I
n
t
e
r
n
at
ion
al
Jou
r
n
al
of
E
lec
t
r
ical
an
d
Com
p
u
t
e
r
E
n
gin
e
e
r
in
g
(
I
JE
CE
)
Vol.
1
4
,
No.
5
,
Oc
tober
20
2
4
,
pp
.
5153
~
5171
I
S
S
N:
2088
-
8708
,
DO
I
:
10
.
11591/i
jec
e
.
v
1
4
i
5
.
pp
5
153
-
5171
5153
Jou
r
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tp:
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AB
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ti
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:
R
e
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J
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2024
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e
vis
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M
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y
27,
2024
Ac
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4,
2024
In
t
o
d
a
y
’s
w
o
r
l
d
,
t
h
e
n
eed
f
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r
h
i
g
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ev
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s
o
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t
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t
ran
s
ferri
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d
at
a
h
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s
b
ec
o
me
a
k
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y
co
n
cern
.
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i
s
e
s
s
e
n
t
i
al
t
o
s
afe
g
u
ar
d
d
a
t
a
fro
m
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o
t
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t
i
a
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k
s
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o
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p
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d
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i
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l
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t
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.
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h
eref
o
re,
t
o
p
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ec
t
cri
t
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cal
an
d
co
n
f
i
d
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t
i
al
s
a
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l
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e
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v
an
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cry
p
t
i
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t
an
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(
A
E
S).
T
h
e
p
r
o
p
o
s
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d
ap
p
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ac
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a
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ms
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o
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f
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el
l
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t
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mag
er
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w
h
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l
e
mai
n
t
a
i
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n
g
effi
ci
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d
ro
b
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s
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n
es
s
ag
ai
n
s
t
v
ar
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s
a
t
t
ac
k
s
.
It
p
o
s
s
e
s
s
e
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s
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v
eral
ap
p
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l
y
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ev
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y
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a
l
a
rg
e
k
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p
ace,
an
d
res
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l
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e
n
ce
t
o
s
i
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g
l
e
ev
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t
u
p
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et
s
(SE
U
s
)
an
d
t
ra
n
s
m
i
s
s
i
o
n
erro
rs
.
T
o
e
v
al
u
at
e
t
h
e
p
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o
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ce
o
f
t
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p
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p
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s
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d
en
cr
y
p
t
i
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e
ch
n
i
q
u
e,
ex
t
e
n
s
i
v
e
ex
p
er
i
men
t
s
h
a
v
e
b
een
co
n
d
u
ct
e
d
b
y
co
n
s
i
d
er
i
n
g
fact
o
rs
s
u
ch
as
s
ec
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ri
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y
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ev
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l
,
res
i
s
t
a
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ce
t
o
SE
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s
,
a
n
d
c
o
mp
u
t
a
t
i
o
n
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l
effi
ci
en
c
y
.
O
u
r
re
s
u
l
t
s
d
emo
n
s
t
rat
e
t
h
a
t
t
h
e
p
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p
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s
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d
met
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o
d
ach
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h
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g
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v
el
o
f
s
ecu
r
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y
an
d
a
l
arg
e
k
e
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s
p
ace,
en
s
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ri
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g
t
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co
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f
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d
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t
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g
ri
t
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o
f
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t
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l
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at
a.
Fu
r
t
h
erm
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t
h
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met
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b
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t
s
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l
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ag
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n
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t
SE
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s
an
d
t
ran
s
mi
s
s
i
o
n
erro
r
s
,
an
d
o
ffer
s
effi
c
i
en
t
p
r
o
ces
s
i
n
g
,
m
ak
i
n
g
i
t
s
u
i
t
a
b
l
e
f
o
r
real
-
w
o
rl
d
ap
p
l
i
ca
t
i
o
n
s
.
K
e
y
w
o
r
d
s
:
Adva
nc
e
d
e
nc
r
ypti
on
s
tanda
r
d
C
ha
oti
c
s
ys
tem
I
mage
e
nc
r
ypti
on
S
a
telli
te
im
a
ge
r
y
S
c
r
a
mbl
ing
Th
i
s
i
s
a
n
o
p
en
a
c
ces
s
a
r
t
i
c
l
e
u
n
d
e
r
t
h
e
CC
B
Y
-
SA
l
i
ce
n
s
e.
C
or
r
e
s
pon
din
g
A
u
th
or
:
Oma
r
B
e
nc
hikh
C
omm
unica
ti
on
Ne
twor
ks
,
Ar
c
hit
e
c
tur
e
a
nd
M
ult
i
media
L
a
bor
a
tor
y,
De
pa
r
tm
e
nt
of
E
lec
tr
onics
,
Djil
lali
L
iabe
s
Unive
r
s
it
y
of
S
idi
B
e
l
Abbe
s
S
idi
B
e
l
Abbe
s
,
Alge
r
ia
E
mail:
omar
b022@ho
tm
a
il
.
c
om
1.
I
NT
RODU
C
T
I
ON
Now
a
da
ys
,
huge
qua
nti
ti
e
s
of
va
r
ious
types
of
digi
tal
da
ta
including
text,
im
a
ge
,
a
nd
v
ideo
a
r
e
e
xc
ha
nge
d
thr
ough
dif
f
e
r
e
nt
kinds
of
c
omm
un
ica
ti
on
ne
twor
ks
.
S
ubjec
t
to
pos
s
ibl
e
high
r
is
ks
,
s
uc
h
inf
or
mation
mus
t
be
p
r
otec
ted
a
nd
s
e
c
ur
e
d
be
c
a
us
e
it
c
ontains
pr
ivate
a
nd
c
onf
idential
da
ta.
I
n
pa
r
ti
c
ular
,
e
a
r
th
obs
e
r
va
ti
on
s
a
telli
tes
(
E
OS
)
a
nd
r
e
mot
e
s
e
ns
ing
(
R
S
)
a
r
e
dis
r
upted
by
r
a
ndom
o
r
int
e
nti
ona
l
a
tt
a
c
ks
on
im
a
ge
s
.
S
ince
digi
tal
im
a
ge
s
a
r
e
tr
a
ns
mi
tt
e
d
on
dif
f
e
r
e
nt
kinds
of
ne
twor
ks
,
their
s
e
c
ur
it
y
be
c
omes
a
ne
c
e
s
s
it
y
[
1]
.
R
e
f
e
r
r
ing
to
the
US
C
omput
e
r
S
e
c
ur
it
y
I
ns
ti
tut
e
s
tudy
[
2]
,
int
e
r
na
l
una
utho
r
ize
d
f
il
e
a
c
c
e
s
s
a
c
c
ounts
f
or
59
%
of
thr
e
a
ts
,
he
nc
e
the
ne
e
d
f
or
e
f
f
icie
nt
mea
ns
to
s
olve
thi
s
pr
ob
lem
a
nd
e
ns
ur
e
the
s
e
c
ur
it
y
of
inf
o
r
mation
s
ys
tems
.
Among
the
s
e
c
ur
it
y
tec
hni
que
s
e
mpl
oye
d,
c
r
yptog
r
a
phy
is
the
mos
t
wide
ly
u
s
e
d
wa
y
to
e
ns
ur
e
the
pr
ivac
y,
int
e
gr
it
y
or
a
uthentica
ti
on
of
text
a
nd
im
a
ge
da
ta
tr
a
ns
mi
s
s
ion
pr
oc
e
s
s
e
s
a
n
d
s
tor
a
ge
f
r
om
una
ppr
ove
d
us
e
r
s
.
T
his
c
omm
on
tec
hnique,
ba
s
e
d
on
two
f
unc
ti
ons
c
a
ll
e
d
e
nc
r
ypti
on
a
nd
de
c
r
ypti
on,
ha
s
f
ound
a
gr
e
a
t
im
pa
c
t
a
nd
be
c
a
me
a
n
int
e
r
e
s
ti
ng
r
e
s
e
a
r
c
h
theme
in
the
a
r
e
a
of
inf
o
r
mation
s
e
c
ur
it
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
1
4
,
No.
5
,
Oc
tober
2
02
4
:
5153
-
5171
5154
[
3]
–
[
5]
.
F
ur
the
r
mor
e
,
e
nc
r
ypti
on
is
a
ls
o
us
e
d
to
e
ns
ur
e
inf
or
mation
tr
a
ns
mi
s
s
ion
in
c
omm
unica
ti
on
n
e
twor
ks
.
T
his
tas
k
c
a
n
be
pe
r
f
o
r
med
by
us
ing
s
tanda
r
d
im
a
ge
e
nc
r
ypti
on
a
lgor
it
h
ms
s
uc
h
a
s
digi
tal
e
n
c
r
ypti
on
s
tanda
r
d
(
DE
S
)
,
int
e
r
na
ti
ona
l
da
ta
e
nc
r
ypti
on
a
lgo
r
it
hm
(
I
DE
A)
,
t
r
ipl
e
da
ta
e
nc
r
ypti
on
s
tanda
r
d
(3
-
DE
S
)
,
a
nd
a
dva
nc
e
d
e
nc
r
ypti
on
s
tanda
r
d
(
AE
S
)
[
6]
.
How
e
v
e
r
,
DE
S
a
nd
I
D
E
A
pos
e
c
ha
ll
e
nge
s
due
to
their
int
r
ica
te
na
tur
e
a
nd
s
low
pe
r
f
o
r
manc
e
whe
n
tas
ke
d
with
e
nc
r
ypti
ng
lar
ge
s
a
telli
te
im
a
ge
da
tas
e
ts
.
M
or
e
ov
e
r
,
both
DE
S
a
nd
3
-
DE
S
e
xhibi
t
s
e
c
ur
it
y
vulner
a
bil
i
ti
e
s
a
tt
r
ibut
a
ble
to
thei
r
li
mi
ted
ke
y
s
ize
.
Among
thes
e
e
n
c
r
ypti
on
a
lgor
it
hms
,
AE
S
is
c
ons
ider
e
d
a
s
a
ne
w
ge
ne
r
a
ti
on
of
wo
r
ld
e
nc
r
ypti
on
s
tanda
r
d
that
wa
s
a
dopted
by
the
Na
ti
ona
l
I
ns
ti
tut
e
o
f
S
tanda
r
ds
a
nd
T
e
c
hnology
(
NI
S
T
)
[
7]
.
I
n
the
las
t
two
de
c
a
de
s
,
AE
S
ha
s
b
e
e
n
mos
t
c
omm
only
us
e
d
a
s
a
n
e
xc
e
ll
e
nt
c
hoice
f
or
many
c
r
it
ica
l
a
ppli
c
a
ti
ons
s
e
e
king
c
onf
identialit
y
a
nd
the
s
e
c
ur
e
e
xc
ha
nge
of
inf
or
mation
s
uc
h
a
s
s
a
telli
te
im
a
ge
r
y
[
8]
,
[
9]
.
T
he
s
e
c
las
s
ica
l
methods
,
while
e
f
f
e
c
ti
ve
in
c
e
r
tain
c
ontexts
,
may
e
xhibi
t
d
r
a
wba
c
ks
whe
n
a
ppli
e
d
to
l
a
r
ge
-
s
ize
or
bulky
s
a
telli
te
im
a
ge
da
ta.
I
n
the
pa
s
t
twe
nty
ye
a
r
s
,
c
ha
oti
c
maps
ha
ve
e
mer
g
e
d
a
s
a
viable
tool
f
or
im
a
ge
e
nc
r
ypti
on,
thanks
to
their
a
tt
r
a
c
ti
ve
c
ha
r
a
c
ter
is
ti
c
s
s
uc
h
a
s
ps
e
udo
-
r
a
ndomnes
s
,
unpr
e
dicta
bil
it
y,
e
r
godicity,
a
nd
s
e
ns
it
ivi
ty
to
ini
ti
a
l
c
ondit
ions
[
10]
,
[
11]
.
C
ha
oti
c
maps
pr
ovide
oppor
tuni
ti
e
s
f
or
de
ve
lopi
ng
nove
l
im
a
ge
e
nc
r
ypti
on
methodologi
e
s
or
modi
f
ying
c
onve
nti
ona
l
a
lgor
it
hms
[
12]
,
[
13]
.
A
c
omp
r
e
he
ns
ive
r
e
view
of
r
e
c
e
nt
s
tudi
e
s
on
im
a
ge
da
ta
s
e
c
ur
it
y
a
nd
c
r
yptogr
a
phic
a
na
lys
is
is
pr
e
s
e
nted
in
[
8]
.
E
xis
ti
ng
c
r
yptog
r
a
phic
s
ys
tems
uti
li
z
ing
c
ha
oti
c
m
a
ps
c
a
n
be
divi
de
d
int
o
two
main
c
a
tegor
ies
:
one
-
dim
e
ns
ional
(
1
-
D)
a
nd
mul
ti
-
dim
e
ns
ional
(
M
-
D)
c
ha
oti
c
maps
.
C
ha
oti
c
dyna
mi
c
s
of
c
e
r
tain
1
-
D
c
ha
oti
c
maps
li
ke
the
L
ogis
ti
c
a
nd
S
ine
maps
a
r
e
of
ten
de
e
med
ins
uf
f
icie
ntl
y
s
e
c
ur
e
due
to
inher
e
nt
li
mi
tations
s
temmi
ng
f
r
om
the
p
r
e
dicta
ble
na
tur
e
of
thei
r
s
im
ple
or
bit
s
a
nd
the
a
bil
it
y
to
f
or
e
c
a
s
t
their
c
ont
r
ol
a
nd
ini
ti
a
l
pa
r
a
mete
r
s
[
14]
.
C
ons
e
que
ntl
y,
c
las
s
ica
l
1
-
D
c
ha
o
ti
c
maps
a
r
e
de
e
med
uns
uit
a
ble
f
or
i
mage
e
nc
r
ypti
on.
As
a
r
e
s
ult
,
numer
ous
e
nde
a
vor
s
ha
ve
be
e
n
unde
r
take
n
to
pr
opos
e
nove
l
M
-
D
c
ha
oti
c
maps
to
e
na
ble
r
a
pid
a
nd
highl
y
s
e
c
ur
e
im
a
ge
e
nc
r
ypt
ion
[
15]
.
C
ompar
e
d
to
1
-
D
c
ha
oti
c
maps
,
thes
e
c
ompl
e
x
s
c
he
mes
e
xhibi
t
gr
e
a
ter
unpr
e
dicta
bil
it
y
a
nd
s
upe
r
ior
c
ha
oti
c
be
ha
vior
,
r
e
nde
r
ing
them
mo
r
e
s
uit
a
ble
f
or
da
ta
e
nc
r
ypti
on
[
13]
,
[
16]
.
How
e
ve
r
,
the
main
l
im
it
a
ti
ons
o
f
M
-
D
c
ha
oti
c
ma
ps
f
or
im
a
ge
e
nc
r
ypti
on
include
c
omput
a
ti
ona
l
c
o
mpl
e
xit
y,
high
dim
e
ns
ionalit
y,
a
nd
c
ha
ll
e
nge
s
in
ke
y
mana
ge
ment.
S
e
c
ur
i
ty
a
na
lys
is
c
a
n
be
c
ompl
e
x
a
nd
may
im
pa
c
t
e
nc
r
ypti
on
pe
r
f
o
r
manc
e
a
nd
the
ove
r
a
ll
s
e
c
ur
it
y
o
f
the
s
ys
tem.
I
n
the
c
ontext
of
im
a
ge
e
nc
r
ypti
on
,
whe
r
e
the
c
o
nf
identialit
y
a
nd
int
e
gr
it
y
of
da
ta
a
r
e
c
r
i
ti
c
a
l,
the
int
e
gr
a
ti
on
of
AE
S
with
c
ha
oti
c
tec
hniques
holds
pa
r
ti
c
ular
s
igni
f
ica
nc
e
[
13]
,
[
17]
,
[
18]
.
I
n
p
a
r
ti
c
ular
,
s
a
telli
te
im
a
ge
r
y
o
f
ten
c
ontains
s
e
ns
it
ive
inf
or
mat
ion
r
e
late
d
to
na
ti
ona
l
s
e
c
ur
it
y
,
e
nvi
r
onmenta
l
mo
nit
or
ing,
dis
a
s
ter
mana
ge
ment,
a
nd
other
a
ppli
c
a
ti
ons
,
ne
c
e
s
s
it
a
ti
ng
r
obus
t
e
nc
r
ypti
on
mec
ha
nis
ms
to
pr
otec
t
a
ga
ins
t
una
uthor
ize
d
a
c
c
e
s
s
a
nd
tampe
r
ing
[
19]
–
[
21]
.
R
e
c
e
nt
de
ve
lopm
e
nts
in
s
a
telli
te
i
mage
e
nc
r
ypt
ion
ha
ve
e
xplor
e
d
nove
l
a
ppr
oa
c
he
s
that
c
ombi
ne
AE
S
wit
h
c
ha
oti
c
tec
hniques
to
a
c
hieve
high
s
e
c
ur
it
y
lev
e
ls
[
13]
,
[
22]
,
[
23]
.
B
y
incor
por
a
ti
ng
c
ha
oti
c
dyna
mi
c
s
int
o
e
nc
r
ypti
on
a
lgor
it
hms
,
s
uc
h
a
s
AE
S
,
r
e
s
e
a
r
c
he
r
s
a
im
to
int
r
oduc
e
a
ddit
ional
laye
r
s
of
r
a
ndomnes
s
a
nd
c
ompl
e
xit
y
,
making
e
nc
r
ypted
da
ta
mo
r
e
r
e
s
is
tant
to
c
r
yptogr
a
phic
a
tt
a
c
ks
.
T
o
incr
e
a
s
e
the
s
e
c
ur
it
y
leve
l
of
AE
S
,
a
c
ombi
na
t
ion
of
AE
S
a
nd
c
ha
os
us
ing
Ar
nold’
s
c
a
t
map
to
s
huf
f
le
pixel
va
lues
a
nd
He
non
map
to
ge
ne
r
a
t
e
r
a
ndom
s
e
que
nc
e
s
wa
s
pr
opos
e
d
f
o
r
s
a
telli
te
im
a
ge
r
y
e
nc
r
ypti
on
[
22]
.
Anothe
r
a
ppr
oa
c
h
ba
s
e
d
on
the
c
ombi
na
ti
on
of
a
c
us
tom
ize
d
ve
r
s
ion
of
the
AE
S
a
nd
the
Ar
nold
c
a
t
map
to
de
r
ive
the
e
nc
r
ypti
on
ke
y
wa
s
int
r
oduc
e
d
in
[
24]
.
S
im
il
a
r
ly,
A
c
ha
oti
c
s
ys
tem
with
f
our
dim
e
ns
ions
wa
s
uti
li
z
e
d
to
pr
oduc
e
ke
ys
a
nd
e
nha
nc
e
the
AE
S
s
tanda
r
d
[
25]
.
Als
o,
a
n
a
djus
t
e
d
AE
S
c
r
yptogr
a
phic
s
ys
tem
f
e
a
tur
ing
dyna
mi
c
r
a
ndom
ke
ys
de
r
ived
f
r
om
c
ha
os
s
ync
hr
oniza
ti
on
wa
s
pr
o
pos
e
d
in
[
18]
.
W
hil
e
thes
e
e
nha
nc
e
ments
s
tr
e
ngthen
e
nc
r
yp
ti
on
ke
ys
,
they
c
ome
with
a
s
igni
f
ica
nt
c
omput
a
ti
o
na
l
load,
making
them
les
s
s
uit
a
ble
f
or
pr
oc
e
s
s
ing
lar
ge
s
a
telli
te
im
a
ge
s
.
An
im
pleme
ntation
o
f
c
ha
os
-
AE
S
c
ombi
na
ti
on
wa
s
pr
opos
e
d
to
r
e
duc
e
the
pr
oc
e
s
s
ing
ti
me
a
nd
a
dhe
r
e
to
the
ti
me
-
s
e
ns
it
ive
r
e
quir
e
ments
[
8]
.
I
n
thi
s
im
pleme
ntation,
a
c
or
r
e
c
ti
on
s
tr
a
tegy
ba
s
e
d
on
the
pr
ope
r
ti
e
s
of
c
ha
os
theor
y
is
s
ugge
s
ted
to
a
ddr
e
s
s
the
inhe
r
e
nt
li
m
it
a
ti
ons
of
the
AE
S
a
lgor
it
hm
pa
r
a
mete
r
s
[
8]
.
