T
E
L
KO
M
N
I
KA
T
e
lec
om
m
u
n
icat
ion
,
Com
p
u
t
i
n
g,
E
lec
t
r
on
ics
an
d
Cont
r
ol
Vol.
18
,
No.
1
,
F
e
br
ua
r
y
2020
,
pp.
289
~
300
I
S
S
N:
1693
-
6930,
a
c
c
r
e
dit
e
d
F
ir
s
t
G
r
a
de
by
Ke
me
nr
is
tekdikti
,
De
c
r
e
e
No:
21/E
/KP
T
/2018
DO
I
:
10.
12928/
T
E
L
KO
M
NI
KA
.
v18i1.
13293
289
Jou
r
n
al
h
omepage
:
ht
tp:
//
jour
nal.
uad
.
ac
.
id/
index
.
php/T
E
L
K
OM
N
I
K
A
Ir
is
i
m
age
s e
n
c
r
y
p
t
io
n
b
ase
d
on
QR c
o
d
e
a
n
d
c
h
aot
i
c
m
a
p
Rani
ah
Ali
M
u
s
t
a
f
a
,
Am
al
Abd
u
lb
a
q
i
M
ar
yoos
h
,
De
n
a
Nadi
r
Geor
ge
,
Walee
d
Ras
h
e
e
d
Hu
m
oo
d
D
ep
ar
t
men
t
o
f
Co
m
p
u
t
er
Sci
en
ce,
Co
l
l
a
g
e
o
f
E
d
u
cat
i
o
n
,
Mu
s
t
a
n
s
i
ri
y
ah
U
n
i
v
er
s
i
t
y
,
Iraq
Ar
t
icle
I
n
f
o
AB
S
T
RA
CT
A
r
ti
c
le
h
is
tor
y
:
R
e
c
e
ived
J
un
10
,
2019
R
e
vis
e
d
J
ul
2
7
,
2019
Ac
c
e
pted
Aug
18,
2019
In
t
h
i
s
p
a
p
er
an
Iri
s
i
ma
g
e
i
s
e
n
cry
p
t
e
d
b
a
s
ed
o
n
Q
R
(
q
u
i
ck
res
p
o
n
s
e)
co
d
e
an
d
ch
a
o
t
i
c
map
.
T
h
e
mai
n
i
d
ea
o
f
t
h
e
p
ro
p
o
s
ed
s
y
s
t
em
i
s
g
e
n
erat
i
n
g
a
Q
R
co
d
e
d
e
p
en
d
i
n
g
o
n
t
h
e
i
n
p
u
t
t
e
x
t
an
d
t
h
en
ex
t
ract
t
h
e
feat
u
res
fr
o
m
Q
R
co
d
e
b
y
u
s
i
n
g
co
n
v
o
l
u
t
i
o
n
,
t
h
es
e
feat
u
res
are
u
s
e
d
fo
r
k
ey
g
en
era
t
i
o
n
.
A
ft
er
t
h
at
t
h
e
p
ermu
t
ed
i
ri
s
i
ma
g
e
i
s
en
cr
y
p
t
ed
b
y
u
s
i
n
g
g
en
era
t
ed
k
ey
,
aft
er
t
h
a
t
t
h
e
res
u
l
t
i
n
g
i
ma
g
e
w
i
l
l
b
e
en
cr
y
p
t
s
u
s
i
n
g
2
D
l
o
g
i
s
t
i
c
map
.
T
h
e
ran
d
o
m
n
es
s
o
f
g
en
era
t
ed
k
e
y
i
s
t
e
s
t
e
d
u
s
i
n
g
t
h
e
meas
u
res
o
f
N
IS
T
,
an
d
q
u
al
i
t
y
o
f
i
mag
e
s
t
h
a
t
en
cr
y
p
t
ed
i
n
t
h
i
s
met
h
o
d
are
t
e
s
t
e
d
b
y
u
s
i
n
g
s
e
cu
ri
t
y
an
al
y
s
i
s
t
e
s
t
s
s
u
c
h
as
PSN
R,
U
A
CI,
N
PCR,
h
i
s
t
o
g
r
am,
co
rrel
at
i
o
n
an
d
en
t
r
o
p
y
.
T
h
e
s
ec
u
ri
t
y
an
a
l
y
s
i
s
s
h
o
w
s
t
h
a
t
t
h
e
p
r
o
p
o
s
ed
s
y
s
t
em
i
s
s
ecu
re
fo
r
i
r
i
s
i
mag
e
en
cr
y
p
t
i
o
n
.
K
e
y
w
o
r
d
s
:
B
inar
iza
ti
on
His
togr
a
m
e
qua
li
z
a
ti
on
I
mage
e
nc
r
ypti
on
L
ogis
ti
c
map
QR
c
ode
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
:
Ama
l
Abdulba
qi
M
a
r
yoos
h
,
De
pa
r
tm
e
nt
of
C
omput
e
r
S
c
ienc
e
,
C
oll
a
ge
o
f
E
duc
a
ti
on,
M
us
tans
ir
iyah
Unive
r
s
it
y
,
I
r
a
q.
E
mail:
a
malmar
yoos
h@uomus
tans
ir
iyah.
e
du.
iq
1.
I
NT
RODU
C
T
I
ON
At
thi
s
ti
me,
us
e
r
s
a
tt
e
mpt
to
s
e
lec
t
a
s
hor
ter
pa
s
s
wor
d
to
a
uthentica
te
their
a
c
c
ounts
.
T
he
pa
s
s
wor
d
may
be
e
a
s
y
f
or
gott
e
n
a
nd
it
c
a
n
be
e
a
s
il
y
a
tt
a
c
ke
d.
