Watermarking
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Transcript of Watermarking
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.
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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
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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: