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m
a
g
e.
T
h
is
p
ap
er
p
r
o
p
o
s
es
i
m
a
g
e
wate
r
m
ar
k
i
n
g
b
ased
o
n
I
W
T
-
SVD
w
it
h
v
ar
ia
n
ce
p
i
x
els.
T
h
e
e
m
b
ed
d
i
n
g
lo
ca
tio
n
s
ar
e
d
eter
m
i
n
ed
b
ase
d
o
n
t
h
e
v
ar
ian
ce
p
ix
el
s
o
n
ea
ch
b
lo
ck
.
B
lo
ck
s
w
i
th
t
h
e
lo
west
v
ar
ia
n
ce
p
ix
el
s
ar
e
s
elec
ted
f
o
r
e
m
b
ed
d
ed
w
at
er
m
ar
k
.
T
h
e
n
u
m
b
er
o
f
s
elec
t
ed
b
lo
ck
s
ar
e
s
i
m
ilar
to
t
h
e
n
u
m
b
er
o
f
w
ater
m
ar
k
b
its
.
T
h
e
b
in
ar
y
w
ater
m
ar
k
b
its
ar
e
s
cr
a
m
b
led
b
e
f
o
r
e
e
m
b
ed
d
in
g
i
n
o
r
d
er
to
p
r
o
v
id
e
ad
d
itio
n
al
s
ec
u
r
it
y
.
E
ac
h
s
elec
ted
b
lo
ck
is
tr
an
s
f
o
r
m
ed
u
s
in
g
8
×8
I
W
T
,
th
en
L
L
s
u
b
-
b
a
n
d
is
co
m
p
u
ted
u
s
in
g
SVD.
T
h
e
o
r
th
o
g
o
n
al
U
m
atr
i
x
o
f
I
W
T
-
SVD
is
m
o
d
if
ied
u
s
i
n
g
s
o
m
e
r
u
le
s
w
it
h
an
o
p
ti
m
al
th
r
e
s
h
o
ld
.
T
h
e
p
r
o
p
o
s
e
d
w
ater
m
ar
k
i
n
g
s
ch
e
m
e
m
a
y
i
m
p
r
o
v
e
t
h
e
i
m
p
er
ce
p
tib
ilit
y
an
d
r
o
b
u
s
t
n
es
s
p
er
f
o
r
m
a
n
ce
co
m
p
ar
e
to
o
th
er
s
ch
e
m
es.
T
h
e
r
est
o
f
th
i
s
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
.
T
h
e
b
r
ief
p
r
eli
m
i
n
ar
ies
o
f
v
ar
ian
ce
p
ix
els,
A
r
n
o
ld
tr
an
s
f
o
r
m
,
I
W
T
an
d
SVD
ar
e
d
is
cu
s
s
ed
in
Sect
io
n
2
.
T
h
e
p
r
o
p
o
s
ed
em
b
ed
d
in
g
a
n
d
ex
tr
ac
tin
g
w
ater
m
ar
k
ar
e
p
r
esen
ted
in
Sectio
n
3
.
T
h
e
ex
p
er
im
e
n
tal
s
et
u
p
o
f
t
h
e
p
r
o
p
o
s
ed
w
ater
m
ar
k
i
n
g
s
c
h
e
m
e
is
d
i
s
cu
s
s
ed
in
Sectio
n
4
.
Sectio
n
5
p
r
esen
ts
th
e
e
x
p
er
i
m
e
n
tal
r
es
u
lts
.
F
in
al
l
y
,
Sectio
n
6
co
n
clu
d
es t
h
e
co
n
tr
ib
u
tio
n
o
f
th
i
s
p
ap
er
.
2.
P
RE
L
I
M
I
NARIE
S
2
.
1
.
Va
ri
a
nce
i
m
a
g
e
pix
e
ls
T
h
e
v
ar
ian
ce
f
u
n
ctio
n
h
a
s
b
e
en
i
m
p
le
m
e
n
ted
i
n
[
1
2
]
,
it
h
as
b
ee
n
u
s
ed
to
d
eter
m
i
n
e
t
h
e
s
elec
ted
b
lo
ck
s
o
f
e
m
b
ed
d
in
g
w
ater
m
ar
k
.
T
h
e
v
ar
ian
ce
p
ix
els
i
n
d
i
ca
te
th
e
m
o
s
t
co
m
p
le
x
b
lo
ck
s
o
f
th
e
i
m
ag
e.
I
ts
lo
ca
tio
n
s
ar
e
s
u
itab
le
f
o
r
e
m
b
ed
d
in
g
w
ater
m
ar
k
in
o
r
d
er
to
i
m
p
r
o
v
e
th
e
in
v
i
s
ib
ilit
y
o
f
t
h
e
w
ater
m
a
r
k
ed
i
m
a
g
e.
T
h
e
v
ar
ian
ce
p
ix
el
s
ar
e
d
ef
in
ed
b
y
:
1
1
2
2
n
V
Vi
S
n
i
(
1
)
w
h
er
e
n
r
ep
r
esen
ts
th
e
n
u
m
b
er
o
f
i
m
ag
e
p
ix
e
ls
o
n
ea
ch
b
lo
ck
,
Vi
d
en
o
tes
ea
ch
p
ix
el
an
d
V
r
e
p
r
esen
ts
t
h
e
av
er
a
g
e
p
ix
e
l v
al
u
e
o
n
ea
ch
i
m
ag
e
b
lo
ck
.