W
idely
us
e
d
tec
hnologi
e
s
s
uc
h
a
s
voice
r
e
c
ognit
ion,
B
a
r
-
c
ode
,
F
inger
pr
int
s
c
a
nning,
i
r
is
r
e
c
ognit
ion
a
n
d
f
a
c
e
r
e
c
ognit
ion
now
play
a
n
im
po
r
tant
r
ole
,
pa
r
ti
c
ular
ly
in
s
e
c
ur
it
y
-
r
e
late
d
a
ppli
c
a
ti
ons
[
1]
.
T
he
ba
r
c
ode
is
one
dim
e
ns
ional
a
nd
be
c
ome
s
il
legible
whe
n
d
a
mage
d.
B
a
r
-
c
ode
ha
s
s
ome
dis
a
dva
ntage
li
ke
it
s
tor
e
s
only
up
to
20
digi
ts
.
F
or
thi
s
r
e
a
s
on,
i
n
the
ba
r
c
ode
,
w
e
c
a
nnot
s
tor
e
pa
s
s
wor
ds
or
c
ompl
e
x
ph
r
a
s
e
s
,
s
o
it
doe
s
n’
t
p
r
ovide
the
be
s
t
method
f
or
a
uthentica
ti
on.
QR
c
ode
s
a
r
e
2D
b
a
r
c
ode
c
a
n
be
r
e
a
d
f
r
om
a
ny
or
ienta
ti
on
a
nd
it
h
a
s
the
a
bil
it
y
to
hold
up
to
4,
296
c
ha
r
a
c
ter
s
a
lphabe
ti
c
a
ll
y.
Anothe
r
f
e
a
tur
e
of
QR
c
ode
is
that
it
c
a
n
be
r
e
a
d
a
f
ter
pa
r
tl
y
da
mage
d.
M
a
ke
it
s
f
e
a
tur
e
of
QR
c
ode
ve
r
y
s
tr
ong
a
nd
popular
in
the
s
e
c
ur
it
y
a
nd
a
dve
r
ti
s
ing
indus
tr
y
[
2]
.
F
o
r
thi
s
r
e
a
s
on,
QR
c
ode
is
c
hos
e
n
in
thi
s
pa
pe
r
.
S
e
ve
r
a
l
publi
s
he
d
wor
ks
a
r
e
r
e
late
d
to
the
ob
jec
ti
ve
s
of
thi
s
wor
k
f
or
e
xa
mpl
e
,
S
im
Hie
w
M
oi
e
t
a
l.
[
1]
pr
e
s
e
nt
a
ne
w
a
ppr
oa
c
h
by
us
ing
ir
is
template
to
c
r
e
a
te
a
unique
a
nd
mor
e
s
e
c
ur
e
e
nc
r
yp
ti
on
ke
y
a
nd
us
e
d
AE
S
a
lgor
it
hm
to
e
nc
r
ypt
a
nd
de
c
r
ypt
da
t
a
of
identit
y
da
ta.
T
e
jas
M
ohod
e
t
a
l.
[
2]
im
p
leme
n
t
a
s
ys
tem
that
take
s
pr
ope
r
ti
e
s
of
both
ir
is
a
nd
QR
c
ode
;
thi
s
e
nha
nc
e
s
s
ys
tem
is
olation,
c
os
t
e
f
f
e
c
ti
ve
a
nd
r
e
li
a
ble
s
e
c
ur
it
y
s
ys
tem.
M
.
A.
M
ur
il
lo
-
E
s
c
oba
r
e
t
a
l.
[
3
]
pr
opos
e
d
a
ne
w
f
inger
pr
int
template
pr
otec
ti
on
ba
s
e
d
on
logi
s
ti
c
map
a
nd
M
ur
il
lo
-
E
s
c
oba
r
’
s
a
lgor
it
hm.
M
oha
mm
a
d
S
olt
a
ni
a
nd
Amid
Kha
ti
bi
B
a
r
ds
ir
i
[
4]
p
r
opos
e
d
a
hybr
id
a
lgor
it
hm
f
or
e
nc
r
ypti
on
a
nd
s
tega
nogr
a
phy,
the
y
ge
ne
r
a
ted
the
QR
-
c
ode
us
ing
input
text
a
nd
e
nc
r
ypted
the
r
e
s
ult
ing
QR
im
a
ge
us
ing
2D
logi
s
ti
c
map
then
c
onve
r
t
the
e
nc
r
ypted
Q
R
to
text,
a
f
ter
that
they
e
nc
r
ypted
the
or
igi
na
l
text
us
ing
A
E
S
a
lgo
r
it
hm
a
nd
hidi
ng
it
us
ing
L
S
B
a
lgor
it
hm
.
S
r
uthi
B
.
As
ok
e
t
a
l
[
5
]
e
xtr
a
c
ts
a
s
e
c
r
e
t
ke
y
f
r
om
ir
is
im
a
ge
a
nd
us
e
it
to
e
nc
r
ypt
da
ta.
Nis
hi
P
r
a
s
a
d
e
t
a
l
.
[
6]
Us
e
d
thr
e
e
leve
l
o
f
s
e
c
ur
it
y
f
or
im
a
ge
e
nc
r
ypti
on.
T
he
y
us
e
d
logi
s
ti
c
map,
s
e
c
r
e
t
k
e
y
c
r
yptogr
a
phy,
a
nd
QR
c
ode
s
.
M
.
M
a
r
y
S
ha
nth
i
R
a
ni
a
nd
K.
R
os
e
mar
y
E
uphr
a
s
ia
[
7]
pr
opos
e
d
a
n
e
nc
r
ypti
on
method
by
us
ing
QR
c
ode
f
o
r
mes
s
a
ge
e
nc
r
ypti
on
a
nd
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
1
,
F
e
br
ua
r
y
2020
:
289
-
300
290
ge
ne
r
a
te
a
nother
QR
c
ode
f
or
a
uthentica
ti
on
a
nd
h
idi
ng
it
in
c
ove
r
im
a
ge
.
