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I
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Vo
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11
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6
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Dec
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1.
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RO
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UCT
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O
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Fo
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ter
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[
1
]
,
[
2
]
,
its
p
r
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d
u
ct
w
as a
r
ap
id
d
ev
elo
p
m
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t o
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tex
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t
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ch
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n
f
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r
m
atio
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[
3
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,
[
4
]
.
T
h
e
p
r
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b
lem
o
f
p
r
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p
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ty
r
i
g
h
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ter
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a
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n
o
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co
m
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y
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s
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n
g
th
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w
ater
m
ar
k
tech
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al
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d
p
r
ev
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t
u
n
tr
u
s
t
w
o
r
th
y
u
s
e
[
5
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,
[
6
]
.
I
f
th
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w
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m
ar
k
is
co
m
b
in
ed
w
i
th
e
n
cr
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tio
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tec
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o
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ies,
w
e
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ill
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e
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an
ad
d
itio
n
al,
s
tr
o
n
g
er
la
y
er
o
f
p
r
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tectio
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[
7
]
.
T
h
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is
a
lar
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e
n
u
m
b
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o
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k
s
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to
p
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c
o
f
w
ater
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.
A
li
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et
a
l.
[
8
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p
r
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s
ed
b
ased
o
n
Sto
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w
ell
'
s
d
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cr
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T
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ith
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[
9
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in
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co
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A
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I
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8
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8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
11
,
No
.
6
,
Dec
em
b
er
2
0
2
1
:
5
2
5
1
-
5
2
5
8
5252
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s
:
d
es
cr
ip
tio
n
o
f
ad
o
p
ted
m
et
h
o
d
s
is
v
ie
w
ed
i
n
s
ec
tio
n
2
.
A
f
u
ll
ex
p
la
n
atio
n
o
f
o
u
r
m
eth
o
d
in
s
ec
tio
n
3
.
Sectio
n
4
p
r
esen
ts
a
n
d
clar
if
ies
th
e
r
esu
lts
.
Fi
n
all
y
,
co
n
cl
u
s
io
n
s
an
d
s
u
g
g
est
io
n
s
f
o
r
f
u
tu
r
e
d
e
v
elo
p
m
e
n
ts
ar
e
d
o
cu
m
e
n
ted
i
n
s
ec
tio
n
5
.
2
.
T
H
E
P
RO
P
O
SE
D
M
E
T
H
O
D
T
h
is
s
t
u
d
y
ai
m
ed
to
f
in
d
a
n
e
w
r
o
b
u
s
t
an
d
s
ec
u
r
e
m
et
h
o
d
to
p
r
o
tect
im
a
g
es
b
y
"
cr
ea
tin
g
th
e
lo
g
o
"
f
r
o
m
th
e
i
m
a
g
e
to
b
e
p
r
o
tecte
d
"
w
it
h
o
u
t
r
ea
l
h
id
i
n
g
"
it
in
th
e
h
o
s
t,
it
ca
n
b
e
th
o
u
g
h
t
o
f
as
"
v
ir
tu
al
h
id
i
n
g
"
an
d
El
-
Ga
m
al
alg
o
r
it
h
m
is
u
s
ed
to
g
et
m
o
r
e
p
r
o
tectio
n
.
I
n
th
e
n
ex
t
s
u
b
s
ec
tio
n
s
,
w
e
h
i
g
h
l
ig
h
t
th
e
w
ater
m
ar
k
i
n
g
tech
n
iq
u
es
u
s
ed
to
d
a
y
,
in
ad
d
i
tio
n
to
th
e
b
asic step
s
f
o
r
en
co
d
in
g
a
n
d
d
ec
o
d
in
g
u
s
in
g
E
l
-
G
a
m
al.
2
.
1
.
El
-
G
a
m
a
l a
lg
o
rit
h
m
C
r
y
p
to
g
r
ap
h
y
is
a
s
cien
ce
s
ec
u
r
in
g
th
e
co
n
f
id
en
tial
ex
c
h
an
g
e
o
f
in
f
o
r
m
a
tio
n
b
et
w
ee
n
t
w
o
p
eo
p
le
b
y
co
n
v
er
t
i
n
g
t
h
e
i
n
f
o
r
m
atio
n
i
n
t
o
a
f
o
r
m
d
i
f
f
icu
lt
to
u
n
d
er
s
ta
n
d
b
y
at
tack
er
s
[
1
3
]
,
[
1
4
]
.
De
p
en
d
in
g
o
n
th
e
k
e
y
u
s
ed
f
o
r
en
cr
y
p
tio
n
-
d
ec
r
y
p
tio
n
o
p
er
atio
n
s
,
cr
y
p
to
g
r
ap
h
ic
m
et
h
o
d
s
ar
e
class
i
f
ied
in
to
s
y
m
m
e
tr
ic
m
et
h
o
d
s
w
h
ic
h
is
u
s
ed
o
n
l
y
o
n
e
k
e
y
f
o
r
en
co
d
in
g
a
n
d
d
ec
o
d
in
g
lik
e
A
E
S
a
n
d
DE
S
w
h
ile
as
y
m
m
etr
ic
m
e
th
o
d
s
r
el
y
o
n
u
s
in
g
t
w
o
k
e
y
s
o
n
e
o
f
t
h
e
m
is
p
u
b
l
icl
y
d
ec
lar
ed
k
e
y
f
o
r
e
n
cr
y
p
ti
o
n
[
1
5
]
,
[
1
6
]
.
