x 0 y 0
f(x,y) 0
1
F( 0,0 )
0
1
8
3
5
4
F(i,j) 0
0
5
1
1
2 0 1 0
2 0 1 0
8 cos
cos
2 2
2 2
1
1
1
5 cos 2 0 1 0 cos 2 1 1 0
2* 2
2
2
2
2
2
2
2 1 1 0
2 0 1 0
3 cos
cos
2
2
2
2
2 1 1 0
2 1 1 0
4 cos
cos
2
2
2
2
=5
F( i, j )
N 1 N 1
1
( 2 x 1 )i
( 2 y 1 ) j
C( i )C( j ) f ( x, y )cos
cos
2N
2N
2N
x 0 y 0
f(x,y) 0
1
F( 0,1 )
0
1
8
3
5
4
0
F(i,j) 0
5
1
0.5
1
2 0 1 0
2 0 1 1
8 cos
cos
2 2
2 2
1
1
1 5 cos 2 0 1 0 cos 2 1 1 1
2* 2
2
2 2
2 2
2 1 1 0
2 0 1 1
3 cos
cos
2 2
2 2
2 1 1 0
2 1 1 1
4 cos
cos
2
2
2
2
= 0.5
F( i, j )
N 1 N 1
1
( 2 x 1 )i
( 2 y 1 ) j
C( i )C( j ) f ( x, y )cos
cos
2N
2N
2N
x 0 y 0
f(x,y) 0
1
0
1
8
3
5
4
1
F( 1,1 )
1 1
2* 2
0
1
F(i,j) 0
5 0.5
1 1.5
1
2 0 1 1
2 0 1 1
8 cos
cos
2 2
2 2
2 0 1 1
2 1 1 1
5 cos
cos
2
2
2
2
2 1 1 1
2 0 1 1
3 cos
cos
2 2
2 2
2 1 1 1
2 1 1 1
4 cos
cos
2
2
2
2
=1
IDCT – 2D
1
f ( x, y ) 2 N
2N
f(x,y) 0
( 2 x 1 )i
( 2 y 1 ) j
C(
i
)C(
j
)D(
i,
j
)cos
cos
2 N
2N
i 0 j 0
N 1 N 1
0
8
1
F(i,j) 0
5 0.5
1 1.5
1
1
2 0 1 0
2 0 1 0
1
1
5 cos
cos
2 2
2 2
2
2
f ( 0,0 ) 2 N
1
2 2
2 0 1 0
2 0 1 1
1
1 0.5 cos
cos
2 2
2 2
2
2 0 1 1
2 0 1 0
1
1
1.5 cos
cos
2 2
2 2
2
2 0 1 1
2 0 1 1
111 cos
cos
2
2
2
2
=8
IDCT – 2D
1
f ( x, y ) 2 N
2N
( 2 x 1 )i
( 2 y 1 ) j
C(
i
)C(
j
)D(
i,
j
)cos
cos
2 N
2N
i 0 j 0
N 1 N 1
0
f(x,y) 0
8
1
5
1
F(i,j) 0
5 0.5
1 1.5
1
2 0 1 0
2 1 1 0
1
1
5 cos
cos
2 2
2 2
2
2
f ( 0,1 ) 2 N
1
2 2
2 0 1 0
2 1 1 1
1
1 0.5 cos
cos
2 2
2 2
2
2 0 1 1
2 1 1 0
1
1
1.5 cos
cos
2 2
2 2
2
2 0 1 1
2 1 1 1
11 1 cos
cos
2
2
2
2
=5
IDCT – 2D
1
f ( x, y ) 2 N
2N
f(x,y) 0
1
( 2 x 1 )i
( 2 y 1 ) j
C(
i
)C(
j
)D(
i,
j
)cos
cos
2 N
2N
i 0 j 0
N 1 N 1
0
1
8
3
5
4
F(i,j) 0
5 0.5
1 1.5
1
頻率域技術
DC
AC1
AC2
AC4
AC5
AC8
AC3
AC9
AC7
AC6
0
1
5
6
14
15
27
28
2
4
7
13
16
26
29
42
3
8
12
17
25
30
41
43
9
11
18
24
31
40
44
53
10
19
23
32
39
45
52
54
20
22
33
38
46
51
55
60
21
34
37
47
50
56
59
61
35
36
48
49
57
58
62
63
3
-1
158 = (10011110)2
2
1
3
2
IDCT
IDCT
158 = (10011110)2
IDCT
q 機密影像量化值
不同量化值下偽裝影像取出的機密影像
q = 10
1999 年 Jpeg-Jsteg
DCT
編碼表
(Code Table)
影像區塊
量化表
量化
編碼
檔頭
Jpeg-Jsteg
壓縮結果
JPEG 檔案
由左而右、由上到下依序藏入
藏入的地方是在非0、1與-1的量化值之中
H = 1011
2002 年Chang 等學者改良Jpeg-Jsteg
Choi 和 Aizawa 1999年