A.
Hus
a
in
a
nd
R
.
Ali
[
8
]
i
nc
r
e
a
s
e
d
the
s
e
c
ur
it
y
of
f
inge
r
p
r
int
im
a
ge
ba
s
e
d
on
QR
c
ode
to
e
xtr
a
c
t
e
nc
r
ypti
on
ke
y.
I
n
thi
s
pa
pe
r
ther
e
is
a
we
a
kne
s
s
in
the
qua
li
ty
of
e
nc
r
ypted
im
a
ge
,
s
o
we
s
ugge
s
t
a
modi
f
ying
f
or
th
is
method
to
g
e
t
be
tt
e
r
r
e
s
ult
s
.
I
n
thi
s
pa
pe
r
,
a
ne
w
a
lgor
it
hm
is
pr
opos
e
d
f
or
I
r
is
im
a
ge
e
nc
r
ypti
on
ba
s
e
d
on
QR
c
od
e
f
e
a
tur
e
e
xtr
a
c
ti
on
a
nd
c
ha
oti
c
map.
T
his
a
lgor
it
h
m
will
incr
e
a
s
e
the
s
e
c
ur
it
y
of
I
r
is
im
a
ge
by
us
ing
QR
c
ode
to
ge
ne
r
a
te
the
ke
y,
high
dif
f
us
ion
that
pr
ovided
by
pe
r
mu
tat
io
n
method
a
nd
the
c
ha
oti
c
s
ys
tem
that
pr
ovides
the
c
onf
us
ion.
T
he
r
a
ndomnes
s
of
the
ke
y
that
ge
ne
r
a
ted
us
ing
QR
c
ode
wa
s
tes
ted
us
ing
NI
S
T
tes
ts
a
nd
pr
ove
d
to
be
e
f
f
icie
nt.
T
he
r
e
s
ult
s
of
thi
s
wor
k
a
r
e
c
ompa
r
e
d
with
the
r
e
s
ult
s
of
[
8]
by
his
togr
a
m
,
e
ntr
op
y
,
UA
C
I
,
NPC
R
,
c
or
r
e
lation
a
nd
P
S
NR
.
T
he
e
xpe
r
im
e
ntal
r
e
s
ult
s
s
how
that
the
pr
opos
e
d
a
ppr
oa
c
h
is
mor
e
e
f
f
icie
nt
a
nd
s
e
c
ur
e
f
or
i
r
is
im
a
ge
e
nc
r
ypti
on.
T
he
r
e
mi
nde
r
of
thi
s
pa
pe
r
is
a
r
r
a
nge
d
a
s
f
oll
ows
.
I
n
s
e
c
ti
on
2
the
methods
tha
t
us
e
d
in
pr
opos
e
d
a
lgor
it
h
m
will
be
p
r
e
s
e
nted.
I
n
s
e
c
ti
on
3
t
he
pr
opos
e
d
method
is
de
s
c
r
ibed
in
de
tails
.
I
n
s
e
c
ti
on
4
QR
ke
y
NI
S
T
tes
ts
will
be
d
is
play.
T
he
s
e
c
ur
it
y
a
na
lys
is
is
s
hown
in
s
e
c
ti
on
5.
F
inally
,
the
c
onc
lus
ions
a
r
e
s
hown
in
s
e
c
ti
on
6.
2.
T
HE
ORE
T
I
CA
L
B
AC
KG
ROUN
D
2.
1.
Qu
i
c
k
r
e
s
p
on
s
e
c
od
e
T
he
quick
r
e
s
pons
e
(
QR
)
C
ode
wa
s
f
ir
s
t
de
s
igned
by
J
a
pa
ne
s
e
c
ompany
f
or
c
a
r
s
indus
tr
y
c
a
ll
e
d
De
ns
o
-
W
a
ve
in
1994
to
tr
a
c
k
c
a
r
pa
r
ts
.
QR
c
ode
i
s
kinds
of
ba
r
-
c
ode
that
c
a
n
be
r
e
c
ognize
us
ing
a
ba
r
-
c
ode
r
e
a
de
r
.
I
t
c
a
n
c
ontain
e
nc
ode
d
inf
o
r
mat
ion
li
ke
we
bs
it
e
UR
L
s
,
da
ta,
a
nd
texts
,
e
tc.
T
oda
y
,
Q
R
c
ode
s
a
r
e
wide
ly
us
e
d
a
s
i
t
us
e
d
in
c
ompanie
s
,
bus
ines
s
e
s
a
nd
gove
r
nment
de
pa
r
t
ments
be
c
a
us
e
of
thei
r
r
e
li
a
bil
it
y
a
nd
e
a
s
e
of
us
e
[
2,
9]
.
Als
o,
QR
c
a
n
us
e
in
s
e
c
ur
it
y
pur
pos
e
.
T
he
inf
or
mation
c
ontaine
d
in
the
c
ode
c
a
n
be
e
nc
r
ypted
a
nd
de
c
r
ypted
by
us
ing
s
pe
c
ial
s
of
twa
r
e
e
ns
ur
ing
be
tt
e
r
s
e
c
u
r
it
y.
QR
s
tr
uc
tur
e
i
s
s
hown
in
F
igu
re
1.
QR
c
ode
s
c
ontain
many
a
r
e
a
s
that
e
xp
lain
a
s
f
oll
ow:
a)
F
inder
pa
tt
e
r
n
:
I
t
c
ons
is
ts
of
3
s
ymm
e
tr
ica
l
s
tr
uc
tu
r
e
s
a
t
thr
e
e
c
or
ne
r
s
o
f
the
QR
c
ode
with
one
mi
s
s
ing
a
t
the
bott
om
r
ight
.