Fo
r
d
ec
o
d
in
g
t
h
er
e
is
an
o
t
h
er
k
e
y
w
h
ic
h
is
o
n
l
y
ac
ce
s
s
ib
le
to
au
t
h
o
r
ized
p
er
s
o
n
s
[
1
7
]
,
R
S
A
an
d
E
l
-
Ga
m
al
ar
e
p
o
p
u
lar
ex
a
m
p
les
o
f
th
i
s
t
y
p
e.
E
l
-
Ga
m
al
al
g
o
r
ith
m
i
s
as
y
m
m
etr
i
c
en
cr
y
p
tio
n
.
I
t
w
as
i
n
v
e
n
ted
i
n
1
9
8
5
b
y
T
ah
er
E
l
-
Ga
m
al
r
eli
ed
o
n
Dav
y
-
Hel
m
en
p
r
in
cip
le
f
o
r
ex
ch
a
n
g
i
n
g
k
e
y
s
[
1
8
]
,
[
1
9
]
.
T
h
e
en
cr
y
p
tio
n
s
tep
s
o
f
th
is
m
eth
o
d
ar
e
[
2
0
]
:
1.
Set e
s
s
e
n
tial p
ar
a
m
e
ter
s
: g
to
g
en
er
ate
a
c
y
clic
g
r
o
u
p
o
f
o
r
d
er
p
,
x
(
s
ec
r
et
k
e
y
)
: a
n
u
m
b
er
<p
-
1
2.
C
alcu
late
th
e
d
ec
lar
ed
k
e
y
(
y
)
f
r
o
m
(
1
)
[
2
0
]
,
[
2
1
]
:
y=
g
x
m
o
d
p
(
1
)
3.
C
h
o
o
s
e
r
an
d
o
m
v
al
u
e
k
a
n
d
en
cr
y
p
t secr
et
p
o
s
itio
n
s
(
m
)
u
s
i
n
g
t
h
e
(
2
)
:
co
d
e
1
=
g
k
(
2
)
co
d
e
2
=m
.
y
k
(
3
)
T
h
e
o
th
er
p
ar
ty
(
t
h
e
r
ec
ip
ien
t)
u
s
e
(
4
)
an
d
(
5
)
d
ec
r
y
p
t
co
d
e1
an
d
co
d
e2
ar
e
[
2
0
]
:
F=co
d
e
1
x
(
4
)
R
etr
iev
ed
=c
o
d
e
2
/F
(
5
)
2
.
2
.
Wa
t
er
m
a
r
k
i
ng
t
ec
hn
iqu
e
s
W
ater
m
ar
k
is
th
e
ar
t
o
f
p
lacin
g
a
s
ig
n
i
n
m
u
lti
m
ed
ia,
s
u
c
h
as
p
ictu
r
es,
au
d
io
,
an
d
v
id
eo
s
,
to
en
s
u
r
e
t
h
e
p
r
o
p
er
ty
r
ig
h
ts
o
f
th
eir
o
w
n
er
s
,
it
ca
n
b
e
im
p
le
m
e
n
ted
eith
er
b
y
ad
d
in
g
a
v
is
ib
le
lo
g
o
w
ith
o
u
t a
f
f
ec
tin
g
th
e
m
ai
n
co
n
te
n
t
o
r
th
e
lo
g
o
is
s
e
cr
et
an
d
h
id
d
en
i
n
s
id
e
th
e
h
o
s
t
[
2
2
]
,
[
2
3
]
.
I
n
an
y
ca
s
e,
t
h
e
w
ater
m
ar
k
m
u
s
t
b
e
d
if
f
ic
u
lt
to
d
elete
b
y
in
tr
u
d
er
s
[
2
4
]
.
T
h
e
w
ater
m
ar
k
ca
n
b
e
im
p
le
m
e
n
ted
b
y
ap
p
ly
i
n
g
s
o
m
e
tr
an
s
f
o
r
m
a
tio
n
s
to
th
e
i
m
a
g
e
s
u
ch
a
s
D
C
T
,
o
r
s
p
atial
tech
n
iq
u
e
s
ca
n
b
e
u
s
ed
li
k
e
L
SB
o
r
ev
en
a
h
y
b
r
id
s
t
y
le
[
2
4
]
.
I
n
th
is
p
ap
er
,
w
e
u
s
ed
a
d
if
f
er
en
t
m
et
h
o
d
f
r
o
m
t
h
e
c
u
r
r
en
t
m
eth
o
d
s
i
n
t
h
is
f
ield
.
2
.
3
.
Q
ua
lity
m
ea
s
ure
s
T
h
er
e
ar
e
s
ev
er
al
m
ea
s
u
r
es
to
ev
alu
ate
i
m
a
g
e
ch
a
n
g
e
s
b
ef
o
r
e
an
d
af
ter
tr
ea
t
m
en
t.
I
n
t
h
is
w
o
r
k
,
t
h
e
f
o
llo
w
in
g
m
ea
s
u
r
es
w
er
e
u
s
ed
[
7
]
,
[
2
5
]
:
MSE
(
af
ter
,
b
ef
o
r
e)
=
1
∑
∑
(
a
fte
r
(
i
,
j
)
−
b
e
for
e
(
i
,
j
)
)
2
=
1
=
1
(
6
)
P
SNR
(
af
ter
,
b
ef
o
r
e)
=1
0
lo
g
10
[
255
2
(
,
)
]
(
7
)
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
A
n
ew me
th
o
d
fo
r
w
a
terma
r
kin
g
co
lo
r
ima
g
es u
s
in
g
virt
u
a
l
h
id
in
g
a
n
d
E
l
-
Ga
ma
l
…
(
N
o
o
r
K
a
d
h
im
A
yo
o
b
)
5253
3.