2004年 Wang 和 Pearmain (像素)
Lmean 1
Wang 和 Pearmain
DC
(0,1)
(1,0)
(1,1)
(2,0)
1.13884 ( DC 4 DC 6 )
AC (0,1)
8
1.13884 ( DC 2 DC 8 )
AC (1,0)
8
AC (0,2)
AC (2,0)
0.27881 ( DC 4 DC 6 2 DC 5 )
8
0.27881 ( DC 2 DC8 2 DC 5 )
8
0.16213 ( DC1 DC 9 DC 3 DC 7 )
AC (1,1)
8
(0,2)
(int) 512/3 = 170
170*170 = 28900
(int) 512/8/3 = 21
21*21 = 441
441*5 = 2205
高斯雜訊後的錯誤率
離散小波轉換DWT
98
31
9
143
120
87
131
2
117
5
111
84
132
51
60
111
144
105
87
40
83
85
93
117
138
93
10
17
36
80
67
65
14
93
148
115
148
2
124
90
69
90
59
23
135
45
142
105
106
102
69
91
28
20
4
30
95
139
49
68
93
120
146
22
251
347
390
304
179
-107
114
78
480
154
284
342
84
40
-46
-22
266
345
330
461
-100
69
236
71
442
277
261
202
-40
-41
-19
98
7
-43
24
-38
-45
-161
-48
180
18
100
52
78
-6
54
42
-26
-52
181
-30
-33
-26
43
-165
-134
-58 5136
48 -32
-3 -12
-3 -164
56 230 -3 28
35 86 -150 -72
-422
144
179
-107
114
78
84
40
-46
-22
-100
69
236
71
-36
68
-108
328
-244
-190
-40
-41
-19
98
7
-43
24
-38
-45
-161
-48
180
18
100
52
78
-6
54
42
-26
-52
181
-30
-33
-58
-3
56
-3
-26
43
-165
-134
48
-3
35
-150
Robust Watermarking and
Compression for Medical Images
based on Genetic Algorithms
Authors: Shih, Y. and Wu. Y. T.
Source: Information Sciences, Vol. 175,
No. 3, pp. 200-216 , 2005
ROI (Region of interest)
Lossless
Compression
Lossy
Compression
Computer Tomography (CT) Image
Signature Data
Embedding
Lossless Compression
Lossy Compression
DCT
Transform
1
8
219
51
69
171
81
41
970.5
-12.37
-4.99
94.09
-94.25
82.58
115.99
96.96
94
108
20
121
17
214
15
74
-144.74
30.63
-165.94
22.53
-55.09
-26.76
45.39
-76.5
233
93
197
83
177
215
183
78
-46.77
-28.71
113.62
-40.93
-28.33
-39.12
131.28
-87.92
41
84
118
62
210
71
122
38
-88.67
-60.13
-70.12
-84.05
-38.84
18.38
-54.63
53.37
222
73
197
248
125
226
210
5
-14.75
32.48
-88.16
-27.56
-18
72.99
76.57
-12.66
35
36
127
5
151
2
197
165
-1.06
-37.05
-19.76
-24.91
-41.49
-91.99
-76.61
171.35
196
180
142
52
173
151
243
164
-16.89
-47.45
24.28
-56.94
-0.44
20.51
59.88
133.33
254
62
172
75
21
196
126
224
222.41
79.21
-18.53
92.78
-46.48
123.71
58.15
-18.58
SPIHT
011111100111000000010001101010110….
