DIGITAL IMAGE WATERMARKING ALGORITHM USING HUMAN VISUAL

SYSTEN ANANLYSIS IN DWT

PRESENTED BYPRESENTED BY V.SUNDHARARAJ V.SUNDHARARAJ M.EM.E

ASSISTANT PROFESSOR/ECEASSISTANT PROFESSOR/ECEPAAVAI COLLEGE OF ENGINEERING PAAVAI COLLEGE OF ENGINEERING

2

OUTLINE

• Introduction

• DWT (Discrete Wavelet Transformation)

• HVS （ Human Visual System）• Proposed Scheme

• Experimental results

• Conclusions

3

Introduction

• The digital watermarking techniques can be classified into two categories:– Spatial domain

• Less complex

• Not more robust

– Frequency domain• Complex

• More robust

INTRODUCTION Watermark embedding:A digital watermark is a piece of information embedded

into a digital image using Human visual system technique .

WATERMARKING DETECTION:

Information can be recovered from the watermarked image.

EXISTING METHOD

ORIGINAL IMAGE

SECRET IMAGE

EMBEDDING

EMBEDDING

WATERMARKED IMAGE

WATERMARKED IMAGE

Embedding in this context means to add the information directly into the image data in such a way that it is not easily removed.

Less complexNot more robust

PROPOSED METHOD

ComplexMore robust

7

DWT (Discrete Wavelet Transformation)1/2

LL1 HL1

LH1 HH1 Original image

LL2 HL2

LH2 HH2

DWT1-level

DWT2-levels

A B C D A+B C+D A-B C-D

L H

A CB D

L H

A+B C+D

C-DA-B

LL HL

LH HH

8

DWT (Discrete Wavelet Transformation)2/2

DWT2-levels

LH1LH1 HH1HH1

HL1HL1

LL2LL2 HL2HL2

LH2LH2 HH2HH2

9

HVS （ Human Visual System）

• The human eye is less sensitive to noise in– High frequency sub-bands– Brightness is high or low– Textured area and more near the edges

Watermark embedding algorithm

)(i,j)x(m,nαw(i,j)I(i,j)I' θl

θl

θl

Example:

206

50*1.2*0.1200

1112*0.11212 02

02

02

),)x(,(w),(I),(I'

Step 1:

Step 2: IDWT

Watermark detection algorithm

• Watermark images is recovered following the expression,

(i,j)αw

(i,j)(i,j)-II'x'(m,n)

θl

θl

θl

Example:

501.2*0.1

200206

-x'(m,n)

Step 2: IDWT

Step 1:

12

Conclusions

• The proposed scheme is based on HVS(Human Visual system) characteristics.

• The proposed scheme has better performance in terms of robustness.

CONFERENCE[1]. V.sundhararaj , ”image watermarking using human visual

system scheme in wavelet domain”, 2011,GOVERNMENT COLLEGE OF TECHNOLOGY,COIMBATORE.

[2]. V.sundhararaj ,” image watermarking detector using Gauss hermite expansion in wavelet domain human visual system”, 2011,ANNA UNIVERSITY OF TECHNOLOGY,COIMBATORE.

[3]. V.sundhararaj ,”watermarking detector using HVS analysis in wavelet domain human visual system”, 2012,jayalaksmi college Engg &tech.

[4]. V.sundhararaj ,” image fusion detection in satellite image”, 2013,ncret, Gujrat.

REFERENCE

[1]. Yaohui Dai,Chunxian wang, “ Digital watermarking Algorithm based on wavelet transform”, Control, Automation Systems Engineering (case), 2011 International Conference on IEEE.

[2] M. M. Rahman, M. O. Ahmad, and M. N. S. Swamy, “Statistical detector for wavelet-based image watermarking using modified GH PDF,” in Proc. IEEE Int. Symp. Circuits and Systems, Seattle, WA, 2008, pp. 712–715.