RE
S
E
ARCH
M
E
T
H
O
D
3
.
1
.
H
idi
ng
w
a
t
er
m
a
r
k
v
irt
ua
lly
T
h
e
m
e
th
o
d
is
g
etti
n
g
s
tar
ted
b
y
r
ea
d
i
n
g
th
e
h
o
s
t
a
n
d
lo
g
o
o
f
s
ize
(
z1
*
z2
)
w
h
ich
is
co
n
v
er
ted
in
to
v
ec
to
r
,
th
er
e
f
o
r
e,
th
e
n
u
m
b
er
o
f
lo
ca
tio
n
s
to
b
e
f
o
u
n
d
is
eq
u
al
to
th
e
n
u
m
b
er
o
f
v
al
u
es
i
n
l
o
g
o
v
ec
to
r
w
h
ic
h
i
s
(
z1
*
z2
*
3
)
s
i
n
ce
t
h
e
lo
g
o
is
R
GB
i
m
ag
e.
T
h
e
p
r
o
ce
s
s
is
d
o
n
e
ac
co
r
d
in
g
to
th
e
f
o
llo
w
i
n
g
s
t
ep
s
:
Step
1
: "
Ma
tch
in
g
p
r
o
ce
s
s
"
T
h
e
id
ea
d
e
p
en
d
s
o
n
s
ea
r
ch
in
g
h
o
s
t
f
o
r
p
ix
els
t
h
at
h
av
e
t
h
e
s
a
m
e
v
al
u
es
a
s
lo
g
o
d
ata,
an
d
m
ain
tai
n
i
n
g
th
e
lo
ca
tio
n
s
o
f
t
h
ese
p
i
x
els
in
a
tab
le
ca
lled
lo
ca
tio
n
s
tab
le.
R
ep
ea
ted
v
al
u
es
ar
e
s
to
r
ed
in
th
e
s
a
m
e
lo
ca
tio
n
.
I
t
is
p
o
s
s
ib
le
th
at
s
o
m
e
v
alu
e
s
d
o
n
o
t
h
a
v
e
a
m
atc
h
in
th
e
h
o
s
t,
in
th
i
s
ca
s
e
a
r
a
n
d
o
m
p
ix
el
is
c
h
o
s
e
n
a
n
d
th
e
d
if
f
er
e
n
ce
b
et
w
ee
n
th
e
v
al
u
e
o
f
th
e
ch
o
s
e
n
p
ix
el
an
d
th
e
v
a
lu
e
o
f
th
e
lo
g
o
is
ca
lcu
lated
an
d
k
ep
t
o
n
th
e
lo
ca
tio
n
s
tab
le.
T
h
e
tab
le
o
f
lo
ca
tio
n
s
is
o
b
tain
ed
b
y
i
m
p
le
m
e
n
t
i
n
g
th
e
s
tep
s
o
f
th
e
al
g
o
r
ith
m
1
.
A
l
g
o
r
ith
m
1
:"
Ma
tch
i
n
g
P
r
o
ce
s
s
"
Inputs: logo as vector, image to be protected
Output: Location table
Steps:
For each item in the watermark:
1)
If the value appears previously, the same location is given.
2)
If
th
e
va
lu
e
ap
pe
ar
s
fo
r
th
e
fi
rs
t
ti
me
,
lo
o
k
at
th
e
ho
st
fo
r
pi
xe
ls
th
at
ha
ve
th
e
same value as the item.
If found:
2.a) Store the location of matching pixel in the table.
2.b) Set the value of the difference 0.
else
2.c
)
Choose a pixel randomly and store the lo
cation of that pixel in the table
2.d)Calculate
the
difference
between
the
pixel
and
item
values
and
store
the
difference
in the table.
End
T
o
h
id
e
h
y
p
o
th
etica
l
2
*
2
R
G
B
lo
g
o
in
5
1
2
*
5
1
2
h
o
s
ts
,
1
2
lo
ca
tio
n
s
(
2
*
2
*
3
=
1
2
v
alu
e
s
)
is
n
ee
d
ed
.
I
m
p
le
m
e
n
tatio
n
o
f
A
l
g
o
r
ith
m
1
co
u
ld
p
r
o
d
u
ce
th
e
tab
le
o
f
lo
ca
tio
n
s
li
k
e
T
ab
le
1
.
T
h
e
"r
an
k
"
r
ef
er
s
to
th
e
s
eq
u
en
ce
o
f
th
e
v
al
u
e
in
lo
g
o
v
ec
to
r
.
T
h
e
p
air
(
X,
Y)
r
e
p
r
esen
t
s
th
e
lo
ca
tio
n
o
f
lo
g
o
v
a
lu
e
in
th
e
h
o
s
t,
f
o
r
ex
a
m
p
le,
th
e
f
ir
s
t
v
al
u
e
o
f
th
e
lo
g
o
is
r
et
r
iev
ed
f
r
o
m
a
p
o
s
itio
n
(
1
8
9
,
1
2
4
)
in
th
e
h
o
s
t.
"
De
f
"
is
th
e
d
if
f
er
en
ce
b
et
w
ee
n
th
e
h
o
s
t
p
i
x
el
lo
ca
ted
at
a
p
o
s
itio
n
an
d
t
h
e
lo
g
o
v
alu
e.
W
h
en
d
ef
i
s
0
th
i
s
p
o
in
t
s
to
th
e
ex
ac
t
m
a
tch
i
n
g
.
T
h
e
n
o
n
-
ze
r
o
(
d
ef
)
m
ea
n
s
t
h
at
th
er
e
is
n
o
p
ix
el
i
n
th
e
h
o
s
t
m
atch
es
t
h
at
v
al
u
e,
s
o
a
r
an
d
o
m
lo
ca
tio
n
is
ch
o
s
en
an
d
d
if
f
er
e
n
ce
s
b
et
w
ee
n
th
e
t
w
o
v
a
lu
e
s
is
k
ep
t.
Step
2
: "
T
a
b
le
r
ed
u
ctio
n
"
Sto
r
in
g
r
ep
etiti
v
e
v
alu
e
s
in
th
e
s
a
m
e
lo
ca
tio
n
lead
s
to
th
e
ap
p
e
ar
an
ce
o
f
r
ep
ea
tin
g
co
lu
m
n
s
in
th
e
tab
le,
allo
w
i
n
g
t
h
e
ab
ilit
y
to
s
h
r
i
n
k
t
h
e
tab
le
to
s
av
e
cip
h
er
i
n
g
ti
m
e.
I
f
a
g
r
o
u
p
o
f
r
ep
ea
ted
co
lu
m
n
s
ap
p
ea
r
s
co
n
s
ec
u
tiv
e
l
y
,
t
h
e
r
ed
u
ctio
n
i
s
d
o
n
e
b
y
ta
k
i
n
g
t
h
e
f
ir
s
t
co
l
u
m
n
in
t
h
at
g
r
o
u
p
an
d
d
eletin
g
th
e
r
est.
T
h
e
s
h
ad
ed
ar
ea
s
in
th
e
T
ab
le
1
r
ef
er
to
g
r
o
u
p
s
o
f
r
ep
ea
ted
co
lu
m
n
s
.
Fo
r
ex
a
m
p
le,
th
e
co
lu
m
n
s
f
r
o
m
5
to
9
ar
e
j
u
s
t a
co
p
y
o
f
co
lu
m
n
4
.
T
o
s
h
r
in
k
th
i
s
g
r
o
u
p
,
w
e
s
a
v
e
co
lu
m
n
4
,
d
elete
co
lu
m
n
s
5
to
9
an
d
s
to
p
d
el
etin
g
at
co
lu
m
n
1
0
b
ec
au
s
e
it
s
to
r
es
a
d
i
f
f
er
en
t
lo
ca
tio
n
.
B
y
ap
p
l
y
in
g
t
h
e
s
a
m
e
p
r
o
ce
d
u
r
e
to
all
th
e
r
ep
ea
ted
an
d
s
eq
u
en
tial
co
lu
m
n
s
,
w
e
g
et
o
n
T
ab
le
2
.
Step
3
: "
E
n
co
d
in
g
th
e
p
o
s
itio
n
s
in
t
h
e
r
ed
u
ce
d
lo
ca
tio
n
tab
le
b
y
E
l
-
Ga
m
al
al
g
o
r
ith
m
"
T
h
is
is
d
o
n
e
th
r
o
u
g
h
th
e
s
tep
s
ex
p
lain
ed
i
n
2
.
1
w
h
ich
i
s
s
ett
i
n
g
p
ar
a
m
e
ter
s
,
g
et
tin
g
t
h
e
k
e
y
(
y
)
,
u
s
i
n
g
(
2
)
an
d
(
3
)
to
g
et
co
d
es.
B
y
en
co
d
in
g
X
an
d
Y
in
T
ab
le
2
u
s
i
n
g
p
r
ev
io
u
s
s
tep
s
,
w
e
g
et
co
d
es
as
s
h
o
w
n
i
n
T
ab
le
3
.
T
a
b
le
4
is
th
e
f
i
n
al
en
cr
y
p
ted
lo
ca
tio
n
s
tab
le.
T
ab
le
1
.
L
o
ca
tio
n
s
tab
le
R
a
n
k
1
2
3
4
5
6
7
8
9
10
11
12
X
1
8
9
1
8
9
2
0
0
1
8
9
1
8
9
1
8
9
1
8
9
1
8
9
1
8
9
43
1
8
9
1
8
9
Y
1
2
4
1
2
4
2
6
6
1
2
4
1
2
4
1
2
4
1
2
4
1
2
4
1
2
4
4
5
9
1
2
4
12
4
d
e
f
0
0
0
0
0
0
0
0
0
-
1
2
3
0
0
T
ab
le
2
.
C
o
m
p
r
ess
ed
lo
ca
tio
n
tab
le
R
a
n
k
1
3
4
10
11
X
1
8
9
2
0
0
1
8
9
43
1
8
9
Y
1
2
4
2
6
6
1
2
4
4
5
9
1
2
4
d
e
f
0
0
0
-
1
2
3
0
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.
11
,
No
.
6
,
Dec
em
b
er
2
0
2
1
:
5
2
5
1
-
5
2
5
8
5254
T
ab
le
3
.
C
ip
h
er
in
g
p
o
s
itio
n
s
b
y
E
l
-
Ga
m
al
X
C
o
d
e
1
C
o
d
e
2
y
C
o
d
e
1
C
o
d
e
2
1
8
9
6
7
0
1
1
2
4
1
5
6
7
8
1
2
0
0
2
5
3
6
3
8
2
6
6
8
3
6
26
1
8
9
4
3
5
1
0
8
1
2
4
1
9
8
90
43
9
6
1
5
8
8
4
5
9
5
8
1
3
9
8
1
8
9
2
9
3
2
8
4
1
2
4
2
8
0
2
4
4
T
ab
le
4
.
E
n
cr
y
p
ted
lo
ca
tio
n
ta
b
le
R
a
n
k
C
o
d
e
s o
f
X
I
n
d
e
x
e
s
C
o
d
e
s o
f
Y
I
n
d
e
x
e
s
D
e
f
e
r
e
n
c
e
1
6
7
0
1
1
5
6
7
8
1
0
3
2
5
3
6
3
8
8
3
6
26
0
4
5
1
0
8
1
9
8
90
0
10
9
6
1
5
8
8
5
8
1
3
9
8
-
1
2
3
11
2
9
3
2
8
4
2
8
0
2
4
4
0
3
.
2
.
Ret
riev
ing
pro
ce
s
s
T
h
e
g
en
er
al
s
tep
s
to
r
esto
r
e
a
w
ater
m
ar
k
ar
e:
a.
Dec
o
d
e
p
o
s
itio
n
s
s
to
r
ed
in
th
e
en
cr
y
p
ted
lo
ca
tio
n
tab
le
u
s
i
n
g
(
4
)
an
d
(
5
)
to
g
et
r
ed
u
ce
d
l
o
ca
tio
n
s
tab
le.
Fo
r
th
e
p
r
ev
io
u
s
e
x
a
m
p
le,
t
h
e
in
p
u
t o
f
th
i
s
s
tep
i
s
T
ab
le
4
,
th
e
o
u
tp
u
t is T
ab
le
2
.
b.
Dec
o
m
p
r
ess
r
ed
u
ce
d
tab
le
to
g
et
th
e
f
u
ll
v
er
s
io
n
o
f
lo
ca
tio
n
s
t
ab
le.
Her
e
co
m
es
th
e
r
o
le
o
f
th
e
r
o
w
(
r
an
k
)
.
T
o
r
etr
iev
e
th
e
d
elete
d
co
lu
m
n
s
b
et
w
ee
n
co
lu
m
n
i
an
d
co
lu
m
n
i+1
,
in
s
er
t
n
co
p
ies
o
f
t
h
e
co
lu
m
n
i
b
et
w
ee
n
th
e
t
w
o
c
o
lu
m
n
s
w
h
er
e
n
=
[
r
an
k
(
i+1
)
–
r
an
k
(
i)
]
-
1
.
Fo
r
ex
a
m
p
le,
to
r
esto
r
e
co
lu
m
n
s
b
et
wee
n
co
lu
m
n
s
1
an
d
2
in
th
e
T
ab
le
2
;
n
=
(
r
a
n
k
2
-
r
a
n
k
1
)
-
1
=
(
3
-
1)
-
1
=1
.
On
e
co
p
y
o
f
t
h
e
co
lu
m
n
1
is
i
n
s
er
t
ed
as
in
T
ab
le
5.
B
y
d
o
in
g
t
h
e
s
a
m
e
s
tep
s
o
n
all
co
lu
m
n
s
w
e
g
et
t
h
e
o
r
ig
i
n
a
l
lo
ca
tio
n
s
tab
le
.
T
ab
le
5
.
T
h
e
d
ec
o
m
p
r
ess
i
n
g
o
f
lo
ca
tio
n
tab
le
R
a
n
k
1
3
4
10
11
R
a
n
k
1
2
3
4
10
11
X
1
8
9
2
0
0
1
8
9
43
1
8
9
X
1
8
9
1
8
9
2
0
0
1
8
9
43
1
8
9
Y
1
2
4
2
6
6
1
2
4
4
5
9
1
2
4
Y
1
2
4
1
2
4
2
6
6
1
2
4
4
5
9
1
2
4
d
e
f
0
0
0
-
1
2
3
0
D
e
f
0
0
0
0
-
1
2
3
0
a
:
b
e
f
o
r
e
d
e
c
o
mp
r
e
ssi
n
g
b
:
a
f
t
e
r
d
e
c
o
m
p
r
e
ssi
n
g
c.
R
etr
iev
e
w
ater
m
ar
k
d
ata
f
r
o
m
th
e
i
m
ag
e
u
s
i
n
g
alg
o
r
it
h
m
2:
A
l
g
o
r
ith
m
2
:"
Ma
tch
i
n
g
R
etr
ie
v
in
g
"
Inputs: Location table, host.
Output: watermark.
Steps:
1
-
For each location (x, y) stored in the table do:
a
-
Check the deference
value for the location:
if def =0
Recovered value is the pixel value of host without change.
else
Recovered value is the pixel value of host+ def.
end
a
-
Append the recovered valu
e of the vector of retrieving.
2
-
Reshape obtained vector to get logo image.
End
4
.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
w
a
s
p
r
o
g
r
a
m
m
ed
u
s
i
n
g
M
A
T
L
A
B
2
0
1
7
an
d
th
en
te
s
ted
u
s
in
g
t
h
r
e
e
s
tan
d
ar
d
h
o
s
ts
: L
e
n
a,
B
ab
o
o
n
an
d
P
ep
p
er
.
T
h
e
w
ater
m
ar
k
s
u
s
ed
i
n
th
i
s
s
t
u
d
y
ar
e
s
h
o
w
n
i
n
Fi
g
u
r
e
1
.
4
.
1
.
M
e
a
s
uring
t
he
qu
a
lity
o
f
t
he
pro
po
s
ed
w
a
t
er
m
a
r
kin
g
T
o
ev
alu
ate
th
e
ef
f
icie
n
c
y
o
f
t
h
e
v
ir
tu
a
l
h
id
in
g
,
P
SNR
is
co
m
p
u
ted
as
d
o
cu
m
e
n
ted
in
T
ab
le
6
t
o
s
e
e
th
e
d
if
f
er
en
ce
b
et
w
ee
n
h
o
s
t
b
ef
o
r
e
an
d
af
ter
w
ater
m
ar
k
in
g
.
I
t
is
k
n
o
w
n
t
h
at
P
SNR
d
ep
en
d
s
m
ain
l
y
o
n
MSE
an
d
th
e
ar
e
b
o
th
ex
p
lai
n
ed
in
2
.
3
.
Fig
u
r
e
2
s
h
o
w
s
:
T
h
e
o
r
ig
in
al
h
o
s
t
an
d
lo
g
o
b
ef
o
r
e
w
at
er
m
ar
k
i
n
g
,
t
h
e
h
o
s
t
af
ter
p
r
o
ce
s
s
in
g
an
d
w
h
at
t
h
e
r
ec
o
v
er
ed
(
cr
ea
ted
)
w
ater
m
ar
k
lo
o
k
s
lik
e
w
h
ic
h
is
co
m
p
let
el
y
id
en
tical
to
th
e
o
r
ig
in
al.
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
A
n
ew me
th
o
d
fo
r
w
a
terma
r
kin
g
co
lo
r
ima
g
es u
s
in
g
virt
u
a
l
h
id
in
g
a
n
d
E
l
-
Ga
ma
l
…
(
N
o
o
r
K
a
d
h
im
A
yo
o
b
)
5255
Fig
u
r
e
1
.
W
ater
m
ar
k
s
T
ab
le
6
.
P
SNR
f
o
r
th
e
h
o
s
t a
f
t
er
v
ir
tu
al
h
id
in
g
H
o
st
W
a
t
e
r
mark
i
W
a
t
e
r
mark
i
i
L
e
n
a
i
n
f
i
n
i
t
e
l
y
i
n
f
i
n
i
t
e
l
y
B
a
b
o
o
n
i
n
f
i
n
i
t
e
l
y
i
n
f
i
n
i
t
e
l
y
P
e
p
p
e
r
i
n
f
i
n
i
t
e
l
y
i
n
f
i
n
i
t
e
l
y
Fig
u
r
e
2
.
R
esu
lts
o
f
v
ir
t
u
al
h
id
in
g
4
.
2
.
M
e
a
s
uring
t
he
i
mm
u
nity
o
f
t
he
m
et
ho
d a
g
a
ins
t
a
t
t
a
ck
s
T
o
p
r
o
v
e
th
e
i
m
m
u
n
it
y
o
f
t
h
e
m
et
h
o
d
,
ad
d
itio
n
al
tes
ts
w
er
e
p
er
f
o
r
m
ed
b
y
u
s
i
n
g
f
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v
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t
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s
o
f
at
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k
o
n
th
e
i
m
ag
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o
s
t a
n
d
th
e
n
r
etr
iev
in
g
t
h
e
w
ater
m
ar
k
af
ter
attac
k
s
.
T
h
e
t
y
p
es o
f
attac
k
s
u
s
e
d
ar
e:
S
alt
&
p
ep
p
er
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d
en
s
it
y
0
.
0
5
)
Me
d
ian
f
il
ter
W
h
ite
n
o
is
e
(
Ga
u
s
s
ian
w
ith
m
=0
,
v
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0
1
)
R
o
tate
h
o
s
t 1
0
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eg
r
ee
s
An
d
f
i
n
all
y
,
h
i
s
to
g
r
a
m
eq
u
aliz
atio
n
.
I
n
Fi
g
u
r
e
3
,
c
h
a
n
g
e
s
to
t
h
e
h
o
s
t
(
L
e
n
a)
a
f
ter
attac
k
s
ar
e
s
h
o
w
n
.
I
n
T
ab
le
s
7
an
d
8
,
t
h
e
w
ater
m
ar
k
s
r
etr
iev
ed
f
r
o
m
t
h
e
h
o
s
t
a
f
ter
attac
k
s
ar
e
s
h
o
w
n
.
T
h
e
v
al
u
e
s
o
f
MSE
a
n
d
P
SNR
f
o
r
t
h
e
s
e
w
ater
m
ar
k
s
ar
e
r
ec
o
r
d
e
d
T
ab
le
s
9
an
d
1
0
.
Fig
u
r
e
3
.
L
en
a
a
f
ter
attac
k
s
:
(
a
)
s
alt
&
p
ep
p
er
,
(
b
)
m
ed
ia
n
,
(
c)
Gau
s
s
ia
n
,
(
d
)
r
o
tatio
n
1
0
,
(
e)
h
is
to
g
r
a
m
eq
u
aliza
tio
n
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.
11
,
No
.
6
,
Dec
em
b
er
2
0
2
1
:
5
2
5
1
-
5
2
5
8
5256
T
ab
le
7
.
R
ec
o
v
er
ed
w
ater
m
ar
k
(
i)
f
r
o
m
L
en
a
a
f
ter
v
ar
io
u
s
a
ttack
s
A
t
t
a
c
k
t
y
p
e
S
a
l
t
&
P
e
p
p
e
r
M
e
d
i
a
n
G
a
u
ssi
a
n
R
o
t
a
t
i
o
n
E
q
u
a
l
i
z
a
t
i
o
n
R
e
c
o
v
e
r
e
d
w
a
t
e
r
mark
T
ab
le
8
.
R
ec
o
v
er
ed
w
ater
m
ar
k
(
ii)
f
r
o
m
L
e
n
a
af
ter
v
ar
io
u
s
attac
k
s
A
t
t
a
c
k
t
y
p
e
S
a
l
t
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e
p
p
e
r
M
e
d
i
a
n
G
a
u
ssi
a
n
R
o
t
a
t
i
o
n
Eq
u
a
l
i
z
a
t
i
o
n
R
e
c
o
v
e
r
e
d
w
a
t
e
r
mark
T
ab
le
9
.
MSE
an
d
P
SNR
f
o
r
r
ec
o
v
er
ed
w
ater
m
ar
k
(
ii)
af
ter
attac
k
s
A
t
t
a
c
k
S
a
l
t
&
P
e
p
p
e
r
M
e
d
i
a
n
G
a
u
ssi
a
n
R
o
t
a
t
i
o
n
Eq
u
a
l
i
z
a
t
i
o
n
M
S
E
2
3
3
.
9
2
6
8
1
6
.
0
5
8
7
2
3
9
.
2
0
4
7
2
.
1
8
5
6
e
+
0
3
9
2
.
4
6
4
6
P
S
N
R
2
4
.
4
4
0
0
3
6
.
0
7
3
7
2
4
.
3
4
3
1
1
4
.
7
3
5
1
2
8
.
4
7
1
0
T
ab
le
10
.
MSE
an
d
P
SNR
f
o
r
r
ec
o
v
er
ed
w
a
ter
m
ar
k
(
i)
af
ter
attac
k
s
A
t
t
a
c
k
S
a
l
t
&
P
e
p
p
e
r
M
e
d
i
a
n
G
a
u
ssi
a
n
R
o
t
a
t
i
o
n
E
q
u
a
l
i
z
a
t
i
o
n
M
S
E
6
8
3
.
5
1
3
5
4
3
.
5
9
1
6
4
1
7
.
7
7
2
2
5
.
0
9
6
6
e
+
0
3
1
8
3
.
1
3
1
6
P
S
N
R
1
9
.
7
8
3
3
3
1
.
7
3
6
8
2
1
.
9
2
1
4
1
1
.
0
5
8
0
2
5
.
5
0
3
2
4
.
3
.
Co
m
pa
ri
s
o
n w
it
h t
he
o
t
her
m
et
ho
ds
Fig
u
r
e
4
s
h
o
w
s
t
h
e
p
er
f
o
r
m
an
ce
o
f
v
ir
t
u
al
h
id
in
g
co
m
p
ar
ed
to
th
e
m
et
h
o
d
s
m
en
t
io
n
ed
in
th
e
in
tr
o
d
u
ctio
n
b
ased
o
n
P
SNR
.
Fig
u
r
e
4
.
co
m
p
ar
is
o
n
w
it
h
p
r
ev
io
u
s
m
et
h
o
d
s
4
.
3
.
Resul
t
ex
pla
na
t
io
n
T
h
e
r
esu
lts
o
b
tain
ed
ca
n
b
e
in
t
er
p
r
et
ed
an
d
an
aly
ze
d
as
f
o
llo
w
s
:
a.
Op
ti
m
al
v
al
u
es
f
o
r
P
SNR
b
etw
ee
n
h
o
s
t
b
ef
o
r
e
an
d
af
ter
w
a
ter
m
ar
k
i
n
g
in
T
ab
le
6
:
T
h
e
u
n
iq
u
e
f
ea
t
u
r
e
o
f
o
u
r
m
et
h
o
d
is
t
h
at
t
h
e
p
r
i
n
cip
le
o
f
"
n
o
ch
a
n
g
e
in
th
e
h
o
s
t"
ad
o
p
ted
b
y
v
ir
t
u
al
h
id
i
n
g
m
a
k
e
s
th
e
v
al
u
e
o
f
MSE
ze
r
o
an
d
th
u
s
th
e
v
alu
e
o
f
P
SNR
is
al
w
a
y
s
i
n
f
in
i
t
y
b
ec
au
s
e
th
e
m
eth
o
d
ai
m
s
to
cr
ea
te,
n
o
t h
id
e,
th
e
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
A
n
ew me
th
o
d
fo
r
w
a
terma
r
kin
g
co
lo
r
ima
g
es u
s
in
g
virt
u
a
l
h
id
in
g
a
n
d
E
l
-
Ga
ma
l
…
(
N
o
o
r
K
a
d
h
im
A
yo
o
b
)
5257
w
ater
m
ar
k
.
T
h
e
s
ec
r
et
o
f
th
e
m
eth
o
d
is
s
ea
r
ch
in
g
h
o
s
t
f
o
r
lo
ca
tio
n
s
w
h
er
e
th
e
p
ix
el
s
m
atc
h
th
e
lo
g
o
v
alu
es
an
d
h
an
d
li
n
g
ca
s
es
w
h
e
n
t
h
er
e
ar
e
n
o
p
ix
els
in
h
o
s
t
id
en
tic
al
to
th
ese
v
alu
e
s
.
As
a
co
n
s
e
q
u
en
ce
,
th
er
e
is
n
o
n
ee
d
f
o
r
ap
p
ly
in
g
t
h
e
m
etr
i
cs lik
e
P
SN
R
b
ec
au
s
e
t
h
e
y
w
i
l
l a
l
w
a
y
s
b
e
id
ea
l.
b.
I
m
m
u
n
it
y
to
v
ar
io
u
s
t
y
p
e
s
o
f
a
ttack
s
a
s
p
r
o
v
en
b
y
e
x
p
er
i
m
e
n
ts
in
T
ab
le
s
7
-
10
:
b
ased
o
n
th
e
r
esu
lt
s
s
h
o
w
n
in
T
ab
le
s
7
an
d
8
,
th
e
lo
g
o
h
as
b
e
en
clea
r
l
y
r
esto
r
ed
in
al
m
o
s
t
all
test
ed
attac
k
s
,
w
h
ic
h
co
n
f
ir
m
s
t
h
e
o
w
n
er
’
s
r
i
g
h
t
to
th
e
i
m
ag
e
p
r
o
tecte
d
b
y
t
h
e
p
r
o
p
o
s
ed
m
eth
o
d
.
