palette partition based data hiding for color images yu-chiang li, piyu tsai, chih-hung lin,...
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Palette Partition Based Data Hiding for Color Images
Palette Partition Based Data Hiding for Color Images
Yu-Chiang Li, Piyu Tsai, Chih-Hung Lin, Hsiu-Lien Yeh, and Chien-Ting Huang
Speaker : Yu-Chiang Li
Date : 2009/09/13
2
Outline
Introduction1
Related Work2
Proposed Scheme33
Experimental Results44
Conclusions5
3
Introduction
Image data hiding employs original image to cover a secret message Secret message is imperceptibly inserted into the
original image
4
Introduction (c.)
Good data hiding technologies Imperceptibility High embedding capacity
Trade-off between stego-image quality hiding capacity
5
Introduction (c.)
Three categories of data hiding Spatial domain Frequency domain Quantization domain
6
Introduction (c.)
Spatial Domain Secret message is directly embedded into
spatial domain Least-significant-bits
Cover Image
1 0 1 0
0 1 0 1
1 1 0 0
0 0 1 1
Secret
Messageinserted
103 102 103 104 104 105 108 109
201 201 202 202 204 204 207 207
185 187 188 188 189 191 192 192
194 194 195 197 198 198 199 201
51 56 61 66 71 76 81 86
90 95 100 105 110 115 120 135
161 163 164 168 171 173 174 176
179 181 183 185 186 188 190 192Stego-Image
102 103 103 104 105 106 108 109
200 201 202 203 204 205 206 207
185 187 188 188 189 191 192 192
194 194 195 197 198 198 199 201
51 56 61 66 71 76 81 86
90 95 100 105 110 115 120 135
161 163 164 168 171 173 174 176
179 181 183 185 186 188 190 192
7
Introduction (c.)
103 102 103 104 104 105 108 109
201 201 202 202 204 204 207 207
185 187 188 188 189 191 192 192
194 194 195 197 198 198 199 201
51 56 61 66 71 76 81 86
90 95 100 105 110 115 120 135
161 163 164 168 171 173 174 176
179 181 183 185 186 188 190 192
Stego-Image 1 0 1 0
0 1 0 1
1 1 0 0
0 0 1 1
Secret Message
8
Introduction (c.)
Frequency Domain Secret message is directly embedded into
frequency domain
100 101 102 103 104 105 106 107
200 201 202 203 204 205 206 207
185 186 187 188 189 190 191 192
193 194 195 196 197 198 199 200
50 55 60 65 70 75 80 85
90 95 100 105 110 115 120 135
160 162 164 168 170 172 174 176
178 180 182 184 186 188 190 192
Cover Image
9772 + 0i -68 + 184i -72 + 84i -80 + 36i -84 + 0i -80 - 36i-72 - 84i
-68 - 184i
759 - 763i -33 - 81i -3 - 50i 18 - 39i 32 - 27i 41 - 9i 52 + 13i 72 + 48i
-1486+ 54i 27 + 30i 10 + 22i 1 + 25i -10 + 22i-23 + 14i
-34 + 2i -45 - 21i
-183 - 439i -14 - 15i -8 - 11i -7 - 13i 0 - 15i 12 - 16i 23 - 10i 39 + 11i
-1328 + 0i -10 - 4i 0 - 8i 6 - 8i 8 + 0i 6 + 8i 0 + 8i -10 + 4i
-183 + 439i 39 - 11i 23 + 10i 12 + 16i 0 + 15i -7 + 13i -8 + 11i -14 + 15i
-1486 -554i -45 + 21i -34 - 2i -23 - 14i -10 - 22i 1 - 25i 10 - 22i 27 - 30i
759 + 763i 72 - 48i 52 - 13i 41 + 9i 32 + 27i 18 + 39i -3 + 50i -33 + 81iFrequency Domain (Cover Image)
1 0 1 0
0 1 0 1