2
.
2
.
Arno
ld t
ra
ns
f
o
rm
A
r
n
o
ld
tr
an
s
f
o
r
m
is
p
er
f
o
r
m
ed
u
s
i
n
g
m
o
d
u
lo
o
p
er
atio
n
[
1
3
]
.
T
h
is
tech
n
iq
u
e
ch
a
n
g
es
th
e
p
i
x
e
l
p
o
s
itio
n
in
o
r
d
er
to
s
cr
am
b
le
th
e
b
in
ar
y
w
ater
m
ar
k
.
T
h
e
p
e
r
io
d
th
e
s
cr
am
b
le
w
ater
m
ar
k
is
u
s
ed
as
a
s
ec
r
et
k
e
y
.
A
ttac
k
er
w
i
ll
d
if
f
icu
lt
to
id
en
tify
t
h
e
in
f
o
r
m
at
io
n
o
f
th
e
e
m
b
ed
d
ed
w
ater
m
ar
k
ev
en
th
e
attac
k
er
s
u
cc
e
s
s
f
u
l to
ex
tr
ac
t th
e
w
a
ter
m
ar
k
.
A
r
n
o
ld
tr
an
s
f
o
r
m
ca
n
b
e
d
ef
in
ed
b
y
:
N
y
x
y
x
m
o
d
2
1
1
1
'
'
(
2
)
w
h
er
e
'
'
y
x
r
ep
r
esen
ts
t
h
e
v
ec
to
r
p
o
s
itio
n
af
ter
s
h
i
f
ti
n
g
,
y
x
d
en
o
tes
th
e
o
r
ig
i
n
al
v
ec
to
r
p
o
s
itio
n
b
ef
o
r
e
s
h
i
f
ti
n
g
an
d
mo
d
d
en
o
tes
th
e
m
o
d
u
l
u
s
o
p
er
atio
n
af
ter
d
iv
i
s
i
o
n
w
i
th
N
.
T
h
e
in
v
er
s
e
A
r
n
o
l
d
tr
an
s
f
o
r
m
a
tio
n
is
d
ef
in
ed
as:
N
y
x
y
x
m
o
d
'
'
1
1
1
2
(
3
)
2
.
3
.
I
nte
g
er
w
a
v
elet
t
r
a
ns
f
o
r
m
I
n
teg
er
w
a
v
elet
tr
a
n
s
f
o
r
m
u
s
es
a
li
f
ti
n
g
s
c
h
e
m
e
to
p
er
f
o
r
m
t
h
e
in
teg
er
to
i
n
te
g
e
r
w
a
v
elet
tr
an
s
f
o
r
m
[
1
1
]
.
T
h
is
tec
h
n
iq
u
e
is
u
s
ed
to
av
o
id
r
o
u
n
d
in
g
o
f
f
er
r
o
r
s
d
u
r
i
n
g
t
h
e
co
n
v
er
s
io
n
f
r
o
m
i
m
ag
e
p
ix
el
s
to
th
e
tr
an
s
f
o
r
m
ed
co
e
f
f
icie
n
t
s
.
T
h
e
li
f
ti
n
g
s
c
h
e
m
e
m
a
y
p
r
o
d
u
ce
p
er
f
ec
t
r
ec
o
n
s
tr
u
c
tio
n
i
m
ag
e
w
ater
m
ar
k
i
n
g
.
T
h
e
lif
ti
n
g
w
a
v
elet
tr
a
n
s
f
o
r
m
ca
n
b
e
d
o
n
e
i
n
t
h
r
ee
s
ta
g
es
e.
g
.
s
p
lit,
p
r
ed
ict
a
n
d
u
p
d
ate.
T
h
e
b
lo
ck
d
ia
g
r
a
m
o
f
th
e
li
f
ti
n
g
a
n
d
in
v
er
s
e
li
f
ti
n
g
o
p
er
atio
n
is
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
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&
C
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n
g
I
SS
N:
2
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8
8
-
8708
I
ma
g
e
w
a
terma
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kin
g
b
a
s
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n
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teg
er w
a
ve
let
tr
a
n
s
fo
r
m
-
s
in
g
u
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r
va
lu
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co
mp
o
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…
(
F
erd
a
E
r
n
a
w
a
n
)
21
87
Fi
g
u
r
e
1
.
L
i
f
ti
n
g
a
n
d
in
v
er
s
e
li
f
ti
n
g
o
p
er
atio
n
s
First
s
ta
g
e
is
s
p
lit
,
t
h
e
s
i
g
n
al
s
ar
e
d
iv
id
ed
in
to
ev
en
f
e
a
n
d
o
d
d
f
o
v
alu
es
.
T
h
e
n
ex
t
s
tag
e
is
p
r
ed
ict
,
th
e
o
d
d
s
eq
u
en
ce
v
alu
e
s
ar
e
p
r
ed
icted
w
it
h
t
h
e
ev
e
n
s
e
q
u
en
ce
i
n
th
e
p
r
ed
icto
r
.
T
h
i
r
d
s
tag
e
is
u
p
d
ate
o
p
er
atio
n
,
a
n
e
w
e
v
en
v
al
u
es
ar
e
o
b
tain
ed
b
y
m
er
g
i
n
g
t
h
e
p
r
ed
icted
o
d
d
v
alu
e
an
d
o
r
ig
i
n
al
ev
en
v
al
u
e
b
ased
o
n
u
p
d
ater
.