Anothe
r
e
f
f
icie
nt
c
ha
os
-
ba
s
e
d
e
nc
r
ypti
on
method
wa
s
pr
opos
e
d
to
e
nc
r
ypt
mul
ti
s
pe
c
tr
a
l
s
a
telli
t
e
im
a
ge
s
onboa
r
d
E
a
r
th
obs
e
r
va
ti
on
s
a
telli
tes
[
12]
.
T
he
e
x
pe
r
im
e
ntal
r
e
s
ult
s
de
mons
tr
a
te
that
the
pr
opos
e
d
a
ppr
oa
c
h
a
c
hieve
s
a
s
a
ti
s
f
a
c
tor
y
leve
l
of
s
e
c
ur
it
y
with
mi
nim
a
l
ha
r
dwa
r
e
c
ompl
e
xit
y,
powe
r
c
ons
umpt
ion,
a
nd
c
omput
a
ti
on
ti
me.
How
e
ve
r
,
the
s
ys
tem
may
be
s
us
c
e
pti
ble
to
s
ingl
e
e
ve
nt
ups
e
ts
(
S
E
Us
)
induce
d
by
na
tur
a
l
s
pa
c
e
r
a
diation.
T
he
s
e
e
nc
r
ypti
on
methods
a
r
e
inade
qua
te
f
or
s
e
c
u
r
ing
im
a
ge
s
onboa
r
d
r
e
mot
e
s
e
ns
ing
s
a
telli
te
s
due
to
the
a
va
lanc
he
e
f
f
e
c
t,
whic
h
is
a
c
r
uc
ial
pr
ope
r
ty
in
e
nc
r
ypti
on
whe
r
e
a
s
mall
c
ha
nge
in
the
input
,
s
uc
h
a
s
a
s
ingl
e
bit
,
r
e
s
ult
s
in
a
s
igni
f
ica
nt
c
ha
nge
in
the
output
.
F
ur
ther
mor
e
,
given
that
r
e
mot
e
s
e
ns
ing
s
a
telli
tes
f
unc
ti
on
with
in
a
c
ha
ll
e
nging
r
a
diation
e
nvir
onm
e
nt,
a
ll
onboa
r
d
e
lec
tr
onics
,
including
e
nc
r
ypti
on
s
ys
tems
,
a
r
e
vulner
a
ble
to
r
a
diation
-
induce
d
de
f
e
c
ts
[
26]
.
C
ons
e
que
ntl
y,
ther
e
is
a
ne
e
d
f
or
e
nc
r
ypti
on
tec
hniq
ue
s
that
a
c
hieve
a
c
ompr
omi
s
e
be
twe
e
n
c
omput
a
ti
ona
l
e
f
f
icie
nc
y,
s
e
c
ur
it
y
s
tr
e
ngth,
a
nd
s
uit
a
bil
it
y
f
o
r
e
nc
r
ypti
ng
lar
ge
-
s
c
a
l
e
s
a
telli
te
im
a
ge
da
ta
in
a
ha
r
s
h
r
a
diation
e
nvir
onment.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
Satelli
te
image
e
nc
r
y
pti
on
us
ing
2D
s
tandar
d
map
and
adv
anc
e
d
e
nc
r
y
pti
on
…
(
Om
ar
B
e
nc
hikh
)
5155
I
n
thi
s
wo
r
k,
a
nove
l
e
nc
r
ypti
on
s
c
he
me,
ba
s
e
d
o
n
the
us
e
o
f
c
ha
os
c
ombi
ne
d
with
a
n
inf
or
mation
s
c
r
a
mbl
ing
pr
oc
e
s
s
,
is
pr
opos
e
d
to
e
nha
n
c
e
the
s
e
c
ur
it
y
leve
l
of
AE
S
.
F
ir
s
tl
y
,
the
pixels
of
the
va
r
io
us
ba
nds
of
the
o
r
igi
na
l
s
a
telli
te
i
mage
unde
r
go
a
meticulo
us
s
c
r
a
mbl
ing
ope
r
a
ti
on.
S
ubs
e
que
ntl
y,
e
mpl
oyin
g
the
2D
S
tanda
r
d
map,
the
s
c
r
a
mbl
e
d
ba
nds
unde
r
go
the
s
e
c
ond
s
tep
of
e
nc
r
ypti
on.
F
inally,
the
e
nc
r
ypti
on
pr
oc
e
s
s
c
onti
nue
s
with
the
a
ppli
c
a
ti
on
of
the
AE
S
a
lgor
it
h
m
ba
s
e
d
on
the
c
ounter
(
C
T
R
)
mode.
T
his
method
e
ns
ur
e
s
c
ompr
e
he
ns
ive
da
ta
s
e
c
ur
it
y,
de
mons
tr
a
ti
ng
a
s
tr
uc
tur
e
d
a
nd
e
f
f
e
c
ti
ve
a
ppr
oa
c
h
to
s
a
f
e
gua
r
ding
s
e
ns
it
ive
s
a
telli
te
im
a
ge
r
y.
M
or
e
ove
r
,
it
a
c
hieve
s
a
ba
lanc
e
be
twe
e
n
c
omput
a
ti
ona
l
e
f
f
icie
nc
y,
s
e
c
ur
it
y
s
tr
e
ngth,
a
nd
s
uit
a
bil
it
y
f
or
e
nc
r
ypti
ng
la
r
ge
-
s
c
a
le
s
a
telli
te
im
a
g
e
da
ta
withi
n
a
ha
r
s
h
r
a
diation
e
nvir
onment.
T
he
main
c
ont
r
ibut
ions
of
thi
s
pa
pe
r
c
a
n
be
outl
ine
d
a
s
f
oll
ows
:
−
W
e
int
r
oduc
e
a
nove
l
s
upe
r
-
e
nc
r
ypti
on
tec
hniqu
e
that
c
ombi
ne
s
im
a
ge
s
c
r
a
mbl
ing
,
2D
s
tanda
r
d
map
-
ba
s
e
d
c
ha
oti
c
e
nc
r
ypti
on,
a
nd
C
T
R
mode
to
bols
ter
the
s
e
c
ur
it
y
leve
l
of
AE
S
.
−
W
e
c
onduc
t
a
c
ompr
e
he
ns
ive
pe
r
f
or
manc
e
e
va
luation
a
nd
c
ompar
is
on
of
ou
r
pr
opos
e
d
method
a
ga
ins
t
c
onve
nti
ona
l
im
a
ge
e
nc
r
ypti
on
a
ppr
oa
c
he
s
.
Our
f
indi
ngs
de
mons
tr
a
te
that
the
a
lgo
r
it
hm
of
f
e
r
s
f
a
s
t
pr
oc
e
s
s
ing,
high
s
e
c
ur
it
y,
a
nd
r
obus
t
r
e
s
is
tanc
e
a
ga
ins
t
br
ute
-
f
or
c
e
,
c
hos
e
n
a
nd
known
plai
ntext,
s
tatis
ti
c
a
l,
a
nd
dif
f
e
r
e
nti
a
l
a
tt
a
c
ks
.
T
he
pr
opos
e
d
a
lgor
it
hm
is
a
ls
o
tol
e
r
a
nt
to
S
E
Us
a
nd
t
r
a
ns
mi
s
s
ion
e
r
r
or
s
.
T
his
pa
pe
r
is
or
ga
nize
d
a
s
f
oll
ows
:
the
theor
e
ti
c
a
l
pr
inciple
of
A
E
S
is
p
r
e
s
e
nted
in
s
e
c
ti
on
2
,
while
the
pr
opos
e
d
tec
hnique
is
de
s
c
r
ibed
in
s
e
c
ti
on
3.
De
tailed
e
xpe
r
im
e
ntal
r
e
s
ult
s
wil
l
be
r
e
por
ted
a
nd
dis
c
us
s
e
d
in
s
e
c
ti
on
4.
F
inally,
a
s
umm
a
r
y
of
the
pr
e
s
e
nt
wor
k
will
be
given
in
s
e
c
ti
on
5
.
2.
B
AC
KG
ROUN
D
I
NF
ORM
AT
I
ON
2.
1
AE
S
d
e
s
c
r
ip
t
ion
S
ince
the
be
ginni
ng
of
the
2000s
,
s
e
ve
r
a
l
c
ha
oti
c
e
nc
r
ypti
on
methods
ha
ve
be
e
n
us
e
d
to
c
r
e
a
te
c
onf
us
ion
a
nd
dif
f
us
ion
p
r
ope
r
ti
e
s
f
or
s
ymm
e
tr
i
c
c
ipher
s
with
the
a
im
to
e
ns
ur
e
high
leve
l
in
f
or
mation
s
e
c
ur
it
y
[
27]
,
[
28
]
.
C
ons
e
que
ntl
y,
in
a
n
im
a
ge
e
nc
r
ypti
on
pr
oc
e
s
s
,
the
c
on
f
us
ion
ope
r
a
ti
on
obs
c
u
r
e
s
pixel
pa
tt
e
r
ns
withi
n
the
im
a
ge
by
r
a
ndoml
y
s
huf
f
li
ng
the
pos
it
ions
of
pixels
.
T
he
r
e
f
o
r
e
,
the
a
c
tual
loca
ti
ons
of
thos
e
pixels
a
r
e
c
ha
nge
d
in
the
dif
f
us
ion
ope
r
a
t
ion.
R
e
c
e
ntl
y,
the
AE
S
a
lgo
r
it
hm
is
s
tr
ong
ly
us
e
d
a
s
a
s
ymm
e
tr
ic
ke
y
e
nc
r
ypt
ion
s
tanda
r
d
.
A
E
S
is
a
ls
o
c
a
ll
e
d
“
R
ij
nda
e
l
a
lgor
it
hm”
in
r
e
f
e
r
e
nc
e
to
it
s
de
s
igner
s
Vinc
e
nt
R
ij
men
a
nd
J
oa
n
Da
e
men
.
AE
S
is
a
s
ymm
e
tr
ic
ke
y
block
c
ipher
,
whic
h
e
nc
r
ypts
inf
or
mation
us
ing
a
n
it
e
r
a
ti
on
pr
oc
e
s
s
of
tr
a
ns
f
or
mation
r
ounds
c
a
ll
e
d
s
ubs
ti
tut
e
bytes
(
S
ubB
ytes
)
,
s
hif
t
r
ows
(
S
hi
f
tR
ows
)
,
m
ix
c
olum
ns
(
M
ixC
olum
ns
)
a
nd
a
dd
r
ound
ke
y
(
AddR
ound
Ke
y)
[
29
]
.
M
or
e
ove
r
,
AE
S
is
a
r
ound
-
ba
s
e
d
e
nc
r
ypti
on
a
lgor
it
hm
ha
ndli
ng
blocks
of
128
bi
ts
a
nd
us
ing
k
e
ys
of
128,
192
o
r
256
bit
s
that
s
e
t
the
nu
mber
o
f
r
ounds
to
10,
12,
or
14
,
r
e
s
pe
c
ti
ve
ly
[
7]
,
[
30]
a
s
s
hown
in
F
igur
e
1.
T
he
block
diagr
a
m
of
the
A
E
S
e
nc
r
ypti
on
pr
oc
e
s
s
is
de
picte
d
in
F
igu
r
e
1(
a
)
,
while
F
igur
e
1(
b)
r
e
pr
e
s
e
nts
the
de
c
r
ypti
on
pr
oc
e
s
s
.
I
n
a
n
A
E
S
it
e
r
a
ti
on
pr
oc
e
s
s
,
we
dis
ti
nguis
h
f
ou
r
tr
a
ns
f
or
mations
c
a
ll
e
d
laye
r
s
whe
r
e
the
r
ound
ope
r
a
ti
on
is
the
ba
s
ic
unit
.
Als
o,
e
a
c
h
r
ound
ha
s
a
r
ound
ke
y
a
nd
tr
a
ns
f
or
mation
s
teps
that
ge
ne
r
a
te
da
ta
c
a
ll
e
d
S
tate
s
or
ga
nize
d
in
4
×
4
matr
ix
o
f
bytes
a
r
r
a
y
c
ons
is
ti
ng
of
f
ou
r
r
ows
a
nd
number
o
f
c
olum
ns
.
I
n
the
e
nc
r
ypti
on/dec
r
ypti
on
ope
r
a
ti
ons
,
the
da
ta
a
nd
ke
y
blocks
a
r
e
p
r
oc
e
s
s
e
d
thr
ough
Nr
r
ounds
w
hich
a
r
e
obtaine
d
f
r
om
ke
y
s
ize
K
o
f
length
Nk
.
On
the
ot
he
r
ha
nd,
let
us
note
Nc
the
numbe
r
o
f
c
olum
ns
i
n
a
s
tate
matr
ix
a
nd
whic
h
de
pe
nds
on
the
b
lock
s
ize
.
S
ome
va
r
iants
of
ke
ys
,
blocks
,
a
nd
r
ounds
a
r
e
u
s
e
d
a
nd
e
xhibi
ted
in
T
a
ble
1.
T
a
ble
1.
AE
S
c
ombi
na
ti
ons
[
31
]
A
E
S
t
ype
Nk
Nc
Nr
128
4
4
10
192
6
4
12
256
8
4
14
AE
S
ope
r
a
tes
s
uc
h
a
s
a
ne
twor
k
of
s
ubs
ti
tut
ion
a
nd
pe
r
mut
a
ti
on
whe
r
e
the
f
ou
r
f
oll
owing
ba
s
ic
tr
a
ns
f
or
mations
a
r
e
pe
r
f
or
med
a
s
f
oll
ows
:
−
S
ubB
ytes
:
I
n
thi
s
t
r
a
ns
f
or
mation,
e
a
c
h
byte
in
a
b
l
oc
k
is
s
ubs
ti
tut
e
d
by
a
nother
f
r
om
the
s
ubs
ti
tut
ion
-
box
(S
-
box)
a
c
c
or
ding
to
a
lookup
table
.
A
non
li
ne
a
r
s
ubs
ti
tut
ion
in
the
c
ipher
is
a
c
hieve
d
us
ing
the
S
-
box
whic
h
is
obtaine
d
f
r
o
m
the
c
ha
oti
c
map
in
Ga
loi
s
f
ini
te
f
ield
GF(
2
8
)
[
32]
,
[
33]
.
−
S
hif
tR
ows
:
I
n
thi
s
s
tep,
the
f
ir
s
t
r
ow
is
unc
ha
nge
d
a
nd
a
c
ir
c
ular
pe
r
mut
a
ti
on
is
a
ppli
e
d
to
e
a
c
h
of
the
las
t
thr
e
e
block
r
ows
.
He
nc
e
,
e
a
c
h
byte
of
a
r
ow
is
s
hif
ted
to
the
lef
t
by
one
,
two
,
a
nd
thr
e
e
f
o
r
the
s
e
c
ond,
the
thi
r
d
a
nd
the
f
our
th
r
ow
,
r
e
s
pe
c
ti
ve
ly.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
1
4
,
No.
5
,
Oc
tober
2
02
4
:
5153
-
5171
5156
−
M
ixC
olum
ns
:
T
his
tr
a
ns
f
or
mation
is
a
mi
xing
op
e
r
a
ti
on
whic
h
a
f
f
e
c
ts
the
c
olum
ns
whe
r
e
the
f
our
bytes
of
e
a
c
h
c
olum
n
o
f
the
s
tate
a
r
e
c
ombi
ne
d
us
ing
a
li
ne
a
r
tr
a
ns
f
or
mation
.
−
AddR
oundKe
y:
T
his
s
tep
c
ombi
ne
s
e
a
c
h
byte
o
f
the
s
tate
with
the
r
ound
ke
y.
F
or
e
a
c
h
r
ound
,
a
r
ound
ke
y
is
ge
ne
r
a
ted
f
r
om
the
ini
ti
a
l
ke
y
us
ing
the
ke
y
s
c
he
dule.
T
he
r
ound
ke
y
is
a
dde
d
to
e
a
c
h
byte
of
the
s
tate
by
the
e
Xc
lus
ive
OR
(
XO
R
)
f
unc
ti
on
.
(
a
)
(
b)
F
igur
e
1.
T
he
a
r
c
hit
e
c
tur
e
o
f
AE
S
(
a
)
A
E
S
e
nc
r
ypt
ion
pr
oc
e
s
s
a
nd
(
b)
AE
S
de
c
r
ypti
on
p
r
oc
e
s
s
T
hr
oughout
the
e
nc
r
ypti
on
pr
oc
e
s
s
,
da
ta
unde
r
goe
s
f
our
tr
a
ns
f
or
mations
in
e
a
c
h
r
ound,
r
e
s
ult
ing
in
a
s
ubs
tantial
a
lt
e
r
a
ti
on
of
inf
o
r
mation.
T
h
is
pr
ope
r
ty,
known
a
s
the
a
va
lanc
he
e
f
f
e
c
t,
s
igni
f
ica
ntl
y
e
nha
nc
e
s
the
s
e
c
ur
it
y
o
f
AE
S
by
e
ns
ur
ing
that
e
ve
n
a
mi
no
r
c
ha
nge
in
e
it
he
r
the
or
igi
na
l
im
a
ge
or
the
e
nc
r
y
pti
on
ke
y
lea
ds
to
a
bout
ha
lf
of
the
im
a
ge
be
ing
mod
if
ied.
T
his
e
f
f
e
c
t
c
a
us
e
s
s
li
ght
modi
f
ica
ti
ons
in
pixel
va
lues
to
pr
opa
ga
te
a
nd
im
pa
c
t
ne
ighbor
ing
pixels
a
nd
r
e
gi
ons
in
a
c
ompl
e
x
manne
r
.
How
e
ve
r
,
in
s
pa
c
e
a
pp
li
c
a
ti
ons
whe
r
e
da
ta
s
tor
a
ge
a
nd
tr
a
ns
mi
s
s
ion
a
r
e
c
r
it
ica
l
a
nd
of
ten
s
us
c
e
pti
ble
to
va
r
ious
e
nvir
onmenta
l
f
a
c
tor
s
s
uc
h
a
s
r
a
diation
a
nd
e
lec
tr
omagne
ti
c
int
e
r
f
e
r
e
nc
e
,
the
a
va
lanc
he
e
f
f
e
c
t
c
a
n
pos
e
c
ha
ll
e
nge
s
.
B
it
ups
e
ts
r
e
s
ult
ing
f
r
om
thes
e
f
a
c
tor
s
c
a
n
c
or
r
upt
the
or
igi
na
l
da
ta,
c
r
e
a
ti
ng
a
not
ice
a
ble
dif
f
e
r
e
nc
e
be
twe
e
n
the
or
ig
inal
a
nd
de
c
r
ypted
im
a
ge
s
[
13]
,
[
26
]
.
F
u
r
ther
mor
e
,
AE
S
is
s
us
c
e
pti
ble
to
c
r
yptana
lys
i
s
,
including
br
ute
f
or
c
e
a
tt
a
c
ks
.
C
ons
e
que
ntl
y,
a
ddr
e
s
s
ing
thes
e
is
s
ue
s
ne
c
e
s
s
it
a
te
s
a
mor
e
r
obus
t
e
nc
r
ypti
on
tec
hnique
that
f
ulf
il
ls
c
e
r
tain
c
r
it
e
r
ia
f
o
r
da
ta
c
on
f
identialit
y:
−
R
e
s
il
ienc
e
a
ga
ins
t
c
r
yptana
lys
is
by
pr
ovidi
ng
e
xtr
e
mely
high
c
ompl
e
xit
y
th
r
ough
the
s
im
ult
a
n
e
ous
ope
r
a
ti
ons
of
im
a
ge
s
c
r
a
mbl
ing
,
c
ha
oti
c
e
nc
r
ypt
io
n,
a
nd
A
E
S
.
−
T
oler
a
nc
e
to
S
E
Us
by
us
ing
the
c
ounter
mode
of
AE
S
to
r
e
duc
e
e
r
r
o
r
p
r
opa
ga
ti
on
dur
ing
i
mage
e
nc
r
ypti
on.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
Satelli
te
image
e
nc
r
y
pti
on
us
ing
2D
s
tandar
d
map
and
adv
anc
e
d
e
nc
r
y
pti
on
…
(
Om
ar
B
e
nc
hikh
)
5157
3.