E
a
c
h
pa
tt
e
r
n
is
ba
s
e
d
on
a
3x3
matr
ix
of
blac
k
modul
e
s
s
ur
r
ounde
d
by
whi
te
mod
ules
that
a
r
e
a
ga
in
s
ur
r
ounde
d
by
blac
k
modul
e
s
.
T
he
f
i
nde
r
pa
tt
e
r
ns
e
na
ble
the
de
c
ode
r
s
of
twa
r
e
to
r
e
c
ognize
the
QR
C
ode
a
nd
de
ter
mi
ne
the
c
or
r
e
c
t
o
r
ienta
ti
on
[
10
]
.
b)
T
im
ing
pa
tt
e
r
n:
thi
s
pa
tt
e
r
n
f
or
dis
c
ove
r
ing
the
c
e
ntr
a
l
c
oor
dinate
of
e
a
c
h
da
ta
c
e
ll
the
QR
c
ode
with
blac
k
a
nd
white
de
s
igns
a
r
e
plac
e
d
a
lt
e
r
na
tely
in
tw
o
plac
e
s
hor
izonta
ll
y
a
nd
ve
r
t
ica
ll
y
be
twe
e
n
the
f
in
de
r
pa
tt
e
r
ns
.
e
ve
n
if
the
c
ode
is
d
is
tor
ted
pa
r
ti
a
ll
y
o
r
a
n
e
r
r
o
r
f
or
the
c
e
ll
p
it
c
h,
thi
s
a
ll
ows
a
c
c
ur
a
te
r
e
a
di
ng
of
c
e
ntr
a
l
c
oor
d
inate
s
.
I
t
tr
a
c
ks
the
t
im
e
of
incomi
ng
c
ode
[
11,
12
].
c)
Alignm
e
nt
pa
tt
e
r
n:
a
model
f
o
r
c
or
r
e
c
ti
ng
the
d
is
tor
ti
on
of
the
c
ode
.
I
t
is
pa
r
ti
c
ula
r
ly
e
f
f
icie
nt
f
or
c
or
r
e
c
ti
ng
nonli
ne
a
r
dis
tor
ti
ons
.
T
he
c
e
ntr
a
l
c
oor
dinate
of
the
a
li
ne
ment
pa
tt
e
r
n
will
be
dis
c
ove
r
e
d
to
co
r
r
e
c
t
the
dis
tor
ti
on
o
f
the
s
ymbol
.
F
or
thi
s
pu
r
po
s
e
,
a
n
is
olate
d
blac
k
c
e
ll
is
di
r
e
c
ted
in
the
c
onjun
c
t
ion
pa
tt
e
r
n
f
or
ge
tt
ing
it
e
a
s
y
to
de
tec
t
the
c
e
ntr
a
l
c
oor
dinate
of
the
a
li
gnment
pa
tt
e
r
n
[
13
]
.
d)
Quie
t
z
one
:
t
his
a
r
e
a
e
mpt
ies
f
r
o
m
a
ny
mar
kings
.
A
mar
gin
s
pa
c
e
is
ne
e
de
d
f
or
r
e
a
ding
QR
c
ode
r
ig
htl
y.
T
his
f
r
e
e
z
one
make
s
the
QR
c
ode
s
ymbol
e
a
s
y
to
r
e
a
d
by
the
C
C
D
s
e
ns
or
[
14
]
.
e)
Da
ta
a
r
e
a
:
i
n
thi
s
a
r
e
a
the
QR
c
ode
da
ta
a
nd
e
r
r
or
c
or
r
e
c
ti
on
c
ode
will
be
s
tor
e
d
.
T
he
da
ta
a
r
e
a
is
r
e
pr
e
s
e
nt
e
d
by
the
g
r
e
y
a
r
e
a
in
F
igu
r
e
1
.
T
he
da
ta
will
be
e
nc
ode
d
int
o
1’
s
a
nd
0’
s
.
T
he
binar
y
nu
mb
e
r
s
will
be
c
onve
r
ted
int
o
white
a
nd
blac
k
c
e
ll
s
a
nd
th
e
n
will
be
a
r
r
a
nge
d
[
14
]
.
F
igur
e
1.
QR
s
tr
uc
tur
e
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
I
r
is
image
s
e
nc
r
y
pti
on
bas
e
d
on
QR
c
ode
and
c
ha
oti
c
map
(
R
aniah
A
li
M
us
taf
a
)
291
2.
2.
L
ogis
t
ic
m
a
p
A
one
-
dim
e
ns
ional
logi
s
ti
c
map
is
de
s
c
r
ibed
in
the
f
oll
owing
e
q
ua
ti
on
:
+
1
=
μ
n
(
1
−
n
)
(
1)
w
he
r
e
x
n
r
e
f
e
r
to
the
nth
output
a
nd
μ
is
the
map’
s
p
a
r
a
mete
r
a
nd
the
r
a
nge
of
it
s
hould
be
wi
thi
n
the
pe
r
iod
(
3.
56,
4
]
.
T
he
ini
ti
a
l
va
lue
x
0
a
nd
μ
c
a
n
be
us
e
d
a
s
a
ke
y
of
e
nc
r
ypti
on
[
15
]
.
W
hil
e
the
2D
logi
s
ti
c
ha
s
mor
e
c
ompl
e
x
be
ha
vior
s
in
i
mage
e
nc
r
ypti
on
than
a
1
D
logi
s
ti
c
map
f
or
thi
s
r
e
a
s
on
thi
s
pa
pe
r
us
e
it
t
o
e
nc
r
ypt
im
a
ge
s
.