T
h
e
p
r
ed
icted
o
d
d
v
alu
e
r
ep
r
esen
t
s
h
i
g
h
f
r
eq
u
en
c
y
co
ef
f
icien
ts
an
d
ev
e
n
v
al
u
e
as
lo
w
f
r
eq
u
e
n
c
y
co
ef
f
icie
n
t
s
.
T
h
e
in
v
er
s
e
l
if
t
in
g
o
p
er
atio
n
ca
n
b
e
d
o
n
e
b
y
m
e
r
g
e
o
p
er
atio
n
.
2
.
4
.
Sin
g
ula
r
v
a
lue dec
o
m
po
s
it
io
n
B
lo
ck
i
m
ag
e
w
i
th
t
h
e
s
ize
o
f
N
×
N
ca
n
b
e
d
ec
o
m
p
o
s
ed
u
s
in
g
s
i
n
g
u
lar
v
al
u
e
d
ec
o
m
p
o
s
i
tio
n
.
T
h
e
SVD
o
f
an
i
m
a
g
e
A
ca
n
b
e
d
ef
in
ed
b
y
[
1
4
]
:
T
U
S
V
A
(
4
)
w
h
er
e
U
a
n
d
V
r
ep
r
esen
t
th
e
o
r
th
o
g
o
n
al
m
atr
ice
s
a
n
d
S
is
th
e
d
iag
o
n
a
l
m
atr
ix
th
a
t
co
n
tai
n
n
o
n
-
n
e
g
ati
v
e
v
alu
e.
3.
P
RO
P
O
SE
D
SCH
E
M
E
T
h
is
s
ec
tio
n
d
is
c
u
s
s
th
e
p
r
o
p
o
s
ed
w
a
ter
m
ar
k
in
g
s
c
h
e
m
e
b
ased
o
n
I
W
T
-
SVD
w
it
h
v
ar
ia
n
ce
p
i
x
els
.
T
h
e
em
b
ed
in
g
a
n
d
ex
tr
ac
ti
n
g
p
r
o
ce
d
u
r
es a
r
e
d
is
cu
s
s
ed
in
t
h
e
n
ex
t
s
u
b
-
s
ec
tio
n
s
.
3
.
1
.
E
m
bed
din
g
pro
ce
du
re
I
n
th
is
p
r
o
ce
d
u
r
e,
th
e
s
elec
ted
r
eg
io
n
s
ar
e
d
eter
m
in
ed
b
y
lo
w
e
s
t
v
ar
ia
n
ce
p
ix
el
v
al
u
es
o
f
ea
ch
b
lo
ck
.
E
ac
h
s
e
lecte
d
b
lo
ck
is
tr
an
s
f
o
r
m
ed
b
y
I
W
T
-
SVD.
T
h
e
U
3
,
1
an
d
U
4
,
1
co
e
f
f
ic
ien
t
s
o
f
o
r
th
o
g
o
n
al
U
m
atr
i
x
ar
e
m
o
d
i
f
ied
f
o
r
e
m
b
ed
d
in
g
wate
r
m
ar
k
.
T
h
e
w
a
ter
m
ar
k
e
m
b
ed
d
i
n
g
s
c
h
e
m
e
i
s
ill
u
s
tr
ated
in
Fi
g
u
r
e
2
.
T
h
e
p
r
o
p
o
s
ed
em
b
ed
d
in
g
al
g
o
r
ith
m
is
p
r
esen
ted
i
n
A
l
g
o
r
ith
m
1
.
Fig
u
r
e
2
.
B
lo
ck
d
iag
r
a
m
o
f
e
m
b
ed
d
i
n
g
w
ater
m
ar
k
f
f
o
f
e
S
p
l
i
t
+
-
P
r
e
d
i
c
t
U
p
d
a
t
e
Hi
Lo
f”
f
e
f
o
Me
r
g
er
-
+
P
r
e
d
i
c
t
U
p
d
a
t
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
3
,
J
u
n
e
2
0
1
9
:
2
1
8
5
-
2
1
9
5
2188
Alg
o
rit
h
m
1
.
E
m
bed
din
g
Al
g
o
rit
h
m
s
I
np
ut:
Ho
s
t i
m
a
g
e;
w
ater
m
ar
k
;
P
re
-
pro
ce
s
s
ing
:
Step
1
:
An
i
m
ag
e
i
s
d
iv
id
ed
in
to
n
o
n
-
o
v
er
lap
p
in
g
b
lo
ck
s
o
f
8
×8
p
ix
els
Step
2
:
T
h
e
v
ar
ian
ce
o
f
ea
ch
b
lo
ck
is
ca
lcu
lated
,
th
e
n
th
e
b
lo
ck
s
w
it
h
th
e
lo
w
est
v
ar
ian
ce
v
a
lu
e
ar
e
s
elec
ted
f
o
r
e
m
b
ed
d
in
g
w
at
er
m
ar
k
.
T
h
e
s
elec
ted
b
lo
ck
s
ar
e
eq
u
al
to
t
h
e
n
u
m
b
er
o
f
w
ater
m
ar
k
b
it
s
.
St
ep
3
:
Sav
e
x
an
d
y
co
o
r
d
in
ates o
f
ea
ch
s
elec
ted
b
lo
ck
w
it
h
t
h
e
lo
w
est v
ar
ia
n
ce
v
al
u
e.