P
ROP
OS
E
D
AL
GO
RI
T
HM
I
n
th
is
pa
pe
r
,
int
e
r
a
nd
int
r
a
-
ba
nd
inf
o
r
mation
s
c
r
a
mbl
ing
is
pe
r
f
o
r
med
joi
ntl
y
with
2D
s
tanda
r
d
map
ba
s
e
d
c
ha
oti
que
e
nc
r
ypti
on
to
im
pr
ove
the
s
e
c
ur
it
y
leve
l
of
s
a
telli
te
im
a
ge
e
nc
r
ypti
on
us
ing
AE
S
.
A
E
S
a
nd
2D
S
tanda
r
d
map
a
r
e
us
e
d
in
the
c
r
yp
tos
ys
tem
a
s
ve
r
y
e
f
f
e
c
ti
ve
e
nc
r
ypti
on
mec
ha
nis
ms
a
ga
ins
t
c
omput
e
r
a
tt
a
c
ks
a
nd
da
ta
lea
ka
ge
.
T
he
de
tailed
p
r
oc
e
dur
e
a
nd
the
block
diagr
a
m
o
f
the
p
r
opos
e
d
e
nc
r
ypti
on
a
lgor
it
hm
a
r
e
s
hown
in
F
igu
r
e
2
.
T
he
pr
opos
e
d
e
nc
r
ypti
on
pr
oc
e
s
s
is
c
a
r
r
ied
out
us
ing
the
f
oll
owing
s
teps
:
S
tep
1:
P
e
r
f
or
m
int
e
r
a
nd
int
r
a
-
ba
nd
s
c
r
a
mbl
ing
ope
r
a
ti
on
f
or
the
pixels
of
the
di
f
f
e
r
e
nt
ba
nds
of
the
or
igi
na
l
s
a
telli
te
im
a
ge
.
S
tep
2:
E
nc
r
ypt
the
obtaine
d
s
c
r
a
mbl
e
d
ba
nds
us
in
g
the
2D
s
tanda
r
d
map.
S
tep
3:
Util
ize
the
AE
S
-
C
T
R
a
lgor
it
hm
to
e
nc
r
ypt
the
a
c
quir
e
d
im
a
ge
s
.
T
he
pr
opos
e
d
e
nc
r
ypti
on
a
ppr
oa
c
h
invol
ve
s
mi
xi
ng
pixels
withi
n
a
nd
be
twe
e
n
ba
nds
of
a
s
a
telli
te
im
a
ge
to
int
r
oduc
e
c
onf
us
ion
int
o
the
da
ta.
B
y
div
idi
ng
the
c
lea
r
im
a
ge
int
o
laye
r
s
a
nd
then
manipul
a
ti
ng
the
a
r
r
a
nge
ment
of
pixels
methodica
ll
y
,
the
a
lgor
it
h
m
a
im
s
to
obs
c
ur
e
the
unde
r
lyi
ng
inf
or
mation,
making
it
ha
r
de
r
f
or
una
uthor
ize
d
us
e
r
s
to
de
c
ipher
.
Addit
ionally,
thi
s
e
nc
r
ypti
on
s
c
he
me
uti
li
z
e
s
a
c
ha
oti
c
s
ys
tem
ba
s
e
d
on
the
2D
s
tanda
r
d
map,
whic
h
a
dds
a
n
e
xtr
a
laye
r
of
s
e
c
ur
it
y
.
T
he
c
ha
oti
c
be
ha
vior
of
the
2D
S
tanda
r
d
map
s
ys
tem
e
nha
nc
e
s
the
e
nc
r
ypti
on
pr
oc
e
s
s
by
int
r
oduc
ing
unpr
e
dicta
bil
it
y
a
nd
c
ompl
e
xit
y.
I
t
he
lps
in
ge
ne
r
a
ti
ng
high
ly
r
a
ndom
s
e
que
nc
e
s
that
a
r
e
di
f
f
icult
to
r
e
pli
c
a
te,
im
pr
oving
the
r
e
s
is
tanc
e
a
ga
ins
t
va
r
ious
c
r
yptogr
a
phic
a
tt
a
c
ks
.
F
ur
ther
mor
e
,
to
e
ns
ur
e
r
obus
t
s
e
c
ur
it
y,
the
a
lgor
it
hm
c
onc
ludes
with
the
a
ppli
c
a
ti
on
of
AE
S
-
C
T
R
e
nc
r
ypti
on,
a
wide
ly
a
dopted
a
nd
we
ll
-
r
e
ga
r
de
d
e
nc
r
ypti
on
s
tanda
r
d
kno
wn
f
or
it
s
s
tr
ong
c
r
yptogr
a
phic
pr
ope
r
ti
e
s
a
nd
wide
s
pr
e
a
d
c
o
mpatibi
li
ty.
T
he
c
ombi
na
ti
on
o
f
in
ter
-
a
nd
int
r
a
-
ba
nd
pixel
mi
xing,
c
ha
os
ba
s
e
d
on
the
2D
s
tanda
r
d
map,
a
nd
AE
S
-
C
T
R
joi
nt
ly
c
ontr
ibut
e
s
to
e
s
tablis
hing
a
r
o
bus
t
a
nd
s
e
c
ur
e
e
nc
r
ypti
on
mec
ha
nis
m.
F
igur
e
2.
F
lowc
ha
r
t
of
the
pr
opos
e
d
e
nc
r
ypti
on
a
lg
or
it
hm
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
1
4
,
No.
5
,
Oc
tober
2
02
4
:
5153
-
5171
5158
3.
1.
S
t
e
p
1:
S
c
r
am
b
li
n
g
p
r
oc
e
s
s
T
he
ini
ti
a
l
e
nc
r
ypti
on
s
tep
in
the
p
r
opos
e
d
a
ppr
oa
c
h,
pr
io
r
to
the
a
ppli
c
a
ti
on
o
f
the
2D
s
tanda
r
d
map
a
nd
AE
S
,
f
a
c
il
it
a
tes
the
mi
xing
o
f
pixels
in
a
h
ig
hly
or
ga
nize
d
a
nd
r
a
pid
manne
r
.
T
his
f
unc
ti
on
t
r
a
ns
f
or
ms
the
dif
f
e
r
e
nt
s
pe
c
tr
a
l
ba
nds
o
f
the
plain
im
a
ge
of
s
ize
[
×
]
to
a
ve
c
tor
of
s
ize
[
×
×
]
,
whe
r
e
is
the
number
of
ba
nds
.
Nota
bly
,
thi
s
s
c
r
a
mbl
ing
s
tep
e
nha
nc
e
s
the
c
onf
us
ion
a
nd
dif
f
us
ion
pr
ope
r
ti
e
s
of
the
ove
r
a
ll
e
nc
r
ypti
on
pr
oc
e
s
s
.
T
he
p
ixels
withi
n
the
s
e
ba
nds
f
or
m
the
e
leme
nts
of
the
s
pe
c
if
ied
ve
c
t
or
.
T
he
ir
pos
it
ions
a
r
e
de
ter
mi
ne
d
f
i
r
s
t
by
their
c
hr
onolo
gica
l
or
de
r
.
S
ubs
e
que
ntl
y,
the
or
de
r
of
the
ba
nd
s
f
ur
ther
or
ga
nize
s
their
plac
e
ment.
At
the
e
nd
of
thi
s
pr
oc
e
s
s
,
the
r
e
s
ult
ing
ve
c
tor
will
be
tr
a
ns
f
or
med
int
o
im
a
ge
s
of
a
s
ize
[
×
]
.
F
igur
e
3
s
hows
a
n
e
xa
mpl
e
of
the
pr
inci
ple
of
the
s
c
r
a
mbl
ing
pr
oc
e
s
s
on
a
n
NR
GB
im
a
ge
of
s
ize
[5
×
4
]
.
T
he
r
e
s
ult
s
of
im
a
ge
s
c
r
a
mbl
ing
a
r
e
de
picte
d
in
F
igur
e
s
4
(
a
)
-
(
d)
,
whe
r
e
F
igu
r
e
4(
a
)
a
nd
F
igur
e
4(
c
)
e
xhibi
t
the
o
r
igi
na
l
im
a
ge
s
.
T
he
e
f
f
e
c
t
s
of
the
s
c
r
a
mbl
ing
pr
oc
e
s
s
on
thes
e
im
a
ge
s
a
r
e
il
lus
tr
a
ted
in
F
igur
e
4(
b)
a
nd
F
igu
r
e
4(
d
)
,
r
e
s
pe
c
ti
ve
ly.
F
igur
e
3.
E
xa
mpl
e
of
NR
GB
im
a
ge
s
c
r
a
mbl
ing
(
a
)
(
b)
(
c
)
(
d)
F
igur
e
4.
R
e
s
ult
s
of
the
p
r
opos
e
d
s
c
r
a
mbl
ing
pr
oc
e
s
s
(
a
)
Als
a
t
-
2
im
a
ge
of
Oua
r
gla
(
Alge
r
ia)
,
(
b)
Als
a
t
-
2
im
a
ge
a
f
ter
a
pplyi
ng
the
p
r
opos
e
d
s
c
r
a
mbl
ing
pr
oc
e
s
s
,
(
c
)
L
e
na
De
lt
a
R
e
s
e
r
ve
im
a
ge
,
a
nd
(
d)
L
e
na
D
e
lt
a
R
e
s
e
r
ve
im
a
ge
a
f
ter
a
pplyi
ng
the
pr
opos
e
d
s
c
r
a
mbl
ing
pr
oc
e
s
s
3.
2.
S
t
e
p
2:
Chaot
ic
e
n
c
r
yp
t
ion
of
t
h
e
s
c
r
am
b
le
d
b
an
d
s
T
he
2D
s
tanda
r
d
map
c
a
n
be
uti
li
z
e
d
in
im
a
ge
e
nc
r
ypti
on
a
s
a
c
ha
oti
c
mapping
f
unc
ti
on
to
int
r
oduc
e
r
a
ndomnes
s
a
nd
c
ompl
e
xit
y
int
o
the
e
nc
r
ypti
on
pr
oc
e
s
s
.
I
t
e
f
f
e
c
ti
ve
ly
dis
r
upts
pixel
pos
it
ions
,
e
nha
nc
ing
s
e
c
ur
it
y.
T
his
a
dde
d
c
ompl
e
xit
y
make
s
the
e
nc
r
ypt
e
d
im
a
ge
s
igni
f
ica
ntl
y
di
f
f
e
r
e
nt
f
r
om
the
o
r
igi
na
l.
T
he
e
nc
r
ypti
on
p
r
oc
e
s
s
invol
ve
s
two
ke
y
pha
s
e
s
:
pe
r
mut
a
ti
on
a
nd
di
f
f
us
ion.
−
P
e
r
mut
a
ti
on:
Dur
ing
thi
s
pha
s
e
,
the
im
a
ge
pixels
unde
r
go
a
r
a
ndom
r
e
a
r
r
a
nge
ment
.
T
his
s
tep
obs
c
ur
e
s
the
or
igi
na
l
pixel
s
e
que
nc
e
in
the
e
nc
r
ypted
im
a
ge
.
E
mpl
oying
the
2D
s
tanda
r
d
m
a
p
f
unc
ti
on,
whic
h
op
e
r
a
tes
a
s
a
c
ha
oti
c
map,
a
dds
int
r
ica
c
y
to
thi
s
pr
oc
e
s
s
.
C
ha
oti
c
maps
a
r
e
highl
y
s
e
ns
it
ive
to
ini
ti
a
l
c
ondi
ti
ons
,
making
it
c
ha
ll
e
nging
to
pr
e
dict
thei
r
output
e
ve
n
with
known
input
.
C
ons
e
que
ntl
y,
r
e
ve
r
s
ing
the
pe
r
mut
a
ti
on
to
r
e
c
ove
r
the
or
igi
na
l
im
a
ge
without
t
he
e
nc
r
ypti
on
ke
y
be
c
omes
e
xc
e
e
dingl
y
dif
f
icult.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
Satelli
te
image
e
nc
r
y
pti
on
us
ing
2D
s
tandar
d
map
and
adv
anc
e
d
e
nc
r
y
pti
on
…
(
Om
ar
B
e
nc
hikh
)
5159
−
Dif
f
us
ion:
I
n
thi
s
pha
s
e
,
pixel
inf
or
mat
ion
is
s
pr
e
a
d
a
c
r
os
s
the
e
nti
r
e
im
a
ge
,
making
indi
vidual
pixel
va
lues
indi
s
c
e
r
nibl
e
in
the
e
nc
r
ypted
i
mage
.
Util
i
z
ing
the
2D
s
tanda
r
d
map
f
unc
ti
on
,
whic
h
s
e
r
ve
s
a
s
a
nonli
ne
a
r
map,
f
u
r
ther
c
ompl
ica
tes
thi
s
s
tage
.
Nonlinea
r
maps
do
not
maintain
o
r
igi
na
l
input
-
output
r
e
lations
hips
,
making
it
dif
f
icult
to
de
duc
e
a
ny
s
in
gle
pixel's
va
lue
s
olely
f
r
o
m
the
e
nc
r
yp
ted
im
a
ge
.
T
he
c
ombi
na
ti
on
of
pe
r
mut
a
t
ion
a
nd
dif
f
us
ion
m
a
ke
s
the
2D
s
tanda
r
d
ma
p
f
unc
ti
on
a
s
e
c
ur
e
a
nd
e
f
f
icie
nt
wa
y
to
e
nc
r
ypt
i
mage
s
.
T
he
2D
c
ha
oti
c
map
known
a
s
the
S
tanda
r
d
map
is
de
f
ined
a
s
[
34
]
:
[
+
1
=
(
+
+
+
)
+
1
=
(
+
+
∗
+
1
2
)
]
(
1)
whe
r
e
a
nd
r
e
pr
e
s
e
nt
the
c
ur
r
e
nt
pos
it
ions
in
th
e
pha
s
e
s
pa
c
e
,
a
nd
+
1
a
nd
+
1
r
e
p
r
e
s
e
nt
the
ne
xt
pos
it
ions
a
f
ter
the
map
it
e
r
a
ti
on.
P
a
r
a
mete
r
is
t
he
s
tr
e
ngth
o
f
the
nonli
ne
a
r
it
y
of
the
s
ys
tem.
F
o
r
s
mall
va
lues
of
,
the
s
ys
tem
e
xhibi
ts
r
e
gular
mot
ion,
wi
th
pe
r
iodi
c
o
r
bit
s
that
a
r
e
c
onf
ined
to
s
pe
c
if
ic
r
e
g
ions
of
the
pha
s
e
s
pa
c
e
.
As
is
incr
e
a
s
e
d,
the
or
bi
ts
be
c
ome
mor
e
c
ompl
e
x
,
a
nd
the
s
ys
tem
c
a
n
manif
e
s
t
c
ha
oti
c
be
ha
vior
,
with
tr
a
jec
tor
ies
that
a
r
e
s
e
ns
it
ive
to
ini
t
ial
c
ondit
ions
.
Note
,
a
nd
a
r
e
two
pa
r
a
mete
r
s
that
both
va
r
y
f
r
om
0
to
−
1
,
a
nd
a
r
e
us
e
d
to
s
hif
t
the
i
mage
in
the
hor
izonta
l
a
nd
ve
r
ti
c
a
l
di
r
e
c
ti
ons
[
34]
.
M
or
e
ov
e
r
,
the
pa
r
a
mete
r
s
pa
c
e
of
the
2D
s
tanda
r
d
map
e
x
c
e
e
ds
that
of
the
C
a
t
map
a
nd
B
a
ke
r
map,
mak
ing
it
a
f
a
vor
a
ble
opti
on
f
or
da
ta
e
nc
r
ypti
on
[
34]
.
W
it
hin
thi
s
s
e
c
ti
on,
the
2D
s
tanda
r
d
map
f
unc
ti
on
is
a
ppli
e
d
to
the
s
c
r
a
mbl
e
d
ba
nds
obtaine
d
a
f
te
r
the
a
ppli
c
a
ti
on
of
the
s
c
r
a
mbl
ing
pr
oc
e
s
s
.
T
he
e
nc
r
ypti
on
pr
oc
e
s
s
invol
ve
s
s
pe
c
if
ying
the
de
s
ir
e
d
number
of
r
ounds
,
de
noted
a
s
n
,
a
nd
e
s
tablis
hing
the
ini
ti
a
l
c
ondit
ion
to
ini
ti
a
te
thi
s
ope
r
a
ti
on.
E
a
c
h
ba
nd
obtai
ne
d
f
r
om
the
s
c
r
a
mbl
ing
f
unc
ti
on
mus
t
unde
r
go
thi
s
c
ha
oti
c
e
nc
r
ypti
on
pr
oc
e
s
s
.
E
ve
ntually,
we
obtain
a
s
e
t
of
e
nc
r
ypted
im
a
ge
s
,
e
a
c
h
with
di
mens
ions
[
×
]
,
a
s
il
lus
tr
a
ted
in
F
igur
e
2.
T
he
a
ppli
c
a
ti
on
of
the
pr
opos
e
d
c
ha
oti
c
map
in
thi
s
s
tage
of
im
a
ge
e
nc
r
ypti
on
c
a
n
s
igni
f
ica
ntl
y
e
nha
nc
e
it
s
s
e
c
ur
it
y.
T
his
is
be
c
a
us
e
without
kno
wle
dge
of
the
p
r
e
c
is
e
c
ontr
ol
pa
r
a
mete
r
s
a
nd
ini
ti
a
l
va
lues
,
a
n
una
uthor
ize
d
indi
vidual
would
be
una
ble
to
pr
e
dict
the
c
ha
oti
c
s
e
que
nc
e
ge
ne
r
a
ted
by
the
s
ys
tem.
I
n
e
s
s
e
nc
e
,
the
inher
e
nt
c
ha
oti
c
be
ha
vior
o
f
thi
s
s
ys
tem
int
r
oduc
e
s
a
n
a
ddit
ional
s
e
c
ur
it
y
ba
r
r
ier
,
m
a
king
it
highl
y
c
ha
ll
e
nging
f
or
una
uthor
ize
d
pa
r
t
ies
to
de
c
i
phe
r
e
nc
r
ypted
im
a
ge
s
.
F
igu
r
e
5
s
hows
the
block
diagr
a
m
of
the
pr
opos
e
d
c
ha
oti
c
im
a
ge
s
huf
f
li
ng
us
ing
the
2D
s
tanda
r
d
map.
F
igu
r
e
s
6
(
a
)
-
(
d)
il
lus
tr
a
te
the
r
e
s
ult
s
of
a
pplyi
ng
the
pr
opos
e
d
c
ha
oti
c
map
to
s
huf
f
le
the
s
c
r
a
mbl
e
d
im
a
ge
s
.
S
pe
c
if
ica
ll
y,
F
igu
r
e
6(
a
)
a
nd
F
ig
ur
e
6(
c
)
e
xhibi
t
the
ini
ti
a
l
s
c
r
a
mbl
e
d
i
mage
s
,
while
F
igu
r
e
6(
b)
a
nd
F
igur
e
6
(
d)
r
e
ve
a
l
the
outcome
s
of
e
n
c
r
ypti
ng
thes
e
im
a
ge
s
us
ing
the
c
ha
oti
c
2D
s
tanda
r
d
m
a
p,
r
e
s
pe
c
ti
ve
ly.
F
igur
e
5.
B
lock
diagr
a
m
of
the
pr
opos
e
d
c
ha
oti
c
p
r
oc
e
s
s
ba
s
e
d
on
2D
s
tanda
r
d
map
(
a
)
(
b)
(
c
)
(
d)
F
igur
e
6.
R
e
s
ult
s
of
the
a
ppl
ica
ti
on
of
c
ha
oti
c
e
nc
r
ypti
on
to
two
dif
f
e
r
e
nt
im
a
ge
s
(
a
)
S
c
r
a
mbl
e
d
im
a
g
e
of
Als
a
t
-
2
im
a
ge
,
(
b)
Als
a
t
-
2
im
a
ge
a
f
ter
2D
s
tanda
r
d
map
f
unc
ti
on
,
(
c
)
S
c
r
a
mbl
e
d
im
a
ge
o
f
L
e
na
De
lt
a
R
e
s
e
r
ve
,
a
nd
(
d)
L
e
na
De
lt
a
R
e
s
e
r
ve
im
a
ge
a
f
ter
2
D
s
tanda
r
d
map
f
unc
ti
on
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
1
4
,
No.
5
,
Oc
tober
2
02
4
:
5153
-
5171
5160
3.
3.
S
t
e
p
3:
AE
S
-
CT
R
e
n
c
r
yp
t
ion
AE
S
in
c
ounter
mode
is
a
block
c
ipher
ope
r
a
ti
on
that
uti
l
ize
s
a
c
ounter
,
ini
ti
a
li
z
e
d
with
a
pr
e
de
ter
mi
ne
d
va
lue,
a
long
with
a
nonc
e
r
e
s
e
m
bli
ng
the
ini
t
ial
ve
c
tor
,
to
ge
ne
r
a
te
a
ke
y
s
tr
e
a
m.
T
he
ge
ne
r
a
ted
ke
y
s
tr
e
a
m
is
s
ubs
e
que
ntl
y
c
ombi
ne
d
w
it
h
the
plaintext
thr
ough
a
n
XO
R
ope
r
a
ti
on
to
p
r
o
duc
e
the
c
ipher
text.
C
T
R
mode
ha
s
s
e
ve
r
a
l
a
dva
ntage
s
ove
r
other
bl
oc
k
c
ipher
modes
of
ope
r
a
ti
on.
F
ir
s
tl
y,
it
ha
s
the
c
a
pa
bil
it
y
to
ove
r
c
ome
e
r
r
or
pr
opa
ga
ti
on
[
21]
.
S
e
c
ondly,
it
doe
s
not
ne
c
e
s
s
it
a
te
pa
dding
plaintext
t
o
matc
h
the
block
s
ize
of
the
c
ipher
.
Additi
ona
ll
y
,
e
a
c
h
pl
a
int
e
xt
block's
e
nc
r
ypti
on
is
indepe
nde
nt
of
the
e
n
c
r
ypti
on
of
pr
e
vious
b
locks
,
e
na
bli
ng
h
ighl
y
pa
r
a
ll
e
l
iza
ble
a
r
c
hit
e
c
tur
e
[
21
]
.
Ove
r
a
ll
,
C
T
R
mode
is
the
p
r
e
f
e
r
r
e
d
c
hoice
f
or
block
c
ipher
s
,
of
f
e
r
ing
e
f
f
icie
nc
y,
v
e
r
s
a
ti
li
ty,
a
nd
high
opti
m
iza
ti
on
f
or
bo
th
ha
r
d
wa
r
e
a
nd
s
of
twa
r
e
im
pleme
ntations
a
c
r
os
s
va
r
ious
platf
or
ms
.
I
n
the
las
t
s
tage
of
our
pr
opos
e
d
a
lgor
it
h
m,
a
n
ini
ti
a
l
ve
c
tor
(
I
V)
is
c
onc
a
tena
ted
with
a
32
-
bit
c
ounter
va
lue
to
ge
ne
r
a
te
the
ke
ys
tr
e
a
m
f
or
e
nc
r
y
pti
on.
T
his
pr
oc
e
s
s
us
e
s
AE
S
e
nc
r
ypt
ion
with
a
s
e
c
r
e
t
ke
y
of
256
bi
ts
.
E
a
c
h
block
of
the
ke
ys
tr
e
a
m
is
f
ixed
a
t
a
length
o
f
256
bit
s
.
F
igur
e
7
il
lus
tr
a
tes
the
p
r
i
nc
ipl
e
of
the
C
T
R
mode
.
I
t
s
hould
be
noted
that
the
s
ize
of
t
he
ge
ne
r
a
ted
ke
ys
tr
e
a
m
is
e
quivale
nt
to
that
of
the
or
igi
na
l
plain
im
a
ge
.
A
f
t
e
r
w
a
r
d
,
i
ma
ge
s
ob
ta
ine
d
t
h
r
o
ug
h
c
h
a
o
t
ic
e
n
c
r
y
pt
io
n
w
il
l
be
p
r
oc
e
s
s
e
d
t
hr
ou
gh
A
E
S
e
n
c
r
y
pt
io
n
u
s
i
ng
C
T
R
mo
de
a
nd
a
s
in
gl
e
s
ha
r
e
d
k
e
y
,
a
s
d
e
p
ic
te
d
i
n
t
he
d
ia
g
r
a
m
p
r
e
s
e
nt
e
d
in
F
i
gu
r
e
2
.
T
he
f
ina
l
o
u
tp
ut
of
t
h
is
e
nc
r
yp
t
io
n
s
ta
ge
is
a
c
ip
he
r
i
mag
e
o
f
s
iz
e
[
×
×
]
c
on
ta
in
i
ng
ba
nds
.
F
i
gu
r
e
s
8
(
a
)
-
(
d)
il
lus
t
r
a
tes
t
he
r
e
s
ul
ts
o
f
t
he
a
p
pl
ica
t
io
n
o
f
AE
S
-
C
T
R
e
nc
r
y
pt
i
on
o
n
t
he
s
h
uf
f
le
d
im
a
g
e
s
ob
ta
ine
d
f
r
om
s
t
e
p
2
o
f
t
he
p
r
op
os
e
d
e
nc
r
y
p
ti
on
s
c
h
e
m
e
.
T
he
or
igi
na
l
s
huf
f
led
im
a
ge
s
a
r
e
pr
e
s
e
nted
in
F
igu
r
e
s
8(
a
)
a
nd
8(
c
)
,
whe
r
e
a
s
t
he
f
inal
e
nc
r
ypted
f
or
ms
o
f
thes
e
im
a
ge
s
a
r
e
de
picte
d
in
F
i
gur
e
s
8(
b)
a
nd
8(
d)
.
F
igur
e
7.
C
ounter
(
C
T
R
)
mode
e
nc
r
ypti
on
(
a
)
(
b)
(
c
)
(
d)
F
igur
e
8.
R
e
s
ult
s
of
the
a
ppl
ica
ti
on
of
AE
S
to
two
dif
f
e
r
e
nt
i
mage
s
(
a
)
c
ipher
e
d
Als
a
t
-
2
im
a
ge
,
(
b)
Al
s
a
t
-
2
im
a
ge
a
f
ter
A
E
S
e
nc
r
ypti
on
,
(
c
)
c
iphe
r
e
d
L
e
na
De
lt
a
R
e
s
e
r
ve
im
a
ge
,
(
d)
L
e
na
De
lt
a
R
e
s
e
r
ve
im
a
ge
a
f
ter
AE
S
e
nc
r
ypti
on
3.
4.
T
h
e
d
e
c
r
yp
t
io
n
p
r
oc
e
s
s
Give
n
that
the
s
ugge
s
ted
e
nc
r
ypti
on
s
c
he
me
is
s
ymm
e
tr
ic,
the
de
c
ipher
ing
pr
oc
e
dur
e
uti
li
z
e
s
a
n
identica
l
e
nc
r
ypti
on
ke
y
(
c
ha
os
a
nd
AE
S
ke
y)
a
n
d
a
n
I
V
to
de
c
r
ypt
the
c
ipher
e
d
im
a
ge
.
C
ons
e
que
ntl
y,
thi
s
r
e
s
ult
s
in
the
ge
ne
r
a
ti
on
o
f
a
n
indi
s
ti
nguis
ha
ble
pe
r
mut
a
ti
on
ke
y
a
nd
ke
ys
tr
e
a
m.
−
At
the
be
ginni
ng
of
the
de
c
r
ypti
on
pr
oc
e
s
s
,
AE
S
is
us
e
d
in
c
onjunction
with
a
s
e
c
r
e
t
ke
y
to
ge
ne
r
a
te
a
ke
ys
tr
e
a
m
ba
s
e
d
on
the
s
a
me
c
onc
a
tena
ted
ini
ti
a
l
ve
c
tor
a
nd
c
ounter
va
lue
.
T
he
e
nc
r
ypted
im
a
ge
ne
e
ds
to
be
f
r
a
gmente
d
int
o
im
a
ge
s
,
a
nd
e
a
c
h
im
a
ge
will
u
nde
r
go
the
s
tanda
r
d
AE
S
de
c
r
ypti
on
pr
oc
e
s
s
us
ing
the
c
onf
idential
ke
y.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
Satelli
te
image
e
nc
r
y
pti
on
us
ing
2D
s
tandar
d
map
and
adv
anc
e
d
e
nc
r
y
pti
on
…
(
Om
ar
B
e
nc
hikh
)
5161
−
T
he
ne
xt
s
tep
invol
ve
s
a
pr
oc
e
dur
e
that
e
na
ble
s
the
r
e
s
tor
a
ti
on
of
the
s
huf
f
led
im
a
ge
s
obtaine
d
by
uti
li
z
ing
the
r
e
ve
r
s
e
of
the
dis
or
de
r
ly
pe
r
mut
a
ti
o
n.
I
nit
ially,
the
de
c
r
ypti
on
pr
oc
e
s
s
ge
ne
r
a
tes
the
ke
y
thr
ough
2D
s
tanda
r
d's
mapping,
incor
po
r
a
ti
ng
t
he
pr
e
s
e
lec
ted
c
ount
of
it
e
r
a
ti
ons
uti
li
z
e
d
dur
in
g
the
e
nc
r
ypti
on
pr
oc
e
s
s
,
ther
e
by
ini
ti
a
ti
ng
the
de
c
r
ypti
on
ope
r
a
ti
on.
T
he
r
e
c
ipi
e
nt
mer
e
ly
r
e
quir
e
s
the
i
nit
ial
pa
r
a
mete
r
s
a
nd
c
a
n
obtain
the
c
or
r
e
s
ponding
e
nc
r
y
pti
on
ke
y
us
ing
thes
e
va
lues
.
−
I
n
the
thi
r
d
s
tage
,
the
int
e
r
mi
ngled
pixels
a
c
r
os
s
im
a
ge
s
a
r
e
s
ubs
e
que
ntl
y
r
e
or
ga
nize
d
ba
s
e
d
on
their
s
e
que
nti
a
l
or
de
r
s
,
c
ons
tr
uc
ti
ng
the
ba
nds
of
the
un
e
nc
r
ypted
im
a
ge
.
−
Ulti
mate
ly,
the
a
f
or
e
mentioned
laye
r
s
a
bove
a
r
e
r
e
a
s
s
e
mbl
e
d
int
o
a
[
×
×
]
pixel
a
r
r
a
y
in
s
e
que
nc
e
,
a
nd
the
r
e
s
ult
ing
im
a
ge
is
the
de
c
r
yp
ted
im
a
ge
.
T
he
de
c
r
ypti
on
of
the
c
ipher
e
d
im
a
ge
is
c
ompl
e
ted.
T
he
r
e
s
ult
s
o
f
e
nc
r
ypti
ng
a
nd
de
c
r
ypti
ng
di
f
f
e
r
e
nt
s
a
telli
te
im
a
ge
s
by
us
ing
the
pr
opos
e
d
method
a
r
e
s
hown
in
F
igur
e
s
9
to
11.
On
the
lef
t,
F
igu
r
e
9(
a
)
,
F
igu
r
e
10(
a
)
,
a
nd
F
igur
e
11(
a
)
dis
play
thr
e
e
dis
ti
nc
t
or
igi
na
l
im
a
ge
s
.
T
he
c
e
ntr
a
l
im
a
ge
s
,
F
igur
e
9(
b
)
,
F
igu
r
e
10
(
b)
,
a
nd
F
igur
e
11(
b)
,
p
r
e
s
e
nt
the
e
n
c
r
ypti
on
r
e
s
ult
s
obtaine
d
us
ing
the
pr
opos
e
d
a
lgor
it
hm.
On
the
r
ight
,
F
igur
e
9(
c
)
,
F
igu
r
e
10(
c
)
,
a
nd
F
ig
ur
e
11(
c
)
il
lus
tr
a
te
the
c
or
r
e
s
ponding
de
c
r
yp
ti
on
r
e
s
ult
s
.
(
a
)
(
b)
(
c
)
F
igur
e
9.
L
e
na
De
lt
a
R
e
s
e
r
ve
©
USGS
E
R
OS
Da
ta
C
e
nter
-
NA
S
A
(
a
)
plain
(
o
r
igi
na
l)
R
GB
im
a
ge
,
(
b)
e
nc
r
ypted
im
a
ge
,
a
nd
(
c
)
de
c
r
ypted
R
GB
im
a
g
e
(
a
)
(
b)
(
c
)
F
igur
e
10.
S
c
ulpt
ing
a
B
a
s
in,
I
r
a
q’
s
Ga
’
a
r
a
De
pr
e
s
s
ion,
L
a
nds
a
t
8
-
OL
I
©
NA
S
A
(
a
)
plain
(
o
r
igi
na
l)
R
GB
im
a
ge
,
(
b
)
e
nc
r
ypted
im
a
ge
,
a
nd
(
c
)
de
c
r
ypted
R
GB
im
a
ge
(
a
)
(
b)
(
c
)
F
igur
e
11.
S
a
nd
R
us
h
in
W
is
c
ons
in
©
L
a
nds
a
t
8
-
OL
I
_NA
S
A
(
a
)
plain
(
or
ig
inal)
R
GB
im
a
ge
,
(
b)
e
nc
r
ypted
im
a
ge
,
a
nd
(
c
)
de
c
r
ypted
R
GB
im
a
g
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
1
4
,
No.
5
,
Oc
tober
2
02
4
:
5153
-
5171
5162
4.
RE
S
UL
T
S
AN
D
DI
S
CU
S
S
I
ON
T
he
s
im
ulation
tes
ts
we
r
e
pe
r
f
or
med
on
a
c
o
mput
e
r
f
e
a
tur
ing
a
n
I
ntel®
C
or
e
™
i7
-
5700HQ
pr
oc
e
s
s
or
(
Qua
d
-
C
or
e
2.
7
GH
z
)
a
nd
12
GB
DD
R
3
-
R
A
M
ope
r
a
ti
ng
a
t
1600
M
Hz
.
T
he
s
ys
tem
wa
s
r
unning
W
indows
10
(
64
-
bit
)
.
T
he
im
pleme
ntation
wa
s
c
a
r
r
ied
out
us
ing
the
M
AT
L
AB
pr
ogr
a
mm
ing
langua
ge
.
T
e
s
ts
we
r
e
pe
r
f
or
med
on
va
r
ious
im
a
ge
s
,
including
“
L
e
na
im
a
ge
”
,
a
nd
s
e
ve
r
a
l
s
a
telli
te
im
a
ge
s
“
L
e
na
De
lt
a
R
e
s
e
r
ve
©
USGS
E
R
OS
Da
ta
C
e
nter
-
NA
S
A”
,
“
S
c
ulpt
ing
a
B
a
s
in,
I
r
a
q
’
s
Ga
’
a
r
a
De
pr
e
s
s
ion,
L
a
nds
a
t
8
-
OL
I
©
NA
S
A”
,
a
nd
“
S
a
nd
R
us
h
in
W
is
c
ons
in
©
L
a
nds
a
t
8
-
OL
I
_NA
S
A”
,
with
s
ize
s
of
512
×
512
,
310
0
×
3100,
4627
×
4627,
a
nd
2848
×
2848,
r
e
s
pe
c
ti
ve
ly.
4.
1.
Hi
s
t
ogr
am
an
alys
is
Us
ing
the
pr
opos
e
d
a
lgor
it
hm,
the
his
togr
a
m
a
na
lys
is
wa
s
pe
r
f
or
med
on
s
e
ve
r
a
l
e
nc
r
ypted
im
a
ge
s
a
s
s
hown
in
F
igur
e
12.
T
he
or
igi
na
l
im
a
ge
's
his
togr
a
m
in
F
igur
e
12(
a
)
s
e
r
ve
s
a
s
a
ba
s
e
li
ne
f
or
c
ompar
is
on.
F
igur
e
12(
b
)
il
lus
tr
a
tes
the
his
togr
a
m
a
f
ter
the
ini
ti
a
l
s
c
r
a
mbl
ing
s
tep.
F
igur
e
12(
c
)
s
hows
the
his
togr
a
m
a
f
ter
a
pplyi
ng
the
2D
s
tanda
r
d
map
.
F
inally
,
F
igur
e
1
2(
d)
pr
e
s
e
nts
the
his
togr
a
m
of
the
f
inal
e
nc
r
ypte
d
im
a
ge
obtaine
d
a
f
ter
a
pplyi
ng
AE
S
-
C
T
R
.
As
e
vident
f
r
o
m
the
dis
playe
d
f
igur
e
s
the
f
r
e
que
nc
y
dis
tr
ibut
ion
s
of
the
c
ipher
e
d
im
a
ge
s
e
xhibi
t
a
unif
or
m
c
ha
r
a
c
ter
is
ti
c
.
T
he
r
e
f
o
r
e
,
the
e
xpe
r
im
e
ntal
r
e
s
ult
s
indi
c
a
te
that
our
pr
opos
e
d
e
nc
r
ypti
on
pr
oc
e
s
s
is
we
ll
-
s
uit
e
d
to
gu
a
r
a
ntee
highl
y
s
e
c
ur
e
a
nd
r
e
li
a
ble
outcome
s
f
o
r
s
a
telli
te
im
a
ge
r
y.
T
he
unif
o
r
m
dis
tr
ibut
ion
o
f
pixel
va
lues
he
lps
pr
e
ve
nt
s
tatis
ti
c
a
l
a
na
lys
is
a
nd
potential
a
tt
a
c
ks
.
(
a
)
(
b)
(
c
)
(
d)
F
igur
e
12.
Or
igi
na
l
im
a
ge
(
L
e
na
)
a
nd
e
nc
r
ypted
i
mage
his
togr
a
ms
(
a
)
His
togr
a
m
of
the
or
ig
inal
im
a
ge
,
(
b)
His
togr
a
m
of
the
s
c
r
a
mbl
e
d
im
a
ge
obtaine
d
a
f
t
e
r
the
a
ppli
c
a
ti
on
o
f
the
s
c
r
a
mbl
ing
pr
oc
e
s
s
(
s
tep
1
)
,
(
c
)
His
togr
a
m
o
f
the
e
nc
r
ypted
im
a
ge
a
f
ter
the
a
pp
li
c
a
ti
on
of
2D
s
tanda
r
d
m
a
p
(
s
tep
2)
,
a
nd
(
d)
His
togr
a
m
of
the
f
inal
c
ipher
e
d
im
a
ge
us
ing
th
e
pr
opos
e
d
s
c
he
me
(
a
f
ter
a
pplyi
ng
AE
S
-
C
T
R
)
4.
2.
S
e
n
s
it
ivi
t
y
an
a
lys
is
of
t
h
e
p
r
op
os
e
d
algori
t
h
m
4.
2.
1.
Dif
f
e
r
e
n
t
ial
a
t
t
ac
k
s
an
alys
is
F
or
im
a
ge
-
ba
s
e
d
c
r
yptos
ys
tems
,
s
e
c
ur
it
y
pe
r
f
or
m
a
nc
e
a
na
lys
is
s
hould
be
c
a
r
r
ied
ou
t
by
opt
im
izing
two
pa
r
a
mete
r
s
:
number
of
pixels
c
ha
nge
r
a
te
(
NP
C
R
)
a
nd
unif
ied
a
ve
r
a
ge
c
ha
nging
int
e
ns
it
y
(
UA
C
I
)
,
whic
h
a
r
e
wide
ly
us
e
d
to
a
na
lyze
the
c
ipher
's
r
e
s
is
tan
c
e
to
dif
f
e
r
e
nti
a
l
a
tt
a
c
ks
[
35]
–
[
37]
.
NPC
R
r
e
f
e
r
s
to
the
pe
r
c
e
ntage
of
pixels
that
c
ha
nge
in
the
e
nc
r
ypte
d
im
a
ge
whe
n
one
pixel
of
the
or
igi
na
l
(
plain)
i
mage
is
modi
f
ied.
I
n
c
ontr
a
s
t,
UA
C
I
c
a
lcula
tes
the
a
ve
r
a
ge
dif
f
e
r
e
nc
e
in
int
e
ns
it
y
be
twe
e
n
c
or
r
e
s
ponding
pixels
in
Evaluation Warning : The document was created with Spire.PDF for Python.