2D
logi
s
ti
c
map
c
a
n
be
s
how
a
s
f
oll
ow
[
4]
:
+
1
=
(
3
+
1
)
(
1
−
)
+
1
=
(
3
+
1
+
1
)
(
1
−
)
w
he
r
e
r
is
the
pa
r
a
mete
r
o
f
s
ys
tem
a
nd
(
x
n
,
y
n
)
is
is
the
pa
ir
-
wis
e
point
a
t
t
he
n
it
e
r
a
ti
on.
As
s
hown
in
F
igur
e
2
the
s
c
a
tt
e
r
plot
of
30
,
000
point
s
of
2D
log
is
ti
c
map
us
ing
the
pa
r
a
mete
r
r
=
1.
19
a
nd
the
ini
ti
a
l
va
lue
(
x
0
,
y
0
)
a
t
(
0
.
8309,
0
.
3342)
.
F
ig
ur
e
2.
A
t
r
a
jec
tor
y
o
f
2D
logi
s
ti
c
map
[
4]
3.
P
ROP
OS
E
D
S
CHE
M
E
T
he
pr
opos
e
d
s
c
he
me
c
ontains
thr
e
e
main
ope
r
a
ti
ons
a
r
e
:
pe
r
mut
a
ti
on
,
e
nc
r
ypti
on
with
QR
ke
y
a
nd
e
nc
r
ypti
on
with
2D
logi
s
ti
c
map.
T
he
ge
ne
r
a
l
s
ys
tem
s
tr
uc
tur
e
is
s
hown
in
F
igu
r
e
3.
3.
1.
P
e
r
m
u
t
at
io
n
m
e
t
h
od
P
e
r
mut
a
ti
on
is
mos
t
s
igni
f
ica
nt
s
tep
in
thi
s
a
lgor
i
t
hm.
I
t
wo
r
ks
to
block
the
high
c
or
r
e
lation
a
mong
pixels
of
im
a
ge
to
incr
e
a
s
e
the
s
e
c
ur
it
y
of
im
a
ge
e
nc
r
ypti
on
a
lgor
it
hm
.
I
n
thi
s
method
we
r
e
li
e
d
on
s
c
r
a
mbl
ing
r
ows
a
nd
c
olum
ns
ba
s
e
d
on
s
um
invar
ianc
e
of
r
ow
a
nd
c
ol
umn
th
r
ough
c
ir
c
ular
s
hif
t
pr
oc
e
s
s
.
I
n
the
be
ginni
ng
it
s
hif
ts
e
a
c
h
r
ow
in
im
a
ge
by
th
e
tot
a
l
s
um
o
f
the
r
ow
a
nd
c
olum
n's
pixel
va
lues
a
nd
s
a
ve
the
r
e
s
ult
in
a
va
r
iable
,
a
nd
then
im
pleme
nt
the
s
a
me
method
in
e
a
c
h
c
olum
n
a
nd
s
a
ve
the
r
e
s
ult
i
n
a
nother
va
r
iable
.
F
inally,
im
pleme
nt
Xor
ope
r
a
ti
on
be
twe
e
n
the
two
r
e
s
ult
s
.
F
igur
e
4
s
hown
the
plain
ir
is
i
mage
a
nd
the
r
e
s
ult
ing
im
a
ge
a
f
ter
pe
r
mu
tation.
3.
2.
QR
k
e
y
ge
n
e
r
at
ion
T
he
f
ir
s
t
s
tep
is
ge
ne
r
a
ti
ng
the
QR
c
ode
de
pe
nding
on
the
input
text
,
then
im
pleme
nt
p
r
e
pr
oc
e
s
s
ing
ope
r
a
ti
ons
s
uc
h
a
s
his
togr
a
m
e
qua
li
z
a
ti
on
a
nd
binar
iza
ti
on
on
QR
im
a
ge
.
Af
ter
that
the
f
e
a
tur
e
s
will
be
e
xtr
a
c
ted
f
r
om
QR
im
a
ge
by
us
ing
c
onvolut
ion
.
T
he
s
e
f
e
a
tur
e
s
a
r
e
r
e
pr
e
s
e
nti
ng
a
r
a
ndom
ke
y
wh
ich
us
e
d
to
f
ir
s
t
e
nc
r
ypti
on
p
r
oc
e
s
s
.
(
2)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
1
,
F
e
br
ua
r
y
2020
:
289
-
300
292
(
a
)
(
b)
F
igur
e
3.
Ge
ne
r
a
l
s
tr
uc
tur
e
of
pr
opos
e
d
s
ys
tem
F
igur
e
4.
(
a
)
P
lain
ir
is
i
mage
,
(
b)
P
e
r
mut
e
d
i
r
is
im
a
ge
3.
3.
Hi
s
t
ogr
am
e
q
u
ali
z
at
io
n
I
t’
s
a
method
f
o
r
a
djus
t
im
a
ge
c
ontr
a
s
t.
L
e
t
f
be
a
n
i
mage
r
e
pr
e
s
e
nted
a
s
a
matr
ix
r
x
c
of
int
e
ge
r
pixel
in
tens
it
ies
r
a
nging
f
r
om
0
to
L
-
1.
W
he
r
e
L
is
the
number
of
gr
a
y
leve
l
va
lues
in
im
a
ge
,
o
f
ten
256.
L
e
t
p
is
the
nor
malize
d
his
togr
a
m
of
f
[
16
].
=
nu
m
b
e
r
of
pi
x
e
ls
w
i
t
h
i
nt
e
nsi
t
y
n
ot
a
l
nu
m
b
er
of
pi
x
e
ls
(3
)
T
he
his
togr
a
m
e
qua
li
z
e
d
im
a
ge
g
will
be
de
f
ined
by
:
,
=
(
(
−
1
)
∑
,
=
0
)
(4
)
in
thi
s
pa
pe
r
,
a
f
ter
tr
a
ns
f
or
m
a
ny
input
text
to
QR
c
ode
,
the
ne
xt
s
tep
is
his
togr
a
m
e
qua
li
z
a
ti
on
a
nd
t
he
r
e
s
ult
of
thi
s
s
tep
s
hown
in
T
a
ble
1
.
T
a
ble
1.
T
he
r
e
s
ult
f
o
r
QR
c
ode
,
his
togr
a
m
e
qua
li
z
a
ti
on
a
nd
binar
iza
ti
on
N
o. bi
t
of
te
xt
8
-
bi
t
16
-
bi
t
24
-
bi
t
32
-
bi
t
40
-
bi
t
48
-
bi
t
Q
R
C
ode
H
is
to
gr
a
m
E
qua
li
z
a
ti
on
B
in
a
r
iz
a
ti
on
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
I
r
is
image
s
e
nc
r
y
pti
on
bas
e
d
on
QR
c
ode
and
c
ha
oti
c
map
(
R
aniah
A
li
M
us
taf
a
)
293
3.
4.
B
in
ar
izat
ion
B
inar
iza
ti
on
is
the
pr
oc
e
s
s
of
c
onve
r
t
a
gr
a
y
leve
l
i
mage
to
binar
y
im
a
ge
,
th
is
s
tep
is
a
n
im
por
tant
s
tep
to
dis
ti
nguis
h
blac
k
-
a
nd
-
white
modul
e
a
c
c
ur
a
tely
in
QR
c
ode
i
mage
s
.
S
o
,
we
pr
opos
e
d
us
e
bin
a
r
iza
ti
on
ope
r
a
ti
on
to
e
xt
r
a
c
t
the
f
e
a
tur
e
s
f
r
om
QR
i
mage
.
I
n
thi
s
method,
the
QR
c
ode
is
d
ivi
de
d
int
o
16x16
0
-
bit
blocks
.
T
he
va
lue
of
the
int
e
ns
it
y
of
thes
e
blocks
is
a
na
lyze
d
a
nd
the
pixel
va
lue
is
then
de
ter
mi
ne
d
a
s
1
if
the
pixel
va
lue
is
gr
e
a
ter
than
the
a
ve
r
a
ge
in
tens
it
y
of
that
block
,
o
ther
wis
e
make
it
e
qu
a
l
to
0
.
T
a
ble
1
s
hows
the
r
e
s
ult
of
QR
c
ode
,
his
togr
a
m
e
qua
li
z
a
ti
on
a
n
d
b
inar
iza
ti
on
f
or
number
bit
of
text
(
8
-
bit
,
16
-
bi
t,
24
-
bit
,
32
-
bit
,
40
-
bit
,
48
-
bit
)
.
3.
5.
F
e
a
t
u
r
e
e
xt
r
ac
t
ion
u
s
in
g
c
on
vol
u
t
ion
T
he
c
onvolut
ion
be
twe
e
n
two
f
unc
ti
ons
(
)
,
(
)
whic
h
we
de
note
by
(
∗
)
(
)
,
the
c
onvolut
ion
gives
the
inver
s
e
L
a
plac
e
tr
a
ns
f
or
m
o
f
a
pr
oduc
t
of
two
t
r
a
ns
f
or
med
f
unc
ti
ons
,
f
or
thi
s
r
e
a
s
on
it
’
s
a
n
i
mpor
tant
c
ons
tr
uc
t
[
17
]:
L
−
1
(
F
(
s
)
G(
s
)
)
=
(
∗
)
(
t)
(5
)
If
(
)
,
(
)
a
r
e
c
a
us
a
l
f
unc
ti
ons
then
their
c
onvolut
ion
is
de
f
ined
by:
(
∗
)
(
)
=
∫
(
−
)
(
)
0
(6
)
the
pr
opos
e
d
s
ys
tem
us
e
d
c
onvolut
ion
f
or
e
xtr
a
c
t
t
he
f
e
a
tur
e
s
f
r
om
QR
c
ode
to
ge
ne
r
a
te
r
a
ndom
ke
y.
T
a
ble
2
s
hows
the
mas
ks
that
us
e
d
in
c
onvolut
ion,
a
nd
the
be
s
t
r
e
s
ult
his
togr
a
m
e
qua
li
z
a
ti
on
a
nd
c
onvolut
ion
il
lus
tr
a
te
in
the
T
a
ble
3.
T
a
ble
2.
T
he
mas
ks
that
us
e
d
in
c
onvolut
ion
M
a
tr
ix
N
o.
3x3 ma
s
ks
C
onvolut
io
n M
a
tr
ix
1
2
3
4
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
1
,
F
e
br
ua
r
y
2020
:
289
-
300
294
T
a
ble
3.
T
he
be
s
t
r
e
s
ult
of
his
togr
a
m
e
qua
li
z
a
ti
on
a
nd
c
onvolut
ion
3.
6.
E
n
c
r
yp
t
ion
a
lgorit
h
m
I
nput:
plain
im
a
ge
(
m
)
,
QR
_k
e
y
,
L
og
is
ti
c
_k
e
y
Output:
e
nc
r
ypted
im
a
ge
(
E
)
S
tep1:
r
e
a
d
c
olor
e
d
im
a
ge
(
m
)
S
tep2:
f
or
c
←
1:
s
ize
(
m
)
I
1
←
c
ir
c
ular
_s
hif
t
(
s
um
(
m
(
c
olum
n)
)
)
e
nd
f
or
r
←
1:
s
ize
(
m
)
I
2
←
c
ir
c
ular
_s
hif
t
(
s
um
(
m
(
r
ow
)
)
)
e
nd
p
←
xor
(
I
1
,
I
2
)
S
tep3:
k
←
xor
(
Q
R
_k
e
y
,
p
)
S
tep4:
E
←
xor
(
L
ogis
ti
c
_k
e
y
,
k
)
S
tep5:
e
nd
4.
S
E
CU
RI
T
Y
AN
A
L
YSI
S
I
n
thi
s
s
e
c
ti
on
we
pr
e
s
e
nt
a
s
e
r
ies
o
f
tes
ts
r
e
s
ult
s
t
o
pr
oo
f
the
e
f
f
e
c
ti
ve
ne
s
s
of
the
p
r
opos
e
d
s
c
he
me
a
nd
c
ompar
e
the
r
e
s
ult
s
with
[
8
]
.
I
n
thi
s
tes
t
we
us
e
d
da
tas
e
t
that
c
a
ptu
r
e
d
by
M
icha
l
Dobe
š
a
nd
L
ibor
M
a
c
ha
la.
T
he
da
tas
e
t
c
ontains
3x128
i
r
is
im
a
ge
s
.
T
he
ir
is
e
s
im
a
ge
s
we
r
e
s
c
a
nne
d
us
ing
T
OPC
ON
T
R
C
50I
A
opti
c
a
l
de
vice
c
onne
c
ted
with
S
ON
Y
DX
C
-
950P
3C
C
D
c
a
mer
a
[
18
]
.
T
he
e
xpe
r
im
e
nts
a
r
e
pe
r
f
or
med
vi
a
M
a
tl
a
b
R
2013a
on
a
c
omput
e
r
wi
th
I
n
tel
C
or
e
i7
C
P
U
1
.
9
9
GH
z
,
8
GB
of
R
AM
.
4.
1.
QR
k
e
y
t
e
s
t
s
Af
ter
f
e
a
tur
e
s
e
xtr
a
c
ti
on
f
or
m
QR
c
ode
.
T
he
ge
ne
r
a
ted
ke
y
is
tes
ted
by
NI
S
T
tes
ts
,
a
nd
the
r
e
s
ult
s
o
f
ke
y
tes
ts
a
r
e
il
lus
tr
a
ted
in
T
a
ble
4
.
4.
2.
Hi
s
t
ogr
am
an
alys
is
His
togr
a
m
a
na
lys
is
is
us
e
d
to
e
xplain
the
dif
f
us
i
on
a
nd
c
onf
us
ion
c
ha
r
a
c
ter
is
ti
c
o
f
the
e
nc
r
ypti
on
a
lgor
it
hm.
T
a
ble
5
s
hown
the
di
f
f
e
r
e
nc
e
in
dis
tr
ib
uted
of
im
a
ge
a
mong
plain
ir
is
i
mage
,
i
ts
pe
r
mut
a
ti
on
a
nd
it
s
e
nc
r
ypti
on
.
4.
3.
Cor
r
e
lat
ion
a
n
alys
is
T
he
c
or
r
e
lation
be
twe
e
n
two
a
djac
e
nt
pixels
in
the
or
dinar
y
im
a
ge
is
pe
r
mane
ntl
y
s
tr
ong,
a
nd
the
va
lues
of
c
or
r
e
lation
a
r
e
s
o
c
los
e
to
1.
F
or
thi
s
r
e
a
s
on,
the
c
or
r
e
lation
mus
t
be
r
e
duc
e
s
igni
f
ica
ntl
y
in
No
M
a
s
k
s
1
2
3
4
5
6
7
8
9
1
C
onvo
H
is
t
73
217
386
271
270
490
225
93
0
2
C
onvo
H
is
t
0
56
455
520
444
495
55
0
0
3
C
onvo
H
is
t
95
211
320
225
252
291
243
121
161
4
C
onvo
H
is
t
121
243
291
252
225
320
211
95
106
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
I
r
is
image
s
e
nc
r
y
pti
on
bas
e
d
on
QR
c
ode
and
c
ha
oti
c
map
(
R
aniah
A
li
M
us
taf
a
)
295
a
n
e
f
f
icie
nt
e
nc
r
ypti
on
a
lgor
it
hm
a
nd
the
va
lue
s
o
c
los
e
to
0
[
19
,
20
]
.
W
e
c
a
n
c
omput
e
the
c
o
r
r
e
lation
c
oe
f
f
icie
nts
f
or
thr
e
e
di
r
e
c
ti
ons
hor
izonta
l,
ve
r
ti
c
a
l
,
a
nd
diagona
l
,
a
c
c
or
ding
to
the
f
oll
owing
e
qua
ti
ons
:
c
ov(
x,
y
)
=
E
{(
x
−
E
(
x)
)
(
y
−
E
(
y
)
)
}
(
7)
r
xy
=
c
ov
(
x, y)
√
D
(
x)
√
D
(
y)
(
8)
(
)
=
1
∑
=
1
(
9)
(
)
=
1
∑
(
−
(
)
)
2
=
1
(
10)
in
(
3
)
,
x
a
nd
y
a
r
e
the
va
lues
of
two
ne
ighbor
ing
pixels
in
the
im
a
ge
,
D(
x)
a
nd
E
(
x
)
a
r
e
the
va
r
ia
nc
e
a
nd
the
e
xpe
c
tation
of
x
.
N
in
(
9)
a
nd
(
10
)
is
the
numbe
r
of
pixels
in
im
a
ge
.
F
ig
ur
e
5
s
hown
the
ho
r
izonta
l,
ve
r
ti
c
a
l
an
d
diagona
l
c
or
r
e
lation
c
oe
f
f
icie
nt
in
plain
a
nd
e
n
c
r
ypted
ir
is
im
a
ge
.