Step
4
:
T
h
e
b
in
ar
y
w
ater
m
ar
k
i
s
s
cr
a
m
b
led
u
s
i
n
g
A
r
n
o
ld
tr
an
s
f
o
r
m
.
E
m
be
dd
i
ng
w
a
t
er
m
a
r
k
:
Step
5
:
E
ac
h
s
elec
ted
b
lo
ck
is
tr
a
n
s
f
o
r
m
ed
u
s
i
n
g
8
×8
I
W
T
.
T
h
u
s
,
L
L
s
u
b
-
b
an
d
is
t
r
an
s
f
o
r
m
ed
b
y
SVD
to
g
e
n
er
ate
o
r
th
o
g
o
n
al
U
,
S
,
an
d
V
m
atr
ices.
Step
6
:
C
alcu
late
th
e
a
v
er
ag
e
ab
s
o
l
u
te
v
alu
e
o
f
co
m
p
o
n
e
n
t
m
a
tr
ix
U
3
,
1
an
d
U
4
,
1
co
ef
f
icie
n
t:
b
=
(
|
U
3
,
1
|
+
|
U
4
,
1
|
)
/2
(
5
)
E
m
b
ed
ea
ch
b
in
ar
y
w
ater
m
ar
k
b
it b
y
m
o
d
i
f
y
in
g
o
r
th
o
g
o
n
al
U
m
atr
i
x
w
it
h
r
u
les
:
R
u
le
1
: I
f
U
k,
1
is
a
n
e
g
ati
v
e
v
a
lu
e,
s
et
a
=
-
1
a
n
d
µ
=
-
R
else
a
=
1
an
d
µ
=
R
,
f
o
r
k
=
3
,
4
a
n
d
R
r
ep
r
esen
ts
th
e
o
p
ti
m
al
p
ar
am
eter
tr
ad
e
-
o
f
f
b
et
w
ee
n
r
o
b
u
s
t
n
es
s
an
d
i
m
p
er
ce
p
tib
ilit
y
.
T
h
e
o
p
ti
m
al
th
r
es
h
o
ld
R
i
s
d
is
c
u
s
s
ed
i
n
d
etail
as
p
r
esen
ted
i
n
Sectio
n
3
.
3
.
R
u
le
2
: I
f
t
h
e
b
in
ar
y
w
ater
m
ar
k
b
it =
1
,
th
en
u
p
d
ate
U
k,
1
b
y
t
h
e
f
o
r
m
u
la:
U
k,
1
=
a
*
b
+
(
-
1)
k
µ/2
(
6
)
R
u
le
3
: I
f
t
h
e
b
in
ar
y
w
ater
m
ar
k
b
it =
0
,
th
en
u
p
d
ate
U
k,
1
b
y
t
h
e
f
o
r
m
u
la
U
k,
1
=
a
*
b
-
(
-
1)
k
µ/2
(
7
)
w
h
er
e
µ
i
s
a
t
h
r
es
h
o
ld
v
al
u
e
o
f
t
h
e
o
p
ti
m
a
l
p
ar
a
m
eter
R
,
eith
er
i
s
a
n
e
g
ativ
e
o
r
p
o
s
iti
v
e
v
alu
e.
I
f
U
k,
1
is
n
e
g
ati
v
e,
th
e
n
w
e
s
e
t
a
an
d
µ
as n
e
g
ati
v
e
an
d
v
ice
v
er
s
a.
P
o
s
t
-
pro
ce
s
s
ing
:
Step
7
:
T
h
e
m
o
d
if
ied
s
elec
ted
b
lo
ck
s
ar
e
p
e
r
f
o
r
m
ed
b
y
i
n
v
er
s
e
S
VD,
th
en
w
e
i
m
p
le
m
e
n
t
i
n
v
e
r
s
e
I
W
T
.
Step
8
:
C
o
m
b
i
n
e
all
th
e
m
o
d
if
ied
s
ele
cted
b
lo
ck
s
to
g
en
er
ate
t
h
e
w
a
ter
m
ar
k
ed
i
m
ag
e.
O
utput
:
W
ater
m
ar
k
ed
i
m
a
g
e
3
.
2
.
E
x
t
ra
ct
ing
p
ro
ce
du
re
T
h
e
b
lo
ck
d
iag
r
am
of
w
ater
m
ar
k
e
x
tr
ac
tio
n
is
ill
u
s
tr
ated
in
Fi
g
u
r
e
3
.
Step
-
by
-
s
tep
alg
o
r
ith
m
o
f
t
h
e
ex
tr
ac
ted
w
ater
m
ar
k
i
s
d
is
c
u
s
s
ed
in
A
l
g
o
r
ith
m
2
.
T
h
e
b
in
ar
y
w
ater
m
ar
k
i
m
a
g
e
ca
n
b
e
e
x
tr
a
cted
b
y
m
ea
s
u
r
i
n
g
th
e
d
if
f
er
en
t
ab
s
o
lu
te
v
alu
e
o
f
U
3
,
1
an
d
U
4
,
1
co
ef
f
icie
n
ts
th
at
o
b
tain
ed
f
r
o
m
I
W
T
-
S
VD.
T
h
e
ex
tr
ac
ted
w
ater
m
ar
k
i
s
r
ec
o
v
er
ed
b
y
ap
p
ly
in
g
i
n
v
er
s
e
A
r
n
o
ld
tr
an
s
f
o
r
m
.
