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123
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1.
I
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RODU
C
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ON
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[
1]
.
W
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f
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[
2]
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T
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t
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r
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[
3]
.
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to
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r
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phy
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t
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phy
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w
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[
4]
.
C
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p
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t
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nl
y
[
5]
.
S
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ga
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phy
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s
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[
6
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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[
7]
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T
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n
e
y
e
i
s
s
us
c
e
pt
i
bl
e
t
o
f
i
nd
i
ng
a
r
e
a
s
f
o
r
e
m
b
e
dd
i
ng
s
e
c
r
e
t
m
e
s
s
a
ge
s
,
s
uc
h
a
s
s
m
o
o
t
h
-
e
dge
por
t
i
o
ns
,
c
h
a
n
ge
s
i
n
c
o
l
o
r
i
n
t
e
n
s
i
t
y
a
n
d
c
h
a
n
ge
s
i
n
im
a
g
e
b
r
i
g
h
t
n
e
s
s
[
8]
.
I
m
a
ge
s
t
e
ga
n
o
gr
a
phy
t
e
c
hni
que
s
pr
i
m
a
r
i
ly
h
a
ve
t
h
r
e
e
b
a
s
i
c
r
e
qu
i
r
e
m
e
n
t
s
:
pa
yl
o
a
d
c
a
pa
c
i
t
y
,
im
pe
r
c
e
pt
i
bil
i
t
y
a
n
d
s
im
i
l
a
r
i
t
y
i
nd
e
x
.
T
h
e
pa
y
l
o
a
d
c
a
pa
c
i
t
y
r
e
f
e
r
s
t
o
t
h
e
n
u
m
be
r
o
f
s
e
c
r
e
t
m
e
s
s
a
ge
bi
t
s
t
h
a
t
c
a
n
b
e
hi
dde
n
i
n
t
h
e
c
o
v
e
r
i
m
a
ge
w
i
t
h
o
ut
c
a
us
i
n
g
n
o
t
i
c
e
a
bl
e
d
i
s
to
r
t
i
o
n
.
I
m
pe
r
c
e
pt
i
bil
i
t
y
i
s
m
e
a
s
ur
e
d
b
y
e
v
a
l
ua
t
i
o
n
pa
r
a
m
e
t
e
r
s
s
uc
h
a
s
pe
a
k
s
i
g
na
l
-
to
-
n
o
i
s
e
r
a
t
i
o
(
P
S
NR
)
a
n
d
m
e
a
n
s
qu
a
r
e
d
e
r
r
or
(
M
S
E
)
.
T
h
e
s
i
mi
l
a
r
i
t
y
i
nde
x
i
s
r
e
pr
e
s
e
n
t
e
d
by
t
h
e
hi
s
t
o
gr
a
m
o
f
t
h
e
c
o
v
e
r
a
n
d
S
t
e
g
o
i
m
a
ge
s
,
whi
c
h
s
h
o
ws
t
h
e
f
r
e
q
ue
n
c
y
o
f
t
h
e
pi
xe
l
s
i
n
t
e
n
s
i
t
e
s
.
T
h
e
pr
o
p
o
s
e
d
a
l
go
r
i
t
hm
ha
s
b
e
e
n
e
v
a
l
ua
t
e
d
f
o
r
a
l
l
m
e
t
r
i
c
s
m
e
n
t
i
o
n
e
d
a
b
o
v
e
.
Ne
a
m
a
h
e
t
al.
[
1]
pr
o
p
o
s
e
d
t
h
e
l
e
a
s
t
s
i
g
ni
f
i
c
a
n
t
bit
(
L
S
B
)
s
t
e
ga
n
o
gr
a
phy
m
e
t
h
o
d
to
hi
de
s
e
c
r
e
t
da
t
a
i
n
c
o
l
o
ur
i
m
a
ge
s
.
An
e
nc
r
y
pt
i
o
n
ke
y
a
n
d
t
h
e
XN
OR
ga
t
e
a
r
e
e
m
p
l
o
y
e
d
to
e
n
c
r
y
pt
t
h
e
s
e
c
r
e
t
i
nf
o
r
m
a
t
i
o
n
.