Signature Data
970.5
-12.37
-4.99
94.09
95.09
-94.25
82.58
115.99
96.96
-144.74
30.63
31.63
-165.94
22.53
-55.09
-26.76
45.39
-76.5
-46.77
-47.77
-28.71
113.62
-40.93
-28.33
-39.12
131.28
-87.92
-88.67
-60.13
-70.12
-84.05
-38.84
18.38
-54.63
53.37
-14.75
32.48
-88.16
-27.56
-18
72.99
76.57
-12.66
-1.06
-37.05
-19.76
-24.91
-41.49
-91.99
-76.61
171.35
-16.89
-47.45
24.28
-56.94
-0.44
20.51
59.88
133.33
222.41
79.21
-18.53
92.78
-46.48
123.71
58.15
-18.58
Embedding
(30)10= (00011110)2
(31)10= (00011111)2
IDCT
1.22
8
218.8
50.7
68.8
170.8
80.6
40.4
94.2
108
19.8
120.8
16.9
213.9
14.7
73.5
233.3
93.1
196.9
83
177.1
215.1
182.9
77.7
41.3
84.1
118
62
210.2
71.3
122.1
37.9
22.2
73
196.9
248
125.2
226.3
210.2
5
35
35.9
126.8
4.9
151.1
23
197.2
165
195.8
179.7
141.6
51.7
173
151.2
243.1
8
219
51
69
171
81
40
256.7
61.6
171.5
74.6
20.9
196.1
126
163.9
1
223.9
94
108
20
121
17
214
15
74
233
93
197
83
177
215
183
78
41
84
118
62
210
71
122
38
22
73
197
248
125
226
210
5
35
36
127
5
151
23
197
165
196
180
142
52
173
151
243
164
257
62
172
75
21
196
126
224
SPIHT
0 1 11…
Signature Data
Round
Extracting
DCT
Transform
1
8
219
51
69
171
81
40
970.3
-42.2
-5.1
94.0
-94.2
82.5
115.9
96.9
94
108
20
121
17
214
15
74
-144.7
30.8
-165.9
22.5
-55.0
-26.7
45.3
-76.5
233
93
197
83
177
215
183
78
-46.9
-28.7
113.6
-40.9
-28.3
-39.1
131.2
-87.9
41
84
118
62
210
71
122
38
-88.6
-60.1
-70.1
-84.0
-38.8
18.3
-54.6
53.3
22
73
197
248
125
226
210
5
-14.7
32.4
-88.1
-27.5
-18.0
72.9
76.5
-12.6
35
36
127
5
151
23
197
165
-1.0
-37.0
-19.7
-24.9
-41.4
-91.9
-76.6
171.3
196
180
142
52
173
151
243
164
-16.89
-47.4
24.2
-56.9
-0.4
20.5
59.8
133.3
253
62
172
75
21
196
126
224
222.4
79.2
-18.5
92.7
-46.4
123.7
58.1
-18.5
Extracted: 1000…
SPIHT
0111…
Signature Data
970.5
-12.37
-4.99
95.09
-94.25
82.58
115.99
96.96
-144.74
31.63
-165.94
22.53
-55.09
-26.76
45.39
-76.5
-47.77
-28.71
113.62
-40.93
-28.33
-39.12
131.28
-87.92
-88.67
-60.13
-70.12
-84.05
-38.84
18.38
-54.63
53.37
-14.75
32.48
-88.16
-27.56
-18
72.99
76.57
-12.66
-1.06
-37.05
-19.76
-24.91
-41.49
-91.99
-76.61
171.35
-16.89
-47.45
24.28
-56.94
-0.44
20.51
59.88
133.33
222.41
79.21
-18.53
92.78
-46.48
123.71
58.15
-18.58
1.22
8
218.8
50.7
68.8
170.8
80.6
40.4
94.2
108
19.8
120.8
16.9
213.9
14.7
73.5
233.3
93.1
196.9
83
177.1
215.1
182.9
77.7
41.3
84.1
118
62
210.2
71.3
122.1
37.9
22.2
73
196.9
248
125.2
226.3
210.2
5
35
35.9
126.8
4.9
151.1
23
197.2
165
195.8
179.7
141.6
51.7
173
151.2
243.1
163.9
253.7
61.6
171.5
74.6
20.9
196.1