1 1 0 0
0 0 1 1
Secret Message
9772 + 0i -68 + 184i -72 + 84i -80 + 36i -84 + 0i -80 - 36i-72 - 84i
-68 - 184i
759 - 763i -33 - 81i -3 - 50i 18 - 39i 32 - 27i 41 - 9i 52 + 13i 72 + 48i
-1486+ 54i 27 + 30i 10 + 22i 1 + 25i -10 + 22i-23 + 14i
-34 + 2i -45 - 21i
-183 - 439i -14 - 15i -8 - 11i -7 - 13i 0 - 15i 13 - 16i 24 - 10i 39 + 11i
-1328 + 0i -10 - 4i 0 - 8i 6 - 8i 9 + 0i 6 + 8i 1 + 8i -10 + 4i
-183 + 439i 39 - 11i 23 + 10i 13 + 16i 1 + 15i -8 + 13i -8 + 11i -14 + 15i
-1486 -554i -45 + 21i -34 - 2i -24 - 14i -10 - 22i 1 - 25i 11 - 22i 27 - 30i
759 + 763i 72 - 48i 52 - 13i 41 + 9i 32 + 27i 18 + 39i -3 + 50i -33 + 81iFrequency Domain (Stego-Image)
100 101 102 103 104 105 106 107
200 201 202 203 204 205 206 207
185 186 187 188 189 190 191 192
193 194 195 196 197 198 199 200
50 55 60 65 70 75 80 85
90 95 100 105 110 115 120 135
160 162 164 168 170 172 174 176
178 180 182 184 186 188 190 192
Stego-Image
9
Introduction (c.)
Quantization Domaint Quantization-based images such as vector
quantization (VQ)
Compress
Codebook
x
x
Uncompress
m
Codebook
x
x
m
12 2
1
10
Related Work
Jo and Kim’s watermarking partitions the codebook into three sub-codebooks to hide watermark
Chang and Wu’s scheme clusters codewords with difference size and performs cycle permutation
Chiang and Tsai’s scheme divides the codebook into several sub-codebooks with codeword overlapping
11
Related Work (c.)
Jo and Kim’s Watermarking Jo and Kim’s technique partitions the
codebook into three sub-codebooks
101- SC,SC,SC
codebook-subother in the codewordsimilar a to
scorrespondcodebook -sub onein codeword
each in which codewords ofconsist SC ,SC 10
watermarkembed tosuitablenot are SC
1 and 0 of value-bit the
represent toconsidered becan SC and SCBoth
1-
10
12
Related Work (c.)
Embed
1-SC 0SC 1SC
Cover Image
0 1
1 0
Watermark Embedded Image
0 1
13
Related Work (c.)
Chiang and Tsai´s Scheme
14
Related Work (c.)
Four-codeword
Three-codeword Three-codeword
Two-codeword
One-codeword
+
Capacity(bit)
2
3
1
0
Sub-cluster
15
Related Work (c.)
Orderin A
Orderin B
Valuein binary
0 0 000 (0)
0 1 001 (1)
0 2 010 (2)
1 0 011 (3)
1 1 100 (4)
1 2 101 (5)
2 0 110 (6)
2 1 111 (7)
2 2 Unuse
Two codewords belongs to 3-member sub-cluster are considered together to embed three secret bits.
16
Proposed Scheme
Color Image Quantization
Palette
Index Table
Palette
Color
Mapping
Original Quantized
17
Proposed Scheme (c.)
Palette Partition
Four-color
Three-color
Two-color
One-color
Capacity(bit)
2
1or 2
1
0
Sub-palette
18
Proposed Scheme (c.)
Palette Partition with Overlapping
Location 0 1 2 3
Four-color
32 15 39 16
32 53 39 47
32 15 31 47
Three-color
146 137 145
146 147 145
Two-color
125 149
131 149
19
Proposed Scheme (c.)
Location 0 1 2 3
Four-color
(00)2 (01)2 (10)2 (11)2
Three-color
(00)2 (1)2 (01)2
Two-color (0)2 (1)2
Lookup table for embed data
20
Proposed Scheme (c.)