Fig
u
r
e
3
.
W
ater
m
ar
k
ex
tr
ac
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
I
ma
g
e
w
a
terma
r
kin
g
b
a
s
ed
o
n
in
teg
er w
a
ve
let
tr
a
n
s
fo
r
m
-
s
in
g
u
la
r
va
lu
e
d
e
co
mp
o
s
itio
n
…
(
F
erd
a
E
r
n
a
w
a
n
)
2189
A
lg
o
rit
h
m
2.
E
x
t
ra
ct
io
n Alg
o
rit
h
m
s
In
p
u
t
:
W
a
t
e
r
mark
e
d
i
mag
e
;
x
a
n
d
y
c
o
o
r
d
i
n
a
t
e
s o
f
e
a
c
h
se
l
e
c
t
e
d
b
l
o
c
k
;
Pr
e
-
p
r
o
c
e
ssi
n
g
:
S
t
e
p
1
:
x
a
n
d
y
c
o
o
r
d
i
n
a
t
e
s
a
r
e
u
se
d
t
o
d
e
t
e
r
mi
n
e
t
h
e
e
mb
e
d
d
e
d
r
e
g
i
o
n
s.
T
h
e
se
l
e
c
t
e
d
r
e
g
i
o
n
s
a
r
e
d
i
v
i
d
e
d
i
n
t
o
n
o
n
-
o
v
e
r
l
a
p
p
i
n
g
o
f
8
×
8
p
i
x
e
l
s.
S
t
e
p
2
:
A
p
p
l
y
8
×
8
I
W
T
f
o
r
e
a
c
h
se
l
e
c
t
e
d
b
l
o
c
k
.
S
t
e
p
3
:
A
p
p
l
y
S
V
D
o
n
t
h
e
f
i
r
s
t
l
e
v
e
l
o
f
L
L
su
b
-
b
a
n
d
o
f
I
W
T
c
o
e
f
f
i
c
i
e
n
t
s.
W
a
t
e
r
m
a
r
k
e
x
t
r
a
c
t
i
o
n
:
S
t
e
p
4
:
C
a
l
c
u
l
a
t
e
t
h
e
d
i
f
f
e
r
e
n
t
a
b
so
l
u
t
e
v
a
l
u
e
o
f
U
3,
1
a
n
d
U
4
,
1
c
o
e
f
f
i
c
i
e
n
t
s
f
r
o
m
o
r
t
h
o
g
o
n
a
l
U
ma
t
r
i
x
o
f
I
W
T
-
S
V
D
o
n
e
a
c
h
se
l
e
c
t
e
d
b
l
o
c
k
.
I
f
i
t
s d
i
f
f
e
r
e
n
t
v
a
l
u
e
i
s
g
r
e
a
t
e
r
t
h
a
n
0
,
t
h
e
e
x
t
r
a
c
t
e
d
w
a
t
e
r
mark
b
i
t
=
1
a
n
d
v
i
c
e
v
e
r
sa.
Po
st
-
p
r
o
c
e
s
si
n
g
:
S
t
e
p
5
:
T
h
e
e
x
t
r
a
c
t
e
d
w
a
t
e
r
mark
b
i
t
s a
r
e
c
o
m
p
u
t
e
d
b
y
i
n
v
e
r
se
A
r
n
o
l
d
t
r
a
n
sf
o
r
m t
o
r
e
st
o
r
e
t
h
e
w
a
t
e
r
mark
i
mag
e
.
Ou
t
p
u
t
:
R
e
c
o
v
e
r
e
d
W
a
t
e
r
mark
3
.
3
.
An o
ptim
a
l t
hre
s
ho
ld
A
t
h
r
es
h
o
ld
v
al
u
e
o
r
w
ei
g
h
t
o
f
e
m
b
ed
d
in
g
w
ater
m
ar
k
g
i
v
e
a
s
ig
n
i
f
ica
n
t
e
f
f
ec
t
to
i
m
p
e
r
ce
p
tib
ilit
y
an
d
r
o
b
u
s
t
n
ess
o
f
t
h
e
w
ater
m
a
r
k
in
g
s
ch
e
m
e.
A
lar
g
e
t
h
r
es
h
o
ld
ca
n
ac
h
iev
e
h
i
g
h
r
o
b
u
s
t
n
es
s
,
w
h
ile
at
th
e
s
a
m
e
ti
m
e
it
p
r
o
d
u
ce
s
lar
g
e
d
is
to
r
tio
n
an
d
v
ice
v
er
s
a.
An
o
p
ti
m
al
t
h
r
es
h
o
ld
b
ased
o
n
a
tr
ad
e
-
o
f
f
b
et
w
ee
n
r
o
b
u
s
tn
es
s
a
n
d
i
m
p
e
r
ce
p
tib
ili
t
y
i
s
i
m
p
o
r
ta
n
t
to
g
iv
e
a
b
ala
n
ce
f
o
r
m
ai
n
tai
n
in
g
i
m
a
g
e
q
u
alit
y
a
n
d
r
esis
ta
n
t
ag
ain
s
t
s
e
v
er
al
attac
k
s
.
L
ai
’
s
s
ch
e
m
e
[
9
]
an
d
Ma
k
b
o
l’
s
s
c
h
e
m
e
[
1
0
]
p
r
esen
ted
v
ar
io
u
s
t
h
r
esh
o
ld
s
;
e.
g
.