126
223.9
Embedding
Frequency domain
IDCT
Spatial domain
Round
1
8
219
51
69
171
81
40
1
8
219
51
69
171
81
40
94
108
20
121
17
214
15
74
94
108
20
121
17
214
15
74
233
93
197
83
177
215
183
78
233
93
197
83
177
215
183
78
41
84
118
62
210
71
122
38
41
84
118
62
210
71
122
38
222
73
197
248
125
226
210
5
222
73
197
248
125
226
210
6
35
36
127
5
151
23
197
165
35
36
127
5
151
23
197
166
196
180
142
52
173
151
243
164
196
180
142
52
173
151
243
164
254
62
172
75
21
196
126
224
254
62
172
75
21
196
126
224
Extracting
DCT
Transform
1
8
219
51
69
171
81
40
970.63
-42.54
-4.83
93.94
-94.13
82.48
116.06
96.92
94
108
20
121
17
214
15
74
-145.05
31.05
-166.34
22.89
-55.39
-26.52
45.23
-76.42
233
93
197
83
177
215
183
78
-47.17
-28.17
113.1
-40.47
-28.72
-38.81
131.07
-87.81
41
84
118
62
210
71
122
38
-88.55
-60.31
-69.95
-84.2
-38.72
18.28
-54.57
53.34
222
73
197
248
125
226
210
6
-14.88
32.65
-88.32
-27.41
-18.13
73.09
76.5
-12.62
35
36
127
5
151
23
197
166
-1.34
-36.66
-20.13
-24.58
-41.77
-91.77
-76.76
171.42
196
180
142
52
173
151
243
164
-16.86
-47.49
24.32
-56.98
-0.42
20.49
59.9
133.32
254
62
172
75
21
196
126
224
222.4
79.22
-18.55
92.79
-46.48
123.71
58.14
-18.58
Extracted: 0111…
SPIHT
0111…
Signature Data
1.22
8
218.8
50.7
68.8
170.8
80.6
40.4
94.2
108
19.8
120.8
16.9
213.9
14.7
73.5
233.3
93.1
196.9
83
177.1
215.1
182.9
77.7
41.3
84.1
118
62
210.2
71.3
122.1
37.9
22.2
73
196.9
248
125.2
226.3
210.2
5
35
35.9
126.8
4.9
151.1
23
197.2
165
195.8
179.7
141.6
51.7
173
151.2
243.1
163.9
253.7
61.6
171.5
74.6
20.9
196.1
126
223.9
Genetic
Algorithm
1
9
219
51
68
171
80
41
969.2
-42.2
-3.8
95.7
-93.8
81.7
116.0
95.9
94
108
19
121
16
213
14
74
-145.9
31.5
-165.8
22.1
-54.8
-26.8
44.7
-76.1
233
93
196
84
178
215
182
77
-47.2
-29.3
113.8
-41.2
-28.8
-39.6
131.2
-87.7
41
85
119
62
210
72
123
37
-87.9
-59.1
-71.0
-84.2
-37.9
19.3
-55.2
53.1
223
73
197
248
125
226
211
5
-14.2
31.7
-88.1
-28.0
-17.5
71.6
75.9
-13.4
36
35
126
5
151
3
197
166
-0.1
-36.1
-20.1
-24.2
-42.4
-91.9
-76.0
170.5
196
179
141
51
174
151
244
164
-17.5
-48.1
24.3
-54.7
-0.8
21.1
59.8
133.1
253
62
171
75
21
196
127
223
223.4
78.8
-18.4
91.6
-46.2
123.5
58.2
-19.6
Pattern
0
1
1
1
0
1
0
1
0
0
0
1
0
0
0
1
0
0
0
1
1
0
0
1
0
1
1
0
0
1
1
0
1
0
1
0
0
0
1
0
1
0
0
1
0
1
0
1
1
0
0
0
1
0
1
1
0
1
0
1
1
0
1
0
Signature: 0111…
Extracted: 1111…
Fitness value: 1
Genetic Algorithm
Patterns
1.22
8
218.8
50.7
68.8
170.8
80.6
40.4
94.2
108
19.8
120.8
16.9
213.9
14.7
73.5
233.3
93.1
196.9
83
177.1
215.1
182.9
77.7
41.3
84.1
118
62
210.2
71.3
122.1
37.9
22.2
73
196.9
248
125.2
226.3
210.2
5
35
35.9
126.8
4.9
151.1
23
197.2
165
195.8
179.7
141.6
51.7
173
151.2
243.1
163.9
253.7
61.6
171.5
74.6
20.9
196.1
126
223.9
0
1
1
1
0
1
0
1
0
1
0
1
0
1
0
1
0
0
0
1
0
0
0
1
0
1
0
1
0
1
1
0
0
0
0
1
1
0
0
1
0
0
1
0
1
0
1
1
0
1
1
0
0
1
1
0
0
1
0
0
1
1
1
0
1
0
1
0
0
0
1
0
0
0
0
1
1
0
1
0
1
0
0
1
0
1
0
1
1
0
1
1
00
11
00
11
1
0
0
0
1
0
1
1
1
1
0
0
11
11
11
11
0
1
0
1
1
0
1
0
0
1
1
0
11
00
00
00
Fitness value: 1
Fitness value: 2
970.5
-12.37
-4.99
94.09
-94.25
82.58
115.99
96.96
-144.74
30.63
-165.94
22.53
-55.09
-26.76
45.39
-76.5
-46.77
-28.71
113.62
-40.93
-28.33
-39.12
131.28
-87.92
-88.67
-60.13
-70.12
-84.05
-38.84
18.38
-54.63
53.37
-14.75
32.48
-88.16
-27.56
-18
72.99
76.57
-12.66
-1.06
-37.05
-19.76
-24.91
-41.49
-91.99
-76.61
171.35
-16.89
-47.45
24.28
-56.94
-0.44
20.51
59.88
133.33
222.41
79.21
-18.53
92.78
-46.48
123.71
58.15
-18.58
(30)10= (00011110)2
(31)10= (00011111)2
Signature Data:
Fragile watermark
(-46)10= (11010010)2
(30)10= (00011110)2
(90)10= (01011010)2
(22)10= (00010110)2
Embedding
Extracting
SPIHT
Wavelet
SPIHT
Wavelet
30
10
15
5
10
18
20
21
15
5
18
15
5
5
10
22
18
20
5
19
30
15
5
19
21
20
25
18
5
10
25
18
18
20
15
21
15
5
20
21
21
20
18
22
5
10
20
22
22
5
10
19
15
5
15
19
20
15
5
18
20
22
25
18
23
8
17
10
8
12
15
22
15
13
10
18
15
7
-4
-7
19
20
15
18
17
12
15
18
20
20
17
22
10
8
20
22
21
10
8
19
18
14
20
19
15
5
-3
-10
5
13
10
-1
20
17
15
17
-1
-3
5
-3
20
19
9
21
-1
-5
3
-2
16
13
16
19
11
-11
4
2
10
-7
9
5
10
7
-8
11
-3
0
-20
1
25
5
-20
1
-3
0
-3
-1
10
-5
0
-1
2
-10
5
1
-5
-17
-10
1
-2 -2
-4
-10 -2
3
15 3 -11
-10 -1
-5
-3 -3 12
-21 -2
4
20 15 12
-21 1 -11
17
17
-5
4
10
-2
-2
-4
15
16
-3
6
-7
-10
-2
3
12
12
-5
-7
9
15
3
-11
17
20
2
2
5
-10
-1
-5
15
-1
-1
11
10
-3
-3
12
7
-3
-5
-11
7
-21
-2
4
-4
5
3
4
-8
20
15
12
-7
-3
-2
2
11
-21
1
-11
SPIHT
17 17 -5
4
10
-2
-2
-4
1
1
0
0
0
0
0
0
15 16 -3
6
-7
-10
-2
3
0
1
0
0
0
0
0
0
12 12 -5
-7
9
15
3
-11
0
0
0
0
0
0
0
0
17 20
2
2
5
-10
-1
-5
1
1
0
0
0
0
0
0
15
-1
-1
11
10
-3
-3
12
0
0
0
0
0
0
0
0
7
-3
-5 -11
7
-21
-2
4
0
0
0
0
0
-1
0
0
-4
5
3
4
-8
20
15
12
0
0
0
0
0
1
0
0
-7
-3
-2
2
11 -21
1
-11
0
0
0
0
0
-1
0
0
TH = 16
Codes: 1 1 0 1
0
10011
10000
0000
1 0 0 0 -1 0 1 0 1 0 -1 0
Experiments
Non-Watermarked
Image PSNR = 38.28
Signature
Watermarked Image
PSNR = 24.08
Extracted
Conclusions
ROI – Lossless compression V.S.
Non-ROI – Lossy compression
Frequency domain watermarking
Fragile watermark
Genetic algorithm