Location 0 1 2 3
Four-color
32 15 39 16
32 53 39 47
32 15 31 47
Three-color
146 137 145
146 147 145
Two-color125 149
131 149
Location 0 1 2 3
Four-color (00)2 (01) 2 (10)2 (11)2
Three-color
(00)2 (1) 2 (01)2 NA
Two-color (0)2 (1) 2 NA NA
Quantized Color 146 67 39 149
Sub-palette Size 3 1 4 2
Embedded Color
147(1)2
137(1)2
146(00)2
145(01)2
67
32(00)2
53(01)2
15(01)2
39(10)2
47(11)2
16(11)2
125(0)2
131(0)2
149(1)2
Case
Quantized Color = 146
Secret Message
1110….
Sub-palette Size = 3
147 replace 146
Secret Message “1” Embedded
21
Proposed Scheme (c.)
Extraction Procedure Location 0 1 2 3
Four-color
32 15 39 16
32 53 39 47
32 15 31 47
Three-color
146 137 145
146 147 145
Two-color125 149
131 149
Location 0 1 2 3
Four-color (00)2 (01) 2 (10)2 (11)2
Three-color
(00)2 (1) 2 (01)2 NA
Two-color (0)2 (1) 2 NA NA
147
Index = 147
Secret Message = 1….
22
Experimental Results
RGB color images, “Airplane,” “Lena,” and “Pepper” of 512 × 512 pixels
Airplane Lena Pepper
ImagesSub-Palette size
Four-color Three-color Two-color
Airplane 113(214) 3(9) 2(4)
Lena 146(236) 2(4) 1(2)
Peppers 71(141) 5(12) 22(42)
23
Experimental Results (c.)
Sub-paletteFour-color
Three-color
Two-color
Estimated Capacity
Airplane 255,216 2,487 893 515,478
Lena 251,938 3,056 553 509,532
Peppers 197,045 14,947 26,457 445,508
Performance of the proposed method
Method CQJo & Kim’s Chiang& Tsai’s Proposed
PSNR Capacity Ratio PSNR Capacity Ratio PSNR Capacity Ratio
Airplane 38.66 35.58 207,966 67,521 32.13 512,568 78,494 32.11 515,037 78,632
Lena 36.72 33.52 206,248 64,452 30.08 505,957 76,198 30.11 509,036 77,010
Pepper 33.07 30.67 150,756 62,815 27.28 428,020 73,924 27.31 442,989 76,908
Average 36.15 33.26 188,323 65,164 29.83 482,181 76,294 29.84 489,020 77,499
Comparison: Ratio= Capacity/(CQ-PSNR)
24
Conclusions
An overlapping color palette partition based data hiding with improved data embedding procedure has been presented
Proposed method provides a largest hiding capacity and supports better stego-image quality than Chiang & Tsai’s method
26
Proposed Scheme
Palette Partition Algorithm Squared Euclidean Distance (SED)
x
y
Euclidean Distance
2/1
1
2
2
21
21
),(
.....,,
.....,,
k
ddd
kj
ki
yxyxd
yyyy
xxxx
Image quantization Index1
Index2
27
Related Work (c.)
Chang & Wu’s Scheme Clusters codewords with difference size Performs cycle permutation
Embeded size in a group I = |)(|log2
IG
Index 32 15 39 16 53 47
Embeded data
00 01 10 11 00 01
28
Experimental Results (c.)
Sub-paletteFour-color
Three-color
Two-color
Estimated Capacity
Airplane 255,216 2,487 893 515,478
Lena 251,938 3,056 553 509,532
Peppers 197,045 14,947 26,457 445,508
Performance of the proposed method
Method CQJo & Kim’s Chang & Wu’s Chiang& Tsai’s Proposed
PSNR Capacity PSNR Capacity PSNR Capacity PSNR Capacity
Airplane 38.66 35.58 207,966 32.38 451,629 32.13 512,568 32.11 515,037
Lena 36.72 33.52 206,248 30.55 421,206 30.08 505,957 30.11 509,036
Peppers 33.07 30.67 150,756 27.11 410,582 27.28 428,020 27.31 442,989
Average 36.15 33.26 188,323 30.01 427,804 29.83 482,181 29.84 489,020
Ratio 65,164 696,745 76,294 77,499
Comparison: Ratio=Capacity/(CQ-PSNR)