T
h
e
r
ea
s
o
n
f
o
r
th
e
s
tr
e
n
g
t
h
an
d
ef
f
icie
n
c
y
o
f
th
e
m
et
h
o
d
is
th
at
o
u
r
m
et
h
o
d
d
o
es
n
o
t
ch
an
g
e
t
h
e
o
r
ig
in
al
i
m
ag
e
d
ata
an
d
th
e
h
o
s
t
lo
s
es
s
o
m
e
o
f
its
d
ata
in
o
n
e
ca
s
e,
o
n
l
y
w
h
e
n
attac
k
i
n
g
.
I
n
o
th
er
w
a
y
s
,
th
e
h
o
s
t
lo
s
es
p
ar
t
o
f
d
ata
t
w
ice:
th
e
f
ir
s
t
w
h
e
n
h
id
in
g
t
h
e
w
ater
m
ar
k
a
n
d
th
e
s
ec
o
n
d
if
it
i
s
attac
k
ed
,
w
h
ich
c
au
s
e
s
it
to
lo
s
e
a
lo
t.
T
h
is
is
w
h
y
t
h
e
p
r
o
p
o
s
ed
v
ir
tu
a
l h
id
i
n
g
g
i
v
es
b
etter
r
esu
lts
th
a
n
o
th
er
m
et
h
o
d
s
.
c.
T
h
e
s
ec
u
r
it
y
a
n
d
t
h
e
r
o
le
o
f
E
l
-
Ga
m
al:
t
h
e
i
m
p
o
r
tan
t
k
e
y
to
th
is
m
et
h
o
d
is
t
h
e
lo
ca
tio
n
s
a
n
d
p
r
o
tectin
g
th
ese
p
o
s
itio
n
s
u
s
i
n
g
en
cr
y
p
t
io
n
alg
o
r
it
h
m
lik
e
E
l
-
Ga
m
al
ad
d
s
an
ad
d
itio
n
al
lev
e
l
o
f
s
ec
u
r
it
y
to
th
e
m
et
h
o
d
.
Usu
all
y
r
esear
c
h
er
s
en
co
d
e
d
ata
o
r
im
a
g
es,
b
u
t
in
t
h
is
s
t
u
d
y
,
w
e
ad
o
p
ted
u
n
f
a
m
i
liar
ap
p
r
o
ac
h
w
h
er
e
th
e
f
o
cu
s
w
a
s
o
n
p
r
o
tec
tin
g
p
o
s
itio
n
s
b
ec
au
s
e
t
h
e
y
ar
e
th
e
s
o
u
r
ce
o
f
cr
ea
tin
g
th
e
w
at
er
m
ar
k
.
I
n
th
e
ca
s
e
o
f
u
n
a
u
t
h
o
r
ized
p
er
s
o
n
o
b
tain
s
th
e
lo
ca
tio
n
tab
le,
th
e
v
alu
es i
n
th
is
tab
le
ar
e
o
n
l
y
co
d
es o
f
p
o
s
itio
n
s
an
d
h
e
m
u
s
t b
r
ea
k
th
ese
co
d
es.
E
v
en
if
h
e
d
o
es,
w
h
a
t h
e
w
i
ll
g
et
is
a
co
n
cise v
er
s
io
n
o
f
th
e
o
r
ig
in
al
tab
le
d
u
e
to
th
e
u
s
e
o
f
co
m
p
r
ess
io
n
.
5.
CO
NCLU
SI
O
N
I
n
th
is
s
t
u
d
y
a
n
e
w
d
ir
ec
tio
n
in
th
e
f
ield
o
f
w
ater
m
ar
k
in
g
is
p
r
o
p
o
s
ed
.
I
t
h
as b
ee
n
p
r
o
v
en
th
at
th
er
e
is
n
o
n
ee
d
f
o
r
p
h
y
s
ical
e
m
b
ed
d
i
n
g
o
f
w
a
ter
m
ar
k
in
th
e
h
o
s
t
if
th
e
p
ix
el
lo
ca
tio
n
s
t
h
at
ar
e
s
i
m
il
ar
to
th
e
w
ater
m
ar
k
p
ix
els
ar
e
k
ep
t
in
a
lo
ca
tio
n
ta
b
le,
an
d
th
e
i
s
s
u
e
o
f
t
h
o
s
e
p
i
x
els
th
a
t
ar
e
n
o
t
m
atc
h
ed
in
t
h
e
h
o
s
t
h
as
al
s
o
b
ee
n
r
eso
lv
ed
.
T
h
e
s
tu
d
y
d
is
c
u
s
s
ed
a
w
a
y
to
r
ed
u
ce
th
e
d
ata
o
f
l
o
ca
tio
n
tab
le
an
d
p
r
o
tect
th
at
d
ata
f
r
o
m
i
n
tr
u
d
er
s
th
r
o
u
g
h
e
n
cr
y
p
tio
n
b
y
E
l
-
Ga
m
al
.
T
h
e
r
esu
l
ts
o
b
tain
ed
p
r
o
v
e
t
h
e
ef
f
ec
ti
v
e
n
es
s
o
f
t
h
e
m
eth
o
d
,
as
th
e
P
SN
R
v
alu
e
b
e
f
o
r
e
an
d
af
ter
w
a
ter
m
ar
k
in
g
is
al
w
a
y
s
in
f
i
n
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