0
.
0
2
,
0
.
0
1
2
,
an
d
0
.
0
4
.
T
h
ese
th
r
es
h
o
ld
s
ar
e
in
te
n
d
to
p
r
o
d
u
ce
m
o
r
e
r
o
b
u
s
tn
e
s
s
o
r
i
m
p
er
ce
p
tib
ilit
y
b
ased
o
n
th
e
a
m
o
u
n
t
o
f
th
r
es
h
o
ld
v
al
u
e.
T
h
ey
d
id
n
o
t
s
u
f
f
icie
n
tl
y
co
n
s
id
er
th
e
o
p
ti
m
al
t
h
r
es
h
o
ld
v
alu
e
a
s
a
tr
ad
e
-
o
f
f
b
et
w
ee
n
i
m
p
er
ce
p
tib
ilit
y
a
n
d
r
o
b
u
s
tn
es
s
.
T
h
er
ef
o
r
e,
b
ased
o
n
th
ese
i
s
s
u
es,
w
e
p
er
f
o
r
m
e
x
p
er
i
m
e
n
ts
to
f
i
n
d
th
e
o
p
ti
m
al
tr
ad
e
-
o
f
f
b
et
w
ee
n
r
o
b
u
s
tn
es
s
a
n
d
i
m
p
er
ce
p
tib
ilit
y
f
o
r
th
e
L
a
i
'
s
s
ch
e
m
e
[
9
]
,
Ma
k
b
o
l’
s
s
c
h
e
m
e
[
1
0
]
an
d
o
u
r
s
ch
e
m
e
ag
ai
n
s
t
J
P
E
G
co
m
p
r
ess
io
n
.
J
P
E
G
co
m
p
r
ess
io
n
h
a
s
b
ee
n
w
id
el
y
u
s
ed
in
th
e
m
o
s
t
o
f
d
ig
ital
ap
p
licatio
n
s
[
1
5
]
-
[
2
4
]
an
d
it is
a
s
tan
d
ar
d
attac
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m
a
g
es.
O
u
r
s
ch
e
m
e
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h
o
w
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g
r
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ter
r
esis
ta
n
t
u
n
d
er
n
o
is
e
attac
k
s
an
d
s
e
v
er
e
J
P
E
G2
0
0
0
co
m
p
r
ess
io
n
th
a
n
o
th
er
w
ater
m
ar
k
in
g
s
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NO
WL
E
D
G
E
M
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NT
S
T
h
e
au
th
o
r
s
s
i
n
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r
el
y
th
a
n
k
U
n
iv
er
s
iti
Ma
la
y
s
ia
P
ah
a
n
g
,
Ma
la
y
s
ia
f
o
r
s
u
p
p
o
r
tin
g
t
h
is
r
ese
ar
ch
w
o
r
k
th
r
o
u
g
h
UM
P
R
e
s
ea
r
ch
Gr
an
t Sch
e
m
e
(
R
DU1
8
0
3
5
8
)
.
RE
F
E
R
E
NC
E
S
[1
]
M
.
Kh
a
li
li
,
“
DCT
-
A
rn
o
ld
Ch
a
o
t
ic
b
a
se
d
W
a
ter
m
a
rk
in
g
u
sin
g
JPE
G
-
YCb
Cr,
”
Op
ti
k
-
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
f
o
r
L
ig
h
t
a
n
d
El
e
c
tro
n
Op
ti
c
s
,
v
o
l
/i
ss
u
e
:
1
2
6
(
23
)
,
p
p
.
4
3
6
7
-
4
3
7
1
,
2
0
1
5
.
[2
]
L
.
Y.
Hsu
a
n
d
H.
T
.
Hu
,
“
Ro
b
u
s
t
Bli
n
d
Im
a
g
e
Wate
r
m
a
rk
in
g
u
si
n
g
Crissc
ro
ss
In
ter
-
Blo
c
k
P
re
d
ict
io
n
i
n
th
e
DCT
Do
m
a
in
,
”
J
o
u
rn
a
l
o
f
Vi
su
a
l
Co
m
mu
n
ica
t
io
n
a
n
d
Ima
g
e
Re
p
re
se
n
ta
ti
o
n
,
v
o
l
.
4
6
,
p
p
.
3
3
-
4
7
,
2
0
1
7
.
[3
]
S
.
P
.
S
in
g
h
a
n
d
G
.
Bh
a
tn
a
g
a
r,
“
A
Ne
w
Ro
b
u
st
W
a
ter
m
a
rk
in
g
S
y
ste
m
in
In
teg
e
r
DCT
Do
m
a
in
,
”
J
o
u
rn
a
l
o
f
Vi
s
u
a
l
Co
mm
u
n
ica
ti
o
n
a
n
d
Ima
g
e
Rep
re
se
n
ta
ti
o
n
,
v
o
l.
5
3
,
p
p
.
8
6
-
1
0
1
,
2
0
1
8
.
[4
]
F
.
Ern
a
w
a
n
,
e
t
a
l
.
,
“
A
n
I
m
p
ro
v
e
d
Im
p
e
rc
e
p
ti
b
il
it
y
a
n
d
Ro
b
u
stn
e
s
s
o
f
4
x
4
DC
T
-
S
V
D
Im
a
g
e
W
a
ter
m
a
rk
in
g
w
it
h
a
M
o
d
if
ied
E
n
tro
p
y
,
”
J
o
u
rn
a
l
o
f
T
e
lec
o
mm
u
n
ica
ti
o
n
,
El
e
c
tro
n
ic
a
n
d
Co
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l
/i
ss
u
e
:
9
(
2
-
7
)
,
p
p
.