[3]. M. Mahbubur Rahman, M. Omair Ahmad, AUGUST 2009 “A New Statistical Detector for DWT-Based Additive Image Watermarking Using the Gauss Hermite Expansion”IEEE TRANSACTIONS ON IMAGE PROCESSING VOL. 18, NO. 8, AUGUST 2009.

15

Watermark embedding algorithm(1/3)

x03 x0

0

x02 x0

1

Image

Watermark

LL HL

LH HH

DWT4-levels

DWT1-levels

Step 1:

Θ=0

Θ=1Θ=2

Θ=3

θlI : The sub-band (θ) at resolution level (l) of image.

16

Watermark embedding algorithm(2/3)

• Find the Weight factors for wavelet- coefficient. (Barni et al.,2001)

(i,j)W θl

)(p*GT θlI

θl S

LL2 HL2

LH2 HH2

0.21 0.1 0.12 1.13

0.25 0.36 0.37 1.38

1.40 1.2 1.3 1.4

1.6 1.7 2.1 2.3Example:

1.21650%ST02 )*(

0.1, 0.12, 0.21, 0.25, 0.36, 0.37, 1.13, 1.2, 1.3, 1.38, 1.4,1.41, 1.6, 1.7, 2.1, 2.3

Step 2:

17

Watermark embedding algorithm(3/3)

)(i,j)x(m,nαw(i,j)I(i,j)I' θl

θl

θl

Example:

206

50*1.2*0.1200

1112*0.11212 02

02

02

),)x(,(w),(I),(I'

Step 3:

Step 4: IDWT

18

Watermark extracting algorithm

• Both the original and the watermark images are needed.

(i,j)αw

(i,j)(i,j)-II'x'(m,n)

θl

θl

θl

Example:

501.2*0.1

200206

-x'(m,n)

Step 2: IDWT

Step 1:

19

Experimental results (1/3)

(a) Lena 512*512

(b) Watermark 64*64

(C) Watermarked Lena PSNR=44.7 dB

20

Experimental results (2/3)

(a) 64 times compressed watermarked Lena

(b) Extracted Watermark

(d) Extracted Watermark

(c) 1.37% remained watermarked Lena after cropping

21

Experimental results (3/3)

(b) Extracted Watermark

(a) Warped watermarked Lena

22

Weight factor (1/4)

63 34 49 10

31 23 14 -13

15 14 3 -12

-9 -7 -14 8

0

0 1

1

0

1

10

LL3 HL3

LH3 HH3

ij

10(0,1)I, 63(0,0)I 03

33

Example:

23

Weight factor (2/4)

0.10.1*1(3,3)

2

),,(),,(),(),(

2.0jiljilljiwl

3 if ,10.0

2 if ,16.0

1 if ,32.0

0 if ,00.1

otherwise ,1

1 if ,2),(

l

l

l

l

l

The human eye is less sensitive to noise in high frequency sub-bands:

Example:

2

(3,0,0)(3,0,0)(3,3)(0,0)W

0.233

24

Weight factor (3/4)

)j

,i

(I

L(l,i,j)Λ(l,i,j)

ll

3333

22256

11

1

otherwise ,

50) if ,1 .L(l,i,j

L(l,i,j)

-L(l,i,j)L'(l,i,j)

The eye is less sensitive to noise in the those areas of the image

where brightness is high or low.Example:

1.75

0.751

0.251 256

641

2

0

2

0

256

11

0031003

0033

),(I

),,L(),,Λ(

25

Weight factor (4/4)

101033

33

3

0

2

0

1

0

21

022

Var2216

1

,y,xll

l

k x ykklkk

jx,

iyI

jx,

iyI

)j,i,l(

717164

228.6875(56)

228.68758)14-12-37-9-141513-1410(49003

2

2

),,(

The is less sensitive to noise in highly texture areas but, The is less sensitive to noise in highly texture areas but, among these, more sensitive near the edges.among these, more sensitive near the edges.

1.3 2

(14.83)1.750.1

2

(717164)1.750.1(0,0)W

0.233

Example: