1 high-capacity data hiding in halftone images using minimal-error bit searching and least-mean...
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1
High-Capacity Data Hidingin Halftone Images
Using Minimal-Error Bit Searching
and Least-Mean Square Filter
Author: Soo-Chang Pei and Jing-Ming GuoSource: IEEE Transactions on Image Processing, Vol. 15, No. 6, pp. 1665-1679, June 2006
Date : 2006.12.26.Reporter: 黃暢卿 蔡篤校
2
Outline Introduction Data Hiding with Minimal-Error Bit
Search (MEBS) Standard MEBS Modified MEBS
Self-Decoding Mode Dot-Diffusion Self-Decoding Mode Color Halftone Image Self-Decoding
Mode Robust Watermarking LMS Halftone Technique
3
Halftoning Methods .Ordered Dithering .Error Diffusion .Dot Diffusion
graylevel
image
LMS(Least Mean Square filter )
Halftone Technique
secr
etha
lfto
neim
age
halftoneimage
MEBSSecretSharing
5
PSNR
1
RobustWatermarking
7
R
G
B
C
M
Y
K
colorimage
R
G
B
C
M
Y
K
SelfDecoding
colorhalftone
image
2
3
4
6
8 1
2
34
5
67
8
4
Introduction halftone image gray level
0¡ã255
transformfrom gray
levelthreshold 128
binary imagevalue 0 or 1
binary imagevalue 0 or 1
transform fromgray level
use human visualtechnique
1
5
Introduction halftoning methods Ordered Dithering 1
Ordered ditheringalgorithm Bayer 1973.
gray halftoneDefining a n¡Ñn dither matrix D
For a given pixel p(x,y)
i = x mod n
j = y mod n
if intensity of pixel p(x,y) >Dij
output dot is on
else output dot is off
End
Dij is the (I, j) entry of
dither matrix D.
gray level binary
2
6
Introduction halftoning methods Ordered Dithering 2
2
Ordered ditheringalgorithm Bayer 1973.
gray halftone
binary
8 8 15
8 8 15
8 14 8
...
...
...15 ...
8
8
7
... ... ... ......
7 6 6
0 8 2
12 4 14
3 11 1
15
10
6
9
7 13 5
0
1
0
1
1
1
11
1
0
0
0
0
0
Ordered dither
4 ¡Ñ 4 matrix
1 1
7
Introduction halftoning methods Error Diffusion 1
Standard EDFflow chart
gray halftone
For i :=1 to m do
For j :=1 to n do
if A[i,j] <1/2 B [i,j] :=0
else B [i,j] :=1;
err := A[i,j] - B [i,j];
A[i,j+1] :=A[i,j+1] + £\ err ;
A[i+1,j-1] :=A[i+1,j-1] + £] err;
A[i+1,j] :=A[i+1,j] + £ ̂err;
A[i+1,j+1] :=A[i+1,j+1] + £_ err;
End
gray level binary
3
8
Introduction halftoning methods Error Diffusion 2
3
116
halftonegray
EDF kernelFloyd & Steinberg (1975)
C 7
3 5 1
116
Jarvis, Judice & Ninke (1976)
C
3 5 71
48
7
5
1 3 5 3
5
3
1
Stucki (1981)
C
2 4 81
42
8
4
1 2 4 2
4
2
1
graylevel
binary
SmoothingSpatialFilters
1
2
2
4
1 2
1
2
1
9
Introduction halftoning methods Error Diffusion 3
3
Threshold128
halftone bit ¡× 1error¡×160-255¡× -95
160 110 120
120 90 100
140 100 100
...
...
...
... ... ... ...
255error ¡×160-255¡× -95
68 ¡×110+
7/16 * -95
Floyd & Steinberg (1975)
C 7
3 5 1
116
90 ¡×120+
5/16 * -95
84 ¡×90+
1/16 * -95
pixel160
left --> right top --> down
255 68 120
90 84 100
140 100 100
...
...
...
... ... ... ...
ERR-95
10
Introduction halftoning methods Error Diffusion 4
3
255 68 120
90 84 100
140 100 100
...
...
...
... ... ... ...