111
-
1
1
6
,
2
0
1
7
.
[5
]
F
.
Er
n
a
w
a
n
,
e
t
a
l
.
,
“
A
Bli
n
d
M
u
l
ti
p
le
W
a
ter
m
a
r
k
s
b
a
se
d
o
n
Hu
m
a
n
Visu
a
l
Ch
a
ra
c
teristics
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
,
v
o
l
/i
ss
u
e
:
8
(
4
)
,
2
0
1
8
.
[6
]
A
.
B
e
n
o
ra
ira,
e
t
a
l
.
,
“
Bli
n
d
Im
a
g
e
W
a
ter
m
a
rk
in
g
T
e
c
h
n
iq
u
e
b
a
se
d
o
n
Dif
fe
re
n
ti
a
l
E
m
b
e
d
d
i
n
g
in
DWT
a
n
d
DCT
Do
m
a
in
s,
”
EURA
S
IP
J
o
u
r
n
a
l
o
n
Ad
v
a
n
c
e
s i
n
S
i
g
n
a
l
Pro
c
e
ss
in
g
,
v
o
l
/i
ss
u
e
:
2
0
1
5
(
1
)
,
p
p
.
5
5
,
2
0
1
5
.
[7
]
I.
A
.
A
n
sa
ri
a
n
d
M
.
P
a
n
t,
“
M
u
lt
i
p
u
r
p
o
se
Im
a
g
e
W
a
ter
m
a
rk
in
g
in
T
h
e
Do
m
a
in
o
f
D
WT
b
a
s
e
d
o
n
S
V
D
a
n
d
A
BC,
”
Pa
tt
e
rn
Rec
o
g
n
it
io
n
L
e
tt
e
rs
,
v
o
l
.
9
4
,
p
p
.
2
2
8
-
2
3
6
,
2
0
1
7
.
[8
]
K.
R.
Ch
e
tan
a
n
d
S.
Nirm
a
la,
“
An
Ef
f
ici
e
n
t
a
n
d
S
e
c
u
re
Ro
b
u
st
W
a
ter
m
a
r
k
in
g
S
c
h
e
m
e
f
o
r
Do
c
u
m
e
n
t
Im
a
g
e
s
u
sin
g
In
teg
e
r
W
a
v
e
lets
a
n
d
Blo
c
k
Co
d
i
n
g
o
f
Bin
a
r
y
Wate
r
m
a
r
k
s,”
J
o
u
rn
a
l
o
f
I
n
f
o
rm
a
ti
o
n
S
e
c
u
rity
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
v
o
l.
24
-
2
5
,
p
p
.
1
3
-
2
4
,
2
0
1
5.
[9
]
C.
C.
L
a
i,
“
A
n
Im
p
ro
v
e
d
S
VD
-
b
a
se
d
W
a
ter
m
a
r
k
in
g
S
c
h
e
m
e
u
sin
g
H
u
m
a
n
V
isu
a
l
Ch
a
ra
c
teristics
,
”
Op
ti
c
s
Co
mm
u
n
ica
ti
o
n
,
v
o
l
/i
ss
u
e
:
2
8
4
(
4
)
,
p
p
.
9
3
8
-
9
4
4
,
2
0
1
1
.
[1
0
]
N.
M
.
M
a
k
b
o
l,
e
t
a
l
.,
“
Blo
c
k
-
b
a
se
d
Disc
re
te
Wav
e
l
e
t
T
r
a
n
sf
o
rm
-
S
in
g
u
lar
V
a
lu
e
De
c
o
m
p
o
siti
o
n
Im
a
g
e
W
a
ter
m
a
rk
in
g
S
c
h
e
m
e
u
sin
g
Hu
m
a
n
V
isu
a
l
S
y
ste
m
Ch
a
ra
c
teristi
c
s,
”
IET
Ima
g
e
Pro
c
e
ss
in
g
,
v
o
l
/i
ss
u
e
:
10
(
1
)
,
p
p
.
34
-
5
2
,
2
0
1
6
.
[1
1
]
I.
A
.
A
n
sa
ri,
e
t
a
l
.
,
“
Ro
b
u
st
a
n
d
F
a
lse
P
o
sit
iv
e
F
re
e
W
a
t
e
r
m
a
r
k
in
g
in
IW
T
d
o
m
a
in
u
sin
g
S
VD
a
n
d
A
BC,
”
En
g
i
n
e
e
rin
g
Ap
p
li
c
a
ti
o
n
s
o
f
Arti
fi
c
ia
l
In
telle
g
e
n
c
e
,
v
o
l
.
4
9
,
p
p
.
1
1
4
-
1
2
5
,
2
0
1
6
.
[1
2
]
M
.
M
o
o
sa
z
a
d
e
h
a
n
d
G
.
Ek
b
a
tan
ifard
,
“
A
n
Im
p
ro
v
e
d
Ro
b
u
st
Im
a
g
e
W
a
ter
m
a
rk
in
g
M
e
th
o
d
u
sin
g
DCT
a
n
d
Yc
o
Cg
-
R
Co
lo
r
S
p
a
c
e
,
”
Op
ti
k
-
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
fo
r
L
ig
h
t
a
n
d
El
e
c
tr
o
n
O
p
ti
c
s
,
v
o
l.
