1 High-Capacity Data Hiding in Halftone Images Using Minimal-Error Bit Searching and Least-Mean...

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


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


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


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

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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


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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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


5

18

MEBS ModifiedData Hiding with Minimal-Error Bit Search

5

19


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

20


5

average of Optimal ERRth¡× 135.52

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Self-Decoding ModeDot-Diffusion Self-Decoding Mode

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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

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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

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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

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LMS Halftone Technique(Least Mean Square filter)

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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

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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


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


8

LMS halftone technique

Flow chart

gray halftoneTRAINING RESULTS OFLMS FILTERS

Error DiffusionOrdered Dithering Dot Diffusion

28


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


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

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1 High-Capacity Data Hiding in Halftone Images Using Minimal-Error Bit Searching and Least-Mean...

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