Wu
a
n
d
T
s
a
i
[
2]
f
o
c
us
e
d
o
n
gr
a
y
s
c
a
l
e
i
m
a
g
e
s
a
n
d
i
n
t
r
o
duc
e
d
a
n
e
w
r
e
v
e
r
s
e
s
t
e
ga
n
o
gr
a
phy
m
e
t
h
o
d
i
n
i
m
a
ge
s
t
e
ga
n
o
gr
a
phy
,
whi
c
h
y
i
e
l
d
s
a
b
e
tt
e
r
s
i
mi
l
a
r
i
t
y
in
de
x
a
n
d
hi
g
he
r
pa
y
l
o
a
d
c
a
pa
c
i
t
y
.
Yus
u
f
a
n
d
Ha
gr
a
s
[
5]
pr
o
p
o
s
e
d
a
n
e
w
s
t
e
ga
n
o
gr
a
phy
t
e
c
hni
que
by
c
o
m
bi
n
i
ng
a
f
uz
z
y
s
y
s
t
e
m
a
n
d
t
h
e
C
a
nny
o
pe
r
a
to
r
.
M
e
l
i
n
e
t
al.
[
8]
s
ugge
s
t
e
d
n
o
f
il
t
e
r
i
s
n
e
e
de
d
t
o
f
in
d
e
dge
s
i
n
a
n
im
a
ge
.
A
ne
w
e
dge
de
t
e
c
t
i
o
n
t
e
c
hni
que
wa
s
pr
o
p
o
s
e
d,
whi
c
h
r
e
li
e
d
o
n
gr
a
d
i
e
n
t
v
a
l
ue
s
a
n
d
f
uz
z
y
s
e
t
t
h
e
o
r
y
.
Na
de
r
n
e
j
a
d
e
t
al.
[
9]
pr
o
p
o
s
e
d
a
n
o
v
e
l
m
e
t
h
o
d
f
o
r
de
t
e
c
t
i
n
g
t
h
e
e
dge
s
o
f
a
ny
c
o
l
o
r
e
d
obj
e
c
t
,
whi
c
h
o
pe
r
a
t
e
s
o
n
E
uc
l
i
de
a
n
d
i
s
t
a
n
c
e
.
C
om
pa
r
a
t
i
v
e
a
n
a
ly
s
i
s
i
s
c
o
n
duc
t
e
d
b
e
t
we
e
n
t
h
e
pr
o
p
o
s
e
d
e
dge
d
e
t
e
c
t
i
o
n
m
e
t
h
o
ds
a
n
d
t
h
e
C
a
nny
o
pe
r
a
tor
,
m
a
i
n
ly
a
i
m
e
d
a
t
o
v
e
r
c
o
m
i
ng
d
i
f
f
i
c
u
l
t
i
e
s
i
n
m
u
l
t
if
l
a
s
h
e
dge
de
t
e
c
t
i
o
n
.
Yuv
a
r
a
j
a
a
n
d
S
a
b
e
e
ni
a
n
[
10]
us
e
d
t
h
e
DW
T
tec
hni
qu
e
to
n
o
r
m
a
l
i
z
e
c
o
l
o
ur
i
m
a
ge
s
,
b
o
t
h
h
o
r
i
z
o
n
t
a
l
ly
a
n
d
v
e
r
t
i
c
a
ll
y
,
a
s
we
l
l
a
s
d
i
a
go
n
a
ll
y
.
T
h
e
f
uz
z
y
t
y
pe
1
i
s
ut
i
li
z
e
d
f
o
r
hi
d
i
ng
i
nf
o
r
m
a
t
i
o
n
.
T
a
n
g
e
t
al.
[
11
]
i
n
t
r
o
duc
e
d
a
c
h
a
o
t
i
c
m
e
t
h
o
d
f
o
r
e
m
b
e
dd
i
ng
s
e
c
r
e
t
m
e
s
s
a
ge
s
.
A
n
e
w
f
uz
z
y
i
n
f
e
r
e
n
c
e
s
y
s
t
e
m
(
F
I
S
)
wa
s
de
s
i
g
n
e
d
to
w
o
r
k
wi
t
h
L
S
B
a
n
d
e
m
be
dd
i
ng
bi
t
o
pt
i
mi
z
a
t
i
o
n
,
a
i
m
i
ng
to
i
m
pr
o
v
e
pa
y
l
o
a
d
c
a
pa
c
i
t
y
a
n
d
i
m
p
e
r
c
e
pt
i
bil
i
t
y
.
T
a
n
g
e
t
al
.
[
12]
f
o
c
us
e
d
o
n
t
h
e
L
S
B
s
t
e
ga
n
o
gr
a
phy
m
e
t
h
o
d,
c
o
m
p
a
r
i
n
g
P
S
NR
a
n
d
M
S
E
va
l
u
e
s
f
o
r
1
-
L
S
B
a
n
d
4
-
L
S
B
i
n
c
o
l
o
r
i
m
a
g
e
s
.
Al
-
F
a
y
d
i
e
t
al.
[
13
]
pr
o
p
o
s
e
d
a
n
e
w
s
t
e
ga
n
o
gr
a
phy
m
e
t
h
o
d
b
a
s
e
d
o
n
L
S
B
a
n
d
M
S
B
t
e
c
h
ni
qu
e
s
,
ut
i
li
z
i
ng
t
h
e
L
Z
W
t
e
c
hni
que
f
o
r
c
o
m
pr
e
s
s
i
ng
s
e
n
s
i
t
i
ve
da
t
a
i
n
d
i
g
i
t
a
l
im
a
ge
s
.
M
uha
m
m
a
d
e
t
al.
[
14]
pr
e
s
e
n
t
e
d
a
n
e
w
s
t
e
ga
n
o
gr
a
phy
m
e
t
h
o
d
r
e
f
e
r
r
e
d
to
a
s
S
K
A
-
L
S
B
,
w
h
e
r
e
S
KA
s
t
a
n
ds
f
o
r
S
t
e
g
o
ke
y
a
l
go
r
i
t
hm
s
a
n
d
L
S
B
s
t
a
n
d
s
f
o
r
L
S
B
.
T
h
e
s
e
2
a
l
go
r
i
t
hm
s
e
m
p
l
o
y
e
d
t
w
o
t
y
pe
s
o
f
L
S
B
,
n
a
m
e
ly
t
wo
-
l
e
v
e
l
L
S
B
a
n
d
M
u
l
t
i
-
l
e
ve
l
L
S
B
,
to
a
c
hi
e
v
e
be
tt
e
r
s
i
mi
l
a
r
i
t
y
b
e
t
we
e
n
t
h
e
or
i
g
i
na
l
im
a
ge
a
n
d
t
h
e
S
t
e
go
i
m
a
g
e
.
R
e
c
e
n
t
l
y
,
a
n
e
w
i
m
a
ge
s
t
e
ga
n
o
gr
a
phy
t
e
c
hni
que
h
a
s
b
e
e
n
pr
o
po
s
e
d
f
o
r
e
m
be
dd
i
n
g
s
e
c
r
e
t
da
t
a
i
n
to
d
i
g
i
t
a
l
im
a
ge
s
.
T
hi
s
t
e
c
hni
que
i
s
b
a
s
e
d
o
n
a
f
uz
z
y
e
dg
e
de
t
e
c
to
r
a
n
d
t
h
e
L
S
B
m
e
t
h
o
d.
T
h
e
L
S
B
a
n
d
M
S
B
m
e
t
h
o
ds
f
o
r
e
m
be
dd
i
n
g
s
e
c
r
e
t
m
e
s
s
a
ge
s
h
a
v
e
b
e
e
n
d
i
s
c
us
s
e
d
by
Al
-
F
a
y
d
i
e
t
al.
[
13
]
.
M
ot
i
va
t
e
d
by
t
h
e
w
o
r
k
do
n
e
i
n
t
hi
s
f
i
e
l
d,
t
h
e
pr
e
s
e
n
t
r
e
s
e
a
r
c
h
pr
o
po
s
e
d
m
s
h
E
dge
R
GB
_T
1
a
l
go
r
i
t
hm
ba
s
e
d
o
n
f
uz
z
y
s
e
t
s
a
n
d
t
h
e
L
S
B
m
e
t
h
o
d,
whi
c
h
s
a
t
i
s
f
i
e
s
a
ll
t
h
r
e
e
b
a
s
i
c
r
e
qu
i
r
e
m
e
n
t
s
o
f
i
m
a
g
e
s
t
e
ga
n
o
gr
a
phy
.
T
o
i
m
pr
o
v
e
s
e
c
ur
i
t
y
,
e
dge
p
i
x
e
l
s
a
r
e
us
e
d
t
o
hi
de
t
h
e
s
e
c
r
e
t
da
t
a
.
A
f
uz
z
y
t
y
pe
-
1
s
y
s
t
e
m
i
s
e
m
p
l
o
y
e
d
t
o
f
i
n
d
m
o
r
e
e
dge
p
i
x
e
l
s
t
h
a
n
t
r
a
di
t
i
o
na
l
e
dge
o
pe
r
a
to
r
s
to
i
n
c
r
e
a
s
e
e
m
b
e
dd
i
ng
c
a
pa
c
i
t
y
.
L
a
s
t
l
y
,
f
o
r
t
h
e
s
i
mi
l
a
r
i
t
y
i
nde
x
,
t
h
e
L
S
B
m
e
t
h
o
d
i
s
us
e
d
to
h
i
d
e
t
h
e
s
e
c
r
e
t
m
e
s
s
a
ge
i
n
t
h
e
c
o
v
e
r
i
m
a
ge
,
r
e
s
u
l
t
i
n
g
i
n
a
S
t
e
g
o
i
m
a
g
e
i
de
n
t
i
c
a
l
t
o
t
h
e
c
o
v
e
r
i
m
a
ge
.
T
h
e
pa
pe
r
i
s
o
r
ga
ni
z
e
d
a
s
f
o
l
l
o
w
s
:
i
n
s
e
c
t
i
o
n
2,
we
p
r
o
p
o
s
e
t
h
e
m
s
h
E
dge
R
GB
_T
1
a
l
go
r
i
t
hm
t
o
e
m
b
e
d
a
n
d
e
x
t
r
a
c
t
t
h
e
s
e
c
r
e
t
da
t
a
f
r
o
m
R
GB
i
m
a
g
e
s
b
a
s
e
d
o
n
f
u
z
z
y
t
y
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2.
P
ROP
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E
D
AL
GO
R
I
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HM
S
I
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hi
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c
t
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we
pr
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p
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dge
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B
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pe
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1
M
a
m
da
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F
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to
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m
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Al
go
r
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t
hm
1
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GR
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r
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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do
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N:
2
5
0
2
-
4
7
52
A
nov
e
l
R
GB
image
s
teganogr
aphy
algor
it
hm
us
ing
type
-
1
f
uz
z
y
logi
c
(
N
av
it
a
Dhak
a
)
125
Al
go
r
i
t
hm
1.
m
s
h
E
dge
R
G
B
_T
1
e
m
b
e
dd
i
ng
a
l
go
r
i
t
hm
Input
RGB Cover image, secret message.
Output
RGB Stego image.
Step 1
Read Cover image.
Step 2
Extract
pixel
intensity
values
of
Cover
image
for
all
three
channels
separatel
y
(RED, GREEN, and BLUE).
Step 3
Mask on cover image for each channel.
Step 4
Find
the
difference
of
two
neighbouring
pixe
ls
in
the
horizontal
and
vertical
directions for each channel separately.
Step 5
Create FIS and initialize its input and output variables.
Step 6
Set the rules for FIS.
Step 7
Apply
fuzzification
to
the
image
data
and
defuzzification
to
the
intermediate
re
sults to obtain fuzzyEdgeOut.
Step 8
Use
the
LSB
and
XOR
method
to
hide
secret
message
within
edge
pixels
of
Cover
image
based on fuzzyEdgeOut and obtain Stego image.
Al
go
r
i
t
hm
2
r
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pr
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s
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t
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m
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G
B
_T
1
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h
i
s
a
p
pr
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a
c
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n
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e
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hi
g
h
l
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ve
l
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f
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c
ur
i
t
y
a
n
d
i
m
p
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r
c
e
pt
i
bil
i
t
y
,
m
a
k
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ng
i
t
d
i
f
f
i
c
u
l
t
f
o
r
t
h
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h
u
m
a
n
e
y
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to
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t
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c
t
di
f
f
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r
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n
c
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s
be
t
we
e
n
t
h
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c
o
v
e
r
a
n
d
S
t
e
g
o
i
m
a
ge
s
.
Al
go
r
i
t
hm
2.
m
s
h
E
dge
R
G
B
_T
1
e
x
t
r
a
c
t
i
n
g
a
l
go
r
i
t
hm
Input
RGB Stego image.
Output
RGB Cover image, secret message.
Step 1
Read Stego image.
Step 2
Extract
pixel
intensity
values
of
Stego
image
for
all
three
channels
separatel
y
(RED, GREEN, and BLUE).
Step 3
Find difference of two neighbouring
pixels
in the horizontal and
vertical directions
for each channel.
Step 4
Create a F
IS and initialize its input and output variables.
Step 5
Set the rules for FIS.
Step 6
Apply
fuzzification
to
the
image
data
and
defuzzification
to
the
intermediate
results to obtain fuzzyEdgeOut.
Step 7
Use
the
modulus
method
to
extract
the
sec
ret
message
from
the
edge
pixels
of
Stego
image using fuzzyEdgeOut.
3.
M
E
T
HO
D
T
hi
s
s
e
c
t
i
o
n
de
s
c
r
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be
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t
h
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pe
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c
pr
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hni
qu
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us
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k
to
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po
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c
ti
o
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1.
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h
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m
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G
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be
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s
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pe
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f
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g
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c
s
y
s
t
e
m
a
n
d
L
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m
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t
h
o
d.
3.
1.
M
at
e
r
ial
s
I
n
t
hi
s
pa
pe
r
,
we
wor
ke
d
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s
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n
da
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d
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t
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GB
R
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us
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(
v
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[
16]
.
3.
2.
2
.
F
u
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in
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uz
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[
17]
.
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s
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,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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52
In
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126
li
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pe
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18]
.
T
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[
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]
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l
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a
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be
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i
gur
e
1
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p
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c
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n
put
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put
1
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t
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F
i
gur
e
1.
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n
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d
o
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t
pu
t
v
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F
I
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3.
3.
P
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r
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I
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we
gi
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[
20]
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D
1
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F
i
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e
2.
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a
m
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bl
o
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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A
nov
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127
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=
132
S
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p
4:
c
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d
[
,
,
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3)
f
r
o
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3)
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3
,
3
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(
132
−
172
)
=
40
S
t
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p
5:
n
o
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,
we
n
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e
d
to
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.
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e
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h
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t
t
e
r
n
o
.
09/382(
15462
)
/2022
-
E
M
R
-
I.
RE
F
E
R
E
NC
E
S
[
1]
R
.
M
.
N
e
a
ma
h,
J
.
A
.
A
be
d,
a
nd
E
.
A
.
A
bbo
o
d,
“
H
id
e
te
x
t
d
e
pe
ndi
ng
o
n
th
e
th
r
e
e
c
ha
nne
ls
of
pi
xe
ls
in
c
o
l
o
r
im
a
g
e
s
us
in
g
th
e
mo
di
f
ie
d
L
S
B
a
lg
or
it
hm,”
I
nt
e
r
nat
io
nal
J
our
nal
of
E
le
c
tr
ic
al
and
C
om
put
e
r
E
ngi
ne
e
r
in
g
(
I
J
E
C
E
)
,
vo
l.
10,
n
o
.
1,
pp.
809
–
8
15,
F
e
b. 2020, d
o
i:
10.11591/i
je
c
e
.
v
10i
1.pp809
-
815.
[
2]
D
.
C
.
W
u
a
nd
W
.
H
.
T
s
a
i,
“
A
s
te
ga
no
gr
a
phi
c
m
e
th
o
d
f
o
r
i
ma
ge
s
b
y
pi
xe
l
-
v
a
lu
e
di
f
f
e
r
e
n
c
in
g,”
P
a
tt
e
r
n
R
e
c
ogni
ti
on
L
e
tt
e
r
s
,
vo
l.
24, n
o
. 9
–
10, pp. 1613
–
16
26, J
un. 2003, d
o
i:
10.1016/S
016
7
-
8655(
02)
00402
-
6.
[
3]
R
.
L
uka
c
,
K
.
N
.
P
la
ta
ni
o
ti
s
,
a
nd
A
.
N
.
V
e
ne
ts
a
no
p
o
ui
o
s
,
“
C
o
l
o
r
im
a
g
e
de
n
o
is
in
g
us
in
g
e
vo
lu
ti
o
na
r
y
c
o
mput
a
ti
o
n,”
I
nt
e
r
nat
i
onal
J
our
nal
of
I
m
agi
ng Sy
s
te
m
s
and T
e
c
hnol
ogy
, v
o
l.
15, n
o
. 5, pp.
236
–
251, 200
5, do
i:
10.1002/i
ma
.20058.
[
4]
S
.
P
r
a
s
a
d
a
nd
A
.
K
.
P
a
l,
“
A
n
R
G
B
c
o
l
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ur
im
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g
e
s
t
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no
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phy
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h
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e
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la
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oc
k
-
ba
s
e
d
pi
xe
l
-
v
a
lu
e
di
f
f
e
r
e
nc
i
ng,”
R
oy
al
Soc
ie
ty
O
pe
n Sc
ie
nc
e
, v
o
l.
4, n
o
. 4, p. 161066, Apr
. 2017
, do
i:
10.1098/
r
s
o
s
.161066.
[
5]
H
.
S
.
Y
us
u
f
a
nd
H
.
H
a
gr
a
s
,
“
H
ig
h
pa
y
l
o
a
d
im
a
g
e
s
te
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n
o
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a
ph
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th
o
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us
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f
u
z
z
y
l
o
gi
c
a
nd
e
dg
e
de
t
e
c
ti
o
n,”
I
nt
e
r
nat
io
nal
J
our
nal
of
C
om
put
at
io
nal
Sc
ie
nc
e
, v
o
l.
8, n
o
. 4, pp. 123
–
134, 2
020.
[
6]
D
.
D
o
ngr
e
a
nd
R
.
M
is
hr
a
,
“
A
r
e
v
ie
w
o
n
e
dg
e
ba
s
e
d
im
a
ge
s
te
ga
no
g
r
a
ph
y
,
”
I
nt
e
r
nat
io
nal
J
our
nal
on
R
e
c
e
nt
and
I
nnov
a
ti
on
T
r
e
nds
i
n C
om
put
in
g and C
om
m
uni
c
at
io
n
, vo
l.
2, n
o
. 9, pp. 28
62
–
2866, 2014.
[
7]
R
.
D
e
v
it
a
,
I
.
F
it
r
i,
a
nd
Y
uha
ndr
i,
“
A
na
l
y
s
is
of
gr
a
di
e
nt
ma
gni
tu
de
pa
r
a
m
e
te
r
v
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lu
e
e
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e
de
t
e
c
ti
o
n
o
n
x
-
r
a
y
im
a
g
e
of
c
hi
ld
tu
be
r
c
ul
o
s
is
,”
i
n
A
I
P
C
onf
e
r
e
nc
e
P
r
oc
e
e
di
ngs
, 2024, v
o
l.
3116,
no
. 1, p. 030001, d
o
i:
10.1063/5.0210241
.
[
8]
P
.
M
e
li
n,
O
.
M
e
nd
o
z
a
,
a
nd
O
.
C
a
s
ti
ll
o
,
“
A
n
im
pr
ove
d
m
e
th
o
d
f
or
e
dg
e
d
e
te
c
ti
o
n
ba
s
e
d
o
n
in
t
e
r
v
a
l
t
y
p
e
-
2
f
u
z
z
y
l
o
g
ic
,”
E
x
pe
r
t
Sy
s
te
m
s
w
it
h A
ppl
ic
at
io
ns
, v
o
l.
37, n
o
. 12, pp. 8527
–
85
35, De
c
. 2010, d
oi
:
10.1016/j
.
e
s
w
a
.2010.05.023.
[
9]
E
.
N
a
de
r
n
e
ja
d,
S
.
S
ha
r
i
f
z
a
d
e
h,
a
nd
H
.
H
a
s
s
a
npo
ur
,
“
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dg
e
de
t
e
c
ti
o
n
t
e
c
hni
qu
e
s
:
e
v
a
lu
a
ti
o
ns
a
nd
c
omp
a
r
is
o
ns
,”
A
p
pl
ie
d
M
at
he
m
at
ic
al
Sc
ie
nc
e
s
,
v
o
l.
2,
n
o
.
31,
pp.
1507
–
1520,
2008,
[
O
nl
in
e
]
.
A
v
a
il
a
bl
e
:
ht
tp
:/
/
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r
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/
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39963/da
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ms
/
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il
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_6
265007/c
o
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nt
.
[
10]
T
.
Y
u
v
a
r
a
ja
a
nd
R
.
S
.
S
a
b
e
e
ni
a
n,
“
P
e
r
f
o
r
ma
nc
e
a
na
l
y
s
is
of
m
e
di
c
a
l
im
a
ge
s
e
c
ur
it
y
us
in
g
s
te
ga
n
o
gr
a
ph
y
ba
s
e
d
o
n
f
u
z
z
y
l
o
g
ic
,”
C
lu
s
te
r
C
om
put
in
g
, vo
l.
22, n
o
. S
2, pp. 3285
–
3291, M
a
r
. 2019,
do
i:
10.1007/s
10586
-
018
-
2096
-
0.
[
11]
L
.
T
a
ng,
D
.
W
u,
H
.
W
a
ng,
M
.
C
he
n,
a
nd
J
.
X
i
e
,
“
A
n
a
da
pt
iv
e
f
uz
z
y
in
f
e
r
e
nc
e
a
ppr
o
a
c
h
f
or
c
o
l
or
im
a
g
e
s
te
ga
n
o
gr
a
p
h
y
,”
Sof
t
C
om
put
in
g
, v
o
l.
25, n
o
. 16, pp. 10987
–
11004, Aug. 2021,
do
i:
10.1007/s
00500
-
021
-
05825
-
y.
[
12]
L
.
T
a
ng,
J
.
X
i
e
,
a
nd
D
.
W
u,
“
A
n
in
t
e
r
v
a
l
t
y
p
e
-
2
f
u
z
z
y
e
dg
e
de
t
e
c
ti
o
n
a
nd
ma
tr
i
x
c
o
d
in
g
a
ppr
o
a
c
h
f
o
r
c
o
l
or
im
a
g
e
a
da
pt
iv
e
s
te
ga
no
g
r
a
ph
y
,”
M
ul
ti
m
e
di
a T
ool
s
and A
ppl
ic
at
io
ns
,
vo
l.
81, n
o
. 27, pp. 39145
–
39167, 2022, d
o
i:
10.1007/s
11042
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022
-
13127
-
0.
[
13]
S
. N
. M
. A
l
-
F
a
y
d
i,
S
.
K
. A
hm
e
d, a
nd
H
. N
. Y
. A
l
-
T
a
lb
, “
I
mpr
ove
d
L
S
B
im
a
ge
s
t
e
ga
no
g
r
a
ph
y
w
it
h hi
gh i
mp
e
r
c
e
pt
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li
t
y
ba
s
e
d
o
n
c
ove
r
-
s
t
e
g
o
ma
tc
hi
ng,”
I
E
T
I
m
age
P
r
oc
e
s
s
i
ng
, v
o
l.
17, n
o
. 7, p
p.
2072
–
2082, M
a
y
2023, d
o
i
:
10.1049/i
pr
2.12773.
[
14]
K
.
M
uha
mm
a
d,
J
.
A
hma
d,
N
.
U
.
R
e
hma
n,
Z
.
J
a
n,
a
nd
M
.
S
a
j
ja
d,
“
C
I
S
S
K
A
-
L
S
B
:
c
o
l
or
im
a
ge
s
t
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ga
no
g
r
a
ph
y
us
in
g
s
te
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o
k
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-
di
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c
t
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d
a
da
pt
i
v
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L
S
B
s
ubs
ti
tu
ti
o
n
m
e
th
o
d,”
M
ul
ti
m
e
di
a
T
oo
ls
and
A
ppl
ic
at
io
ns
,
vol
.
76,
n
o
.
6,
pp.
8597
–
8626,
M
a
r
.
20
17,
do
i:
10.1007/s
11042
-
016
-
3383
-
5.
[
15]
S
.
K
uma
r
,
S
.
H
i
r
a
nw
a
l,
S
.
D
.
P
ur
o
hi
t,
a
nd
M
.
P
r
a
s
a
d,
P
r
oc
e
e
di
ngs
o
f
I
nt
e
r
nat
io
nal
C
on
fe
r
e
n
c
e
on
C
om
m
uni
c
at
io
n
and
C
om
put
at
io
nal
T
e
c
hnol
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e
s
:
I
C
C
C
T
2023
. S
in
ga
po
r
e
:
S
pr
in
g
e
r
N
a
tu
r
e
S
in
ga
p
o
r
e
, 2023.
[
16]
M
.
A
.
A
s
la
m
e
t
al
.
,
“
I
ma
ge
s
te
ga
no
g
r
a
ph
y
us
in
g
l
e
a
s
t
s
ig
ni
f
i
c
a
nt
b
it
(
L
S
B
)
-
a
s
y
s
t
e
ma
ti
c
li
t
e
r
a
tu
r
e
r
e
v
i
e
w
,”
in
P
r
oc
e
e
di
ngs
o
f
2022
2nd
I
nt
e
r
nat
io
nal
C
onf
e
r
e
nc
e
on
C
om
put
in
g
and
I
nf
or
m
at
io
n
T
e
c
hnol
ogy
,
I
C
C
I
T
2022
,
J
a
n.
2022,
pp.
32
–
38,
do
i:
10.1109/
I
C
C
I
T
52419.2022.9711628.
[
17]
H
.
H
u
ma
ir
a
,
R
.
R
a
s
y
id
a
h,
a
nd
I
.
R
a
hma
y
un
i,
“
D
e
s
ig
ni
ng
ma
mda
ni
f
u
z
z
y
in
f
e
r
e
n
c
e
s
y
s
te
ms
f
o
r
d
e
c
is
io
n
s
uppor
t
s
y
s
te
ms
,”
in
P
r
oc
e
e
di
ngs
of
I
C
A
I
T
I
2019
-
2nd
I
nt
e
r
nat
io
nal
C
onf
e
r
e
nc
e
on
A
ppl
ie
d
I
nf
or
m
at
io
n
T
e
c
hnol
ogy
a
nd
I
nnov
at
io
n:
E
x
pl
or
in
g
th
e
F
u
tu
r
e
T
e
c
hnol
ogy
o
f
A
ppl
ie
d
I
nf
o
r
m
at
io
n
T
e
c
h
nol
ogy
and
I
nnov
at
io
n
,
S
e
p.
2019,
pp.
111
–
115,
do
i:
10.1109/
I
C
A
I
T
I
48442.2019.8982153.
[
18]
“
M
A
T
H
W
O
R
K
S
-
M
a
mda
ni
a
nd
S
uge
no
f
uz
z
y
in
f
e
r
e
n
c
e
s
y
s
te
ms
,”
2024.
M
a
mda
ni
a
nd
S
uge
no
F
u
z
z
y
i
n
f
e
r
e
nc
e
s
y
s
t
e
m
s
-
M
A
T
L
A
B
& S
im
u
li
nk
-
M
a
th
W
or
ks
I
ta
li
a
(
a
c
c
e
s
s
e
d A
pr
. 20, 2
024)
.
[
19]
S
.
T
oma
s
ie
ll
o
,
W
.
P
e
d
r
y
c
z
,
a
nd
V
.
L
o
ia
,
“
F
uz
z
y
in
f
e
r
e
n
c
e
s
y
s
t
e
ms
.
in
:
c
o
nt
e
mp
o
r
a
r
y
f
u
z
z
y
l
o
gi
c
,”
A
r
ti
f
ic
ia
l
I
nt
e
ll
ig
e
nc
e
,
vo
l.
1, 2022, d
o
i:
10.1007/978
-
3
-
030
-
98974
-
35.
[
20]
E
.
Z
i
e
li
ns
ka
,
W
.
M
a
z
ur
c
z
y
k,
a
nd
K
.
S
z
c
z
y
pi
o
r
s
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