1
4
0
,
p
p
.
9
7
5
-
9
8
8
,
2
0
1
7
.
[1
3
]
P
.
S
in
g
h
,
e
t
a
l
.
,
“
P
h
a
se
ima
g
e
e
n
c
ry
p
ti
o
n
in
t
h
e
f
ra
c
ti
o
n
a
l
Ha
rtl
e
y
d
o
m
a
in
u
sin
g
A
rn
o
ld
tran
sf
o
r
m
a
n
d
sin
g
u
lar
v
a
lu
e
d
e
c
o
m
p
o
siti
o
n
,
”
O
p
ti
c
s a
n
d
L
a
se
rs
in
En
g
in
e
e
rin
g
,
v
o
l.
9
1
,
p
p
.
1
8
7
-
1
9
5
,
2
0
1
7
.
[1
4
]
K.
L
o
u
k
h
a
o
u
k
h
a
,
e
t
a
l
.
,
“
Am
b
i
g
u
it
y
a
tt
a
c
k
s
o
n
ro
b
u
st
b
l
in
d
i
m
a
g
e
w
a
t
e
r
m
a
rk
in
g
s
c
h
e
m
e
b
a
s
e
d
o
n
re
d
u
n
d
a
n
t
d
isc
re
te
w
a
v
e
let
tran
s
f
o
r
m
a
n
d
sin
g
u
lar
v
a
lu
e
d
e
c
o
m
p
o
siti
o
n
,
”
J
o
u
rn
a
l
o
f
El
e
c
trica
l
S
y
ste
ms
a
n
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
/i
ss
u
e
:
4
(
3
)
,
p
p
.
3
5
9
-
3
6
8
,
2
0
1
7
.
[1
5
]
F
.
Ern
a
w
a
n
,
e
t
a
l
.
,
“
Bit
A
ll
o
c
a
ti
o
n
S
trate
g
y
b
a
se
d
o
n
P
sy
c
h
o
v
isu
a
l
T
h
re
sh
o
ld
in
Im
a
g
e
Co
m
p
re
ss
io
n
,
”
M
u
lt
ime
d
ia
T
o
o
ls
a
n
d
A
p
p
li
c
a
ti
o
n
s
,
v
o
l
/i
ss
u
e
:
77
(
11
)
,
p
p
.
1
3
9
2
3
-
1
3
9
4
6
,
2
0
1
8
.
[1
6
]
F
.
Ern
a
w
a
n
,
e
t
a
l
.
,
“
A
n
E
ff
icie
n
t
Im
a
g
e
Co
m
p
re
ss
io
n
Tec
h
n
iq
u
e
u
sin
g
T
c
h
e
b
ich
e
f
Bit
A
ll
o
c
a
ti
o
n
,
”
Op
ti
k
-
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
f
o
r L
i
g
h
t
a
n
d
E
le
c
tro
n
O
p
ti
c
s
,
v
o
l.
1
4
8
,
p
p
.
1
0
6
-
1
1
9
,
2
0
1
7
.
[1
7
]
N.
A
.
A
b
u
,
e
t
a
l
.,
“
A
Ge
n
e
ric
P
s
y
c
h
o
v
isu
a
l
Err
o
r
T
h
re
sh
o
ld
f
o
r
t
h
e
Qu
a
n
ti
z
a
ti
o
n
T
a
b
le
G
e
n
e
ra
ti
o
n
o
n
J
P
EG
Im
a
g
e
Co
m
p
re
ss
io
n
,
”
9
th
In
ter
n
a
ti
o
n
a
l
Co
ll
o
q
u
i
u
m
o
n
S
ig
n
a
l
Pro
c
e
ss
in
g
a
n
d
it
s
Ap
p
li
c
a
ti
o
n
s
,
p
p
.
3
9
-
4
3
,
2
0
1
3
.
[1
8
]
N.
A
.
A
b
u
a
n
d
F
.
Ern
a
w
a
n
,
“
A
No
v
e
l
P
sy
c
h
o
v
isu
a
l
T
h
re
sh
o
ld
o
n
L
a
rg
e
DC
T
f
o
r
I
m
a
g
e
Co
m
p
re
ss
io
n
,
”
T
h
e
S
c
ien
ti
fi
c
W
o
rl
d
J
o
u
rn
a
l
,
v
o
l
/
issu
e
:
2
0
1
5
(
2
0
1
5
)
,
p
p
.
0
0
1
-
0
1
1
,
2
0
1
5
.
[1
9
]
N.
A
.
A
b
u
a
n
d
F
.
Ern
a
w
a
n
,
“
P
s
y
c
h
o
v
isu
a
l
T
h
re
sh
o
ld
o
n
L
a
rg
e
T
c
h
e
b
ich
e
f
M
o
m
e
n
t
f
o
r
Im
a
g
e
Co
m
p
re
ss
io
n
,
”
Ap
p
li
e
d
M
a
t
h
e
ma
ti
c
a
l
S
c
ien
c
e
s
,
v
o
l
/i
ss
u
e
:
8
(
1
4
0
)
,
p
p
.
6
9
5
1
-
6
9
6
1
,
2
0
1
4
.
[2
0
]
F
.
Ern
a
w
a
n
,
e
t
a
l
.
,
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