Threshold128
halftone bit ¡× 0error¡×68-0¡× 68
Floyd & Steinberg (1975)
C 7
3 5 1
116
pixel68
0error ¡×
68-0¡× 68
150 ¡×120+
7/16 * 68
103 ¡×90+
3/16 * 68
105 ¡×84+
5/16 * 68
left --> right top --> down
104 ¡×100+
1/16 * 68
255 0 150
103 105 104
140 100 100
...
...
...
... ... ... ...
ERR68
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Introduction halftoning methods Dot Diffusion 1
Dot diffusionalgorithm Knuth 1987.
gray halftoneExample: divided into 64 classes according i mod 8, j mod 8.
For k ¡×0 to 63 do
for all (i,j) of class k do
if A[i,j]¡Õ1/2 B [i,j]¡×0 else B [i,j]¡×1;
err ¡×A[i,j] - B [i,j]; w ¡×0;
for all neighbors (u,v) of (i,j) do
if class(u,v)¡Ök w¡×w+weight(u-i, v-j);
if w¡Ö0 for all neighbors (u,v) of (i,j) do
A[u,v]¡×A[u,v] + err ¡Ñ weight(u-i,v-j) / w
End
4
12
Introduction halftoning methods Dot Diffusion 2
Dot diffusionalgorithm Knuth 1987.
gray halftone
255 0 60
255 84 70
90 95 80
...
...
...... ... ... ...
... ... ... ...............
...164 208 218
206 215 235
216 236 241
...
...
...... ... ... ...
... ... ... ...............
...
1 2 1
2 W 2
1 2 1
classes (16¡Ñ16)
weight
255 0 72
255 084<128
94
102 119 92
...
...
...... ... ... ...
... ... ... ...............
...
err¡×84-0¡× 8460¡Ï1/7¡Ñ84¡× 7270¡Ï2/7¡Ñ84¡× 9490¡Ï1/7¡Ñ84¡× 10295¡Ï2/7¡Ñ84¡× 11980¡Ï1/7¡Ñ84¡× 92
4
13
secret sharing
secret halftoneimage
Error Diffusionand Data Hiding
gray level image halftone image
5
14
MEBS StandardData Hiding with Minimal-Error Bit Search
5
15
MEBS StandardData Hiding with Minimal-Error Bit Search
5
Secret bit
Gray Code
1
0
0
0
0
1
1
1
1
0
1
0
"0" group"1" group
160 .........
108 ...... ...
Lenna Baboon
0 1
... ...
Secret
255 ... 0 ...1 0
OK.
threshold 128
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MEBS StandardData Hiding with Minimal-Error Bit Search
5
Secret bit
Gray Code
1
0
0
0
0
1
1
1
1
0
1
0
"0"group
"1"group
255
......
0
... ...
Lenna Baboon
0 1
... ...
Secret
255 0
1 0
110150
cost¡×|150-0|¡Ï|110-0| ¡×260
cost¡×|150-255|¡Ï|110-255| ¡×250
0 0if select
1 1if select
Select
255
NG
ERR-105
ERR-145
17
MEBS StandardData Hiding with Minimal-Error Bit Search
5
18
MEBS ModifiedData Hiding with Minimal-Error Bit Search
5
19
MEBS ModifiedData Hiding with Minimal-Error Bit Search
5
Secret bit
Gray Code
1
0
0
0
0
1
1
1
1
0
1
0
"0"group
"1"group
255
......
0
... ...
Lenna Baboon
0 1
... ...
Secret
255 0
1 0
110150
cost¡×|150-0|¡Ï|110-0| ¡× 135 ¡Ï 110 ¡× 245cost¡×|150-255|¡Ï|110-255| ¡× 105 ¡Ï 135 ¡× 240
Let Max ERR¡×135
0 0if select
1 1if select
Select
ERR-105
NG
ERR-135
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MEBS ModifiedData Hiding with Minimal-Error Bit Search
5
average of Optimal ERRth¡× 135.52
21
Self-Decoding ModeDot-Diffusion Self-Decoding Mode
6
Secret 240¡Ñ480halftone image.
Embedded im
age
grayOrig
inal
480
¡Ñ48
0gr
ay le
vel i
mag
e.
Orig
inal
480
¡Ñ48
0do
t-diff
used
imag
e.
PSNR25.39 dB
22
Self-Decoding ModeColor Halftone Image Self-Decoding Mode
6
R G
BPS
NR
29.
56 d
B
PSN
R 3
0.92
dB
C M
Y K
23
Robust Watermarking7
512 ¡Ñ 512 64¡Ñ64
embed same secret512/64 ¡Ñ 512/64 times
decoding rate 92.65%.
Rotated 45 0
decoding rate 89.92%.Cropping 40%
decoding rate 90.41%.
Croppingattack
Rotationattack
Tampleringattack
24
LMS Halftone Technique(Least Mean Square filter)
8
Training LMS filter w m,nconvergent speed ¡× iteration times £g : 10 -4 ; 10 -5 ; 10 -6
size : 3¡Ñ3 ; 5¡Ñ5 ; 7¡Ñ7 ; 9¡Ñ9
gray
OrderedDithering
Error Diffusion
Dot Diffusion
to overcome watermarked image become more blurred
25
LMS Halftone Technique PSNR (Peak Signal-to-Noise Ratio)
8
gray
160 110 129
120 90 100
140 100 100
...
...
...... ... ... ...
halftone
1 0 1
0 0 0
1 0 0
...
...
...... ... ... ...
P
i
Q
j Rnm njminmji bwx
QP
1 1
2
, ,,, )(PSNR
.6275 .4314 .5020
.4706 .3529 .3922
.5490 .3922 .3922
normalized
.3529 ¡Ð ( .080¡Ñ1 ¡Ï .099¡Ñ0 ¡Ï .062¡Ñ1 ¡Ï .096¡Ñ0 ¡Ï .122¡Ñ0 ¡Ï .080¡Ñ0 ¡Ï .052¡Ñ1 ¡Ï .071¡Ñ0 ¡Ï .046¡Ñ0 )
.080 .099 .062
.096 .122 .080
.052 .071 .046
LMS filter w 3,3
.080 .099 .062
.096 .122 .080
.052 .071 .046
1 0 1
0 0 0
1 0 0
3,3 1,1,2,2
3 ,2 ,2 .
nmnm bwx
nmjiEx
region
Rnm njminmjiji bwxe
, ,,,,
P
Qn
m
26
LMS Halftone Technique(Least Mean Square filter)
8
2,,
2,
,,, ,,,,
)(
.
jijiji
jijiRnm njminmjiji
gge
gghwgets
njmijinm
ji hew
e
,,,
2, 2
decreased then 0 &
decreased then 0 &
,,,
,,,
slopwwif
slopwwif
optnmnm
optnmnm njmijik
nmk
nm heww ,,,
)1(,
if no change then stop
sharpening spatial Filters
Lennaerr-diffusion
halftone image
Training LMS filter w m,n £g : 10 -4 ; 10 -5 ; 10 -6
size : 3¡Ñ3 ; 5¡Ñ5 ; 7¡Ñ7 ; 9¡Ñ9
Lennagray
image
jie ,
jig ,
jig ,
optnmw ,,
27
LMS Halftone Technique(Least Mean Square filter)
8
LMS halftone technique
Flow chart
gray halftoneTRAINING RESULTS OFLMS FILTERS
Error DiffusionOrdered Dithering Dot Diffusion
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LMS Halftone Technique(Least Mean Square filter)
8
Iteration number
Chan
ges
251 251
7¡Ñ7Adaptive filter of size Adaptive filter of size 9¡Ñ9
5 5 Iteration number
£g¡×10 -¢´ £g¡×10 -¢µ £g¡×10 -¢´ £g¡×10 -¢µ
£g¡×10 -¢µ
£g¡×10-¢´
Chan
ges
29
LMS Halftone Technique(Least Mean Square filter)
8
. sharpness
. lower PSNR
LMS base with MEBS
PSNR¡×25.06
PSNR¡×26.36
MEBS ERR th ¡×135
PSNR¡×26.95
PSNR¡×26.78
LMS+
MEBSMEBS
30
THANKS