T
a
ble
6
s
hown
the
r
e
s
ult
s
o
f
c
o
r
r
e
lation
f
or
s
a
mpl
e
of
plain
ir
is
im
a
ge
s
a
nd
e
nc
r
ypted
im
a
g
e
s
a
nd
c
ompar
e
d
it
with
[
8
].
T
a
ble
4.
T
e
s
t
ke
y
r
e
s
ult
s
N
o.
T
e
s
t
T
ype
P
a
r
a
me
te
r
iz
e
t
e
s
t
N
o.
T
e
s
t
S
uc
c
e
s
s
F
a
il
ur
e
%
1
G
us
in
g S
H
A
-
1
A
ppr
oxi
ma
te
E
nt
r
opy T
E
S
T
255
255
0
100%
B
L
O
C
K
F
R
E
Q
U
E
N
C
Y
T
E
S
T
255
255
0
100%
C
U
M
U
L
A
T
I
V
E
S
U
M
S
T
E
S
T
510
503
7
98.6%
D
is
c
r
e
te
F
F
T
T
E
S
T
255
255
0
100%
F
r
e
que
nc
y T
E
S
T
255
253
2
99.2%
L
E
M
P
E
L
-
Z
I
V
C
O
M
P
R
E
S
S
I
O
N
T
E
S
T
255
253
2
99.2%
li
ne
a
r
-
c
ompl
e
xi
ty
255
255
0
100%
N
on pe
r
io
di
c
-
te
mpl
a
te
s
255
255
0
100%
ove
r
la
ppi
ng
-
te
mpl
a
te
s
37740
35855
1885
95%
r
a
ndom
-
e
xc
ur
s
io
ns
255
255
0
100%
r
uns
255
255
0
100%
S
e
r
ia
l
255
252
3
98.8%
2
L
in
e
a
r
C
in
gr
ue
nt
ia
l
A
ppr
oxi
ma
te
E
nt
r
opy T
E
S
T
128
128
0
100%
B
L
O
C
K
F
R
E
Q
U
E
N
C
Y
T
E
S
T
128
123
5
96%
C
U
M
U
L
A
T
I
V
E
S
U
M
S
T
E
S
T
256
248
8
96.8%
D
is
c
r
e
te
F
F
T
T
E
S
T
128
128
0
100%
F
R
E
Q
U
E
N
C
Y
T
E
S
T
128
123
5
96%
L
E
M
P
E
L
-
Z
I
V
C
O
M
P
R
E
S
S
I
O
N
T
E
S
T
128
0
128
0%
li
ne
a
r
-
c
ompl
e
xi
ty
128
128
0
100%
N
on pe
r
io
di
c
-
te
mpl
a
te
s
18944
16675
2269
88%
ove
r
la
ppi
ng
-
te
mpl
a
te
s
128
128
0
100%
R
A
N
K
T
E
S
T
128
128
0
100%
R
U
N
S
T
E
S
T
128
126
2
98.4%
S
E
R
I
A
L
T
E
S
T
256
250
6
97.6%
3
B
lu
m
-
B
lu
m
-
S
hub
A
ppr
oxi
ma
te
E
nt
r
opy
128
128
0
100%
B
L
O
C
K
F
R
E
Q
U
E
N
C
Y
T
E
S
T
128
125
3
97.6%
C
U
M
U
L
A
T
I
V
E
S
U
M
S
256
253
3
98.8%
D
is
c
r
e
te
F
F
T
128
128
0
100%
F
R
E
Q
U
E
N
C
Y
T
E
S
T
128
125
3
97.6%
L
E
M
P
E
L
-
Z
I
V
C
O
M
P
R
E
S
S
I
O
N
T
E
S
T
128
128
0
100%
li
ne
a
r
-
c
ompl
e
xi
ty
128
128
0
100%
N
on
pe
r
io
di
c
-
te
mpl
a
te
s
18944
16705
2239
88%
ove
r
la
ppi
ng
-
te
mpl
a
te
s
128
128
0
100%
R
A
N
K
T
E
S
T
128
128
0
100%
R
U
N
S
T
E
S
T
128
128
0
100%
S
E
R
I
A
L
T
E
S
T
256
255
1
99.6%
4
XOR
A
ppr
oxi
ma
te
E
nt
r
opy
180
180
0
100%
B
L
O
C
K
F
R
E
Q
U
E
N
C
Y
T
E
S
T
180
180
0
100%
D
is
c
r
e
te
F
F
T
180
180
0
100%
C
U
M
U
L
A
T
I
V
E
S
U
M
S
362
362
0
100%
F
R
E
Q
U
E
N
C
Y
T
E
S
T
180
179
1
99.4%
L
O
N
G
E
S
T
R
U
N
S
O
F
O
N
E
S
T
E
S
T
180
180
0
100%
L
E
M
P
E
L
-
Z
I
V
C
O
M
P
R
E
S
S
I
O
N
T
E
S
T
180
180
0
100%
R
A
N
K
T
E
S
T
180
180
0
100%
N
O
N
P
E
R
I
O
D
I
C
T
E
M
P
L
A
T
E
S
T
E
S
T
26788
21429
5359
79.9%
R
U
N
S
T
E
S
T
180
177
3
98.3%
S
E
R
I
A
L
T
E
S
T
362
357
5
98.6%
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
1
,
F
e
br
ua
r
y
2020
:
289
-
300
296
T
a
ble
5.
His
togr
a
m
a
na
lys
is
of
o
r
igi
na
l
,
pe
r
mut
e
d
a
nd
e
nc
r
ypted
R
GB
ir
is
im
a
ge
D
e
s
c
r
ip
ti
on
I
ma
ge
R
, G
a
nd B
ba
nds
H
is
to
gr
a
m
O
r
ig
in
a
l
ir
is
i
ma
ge
P
e
r
mut
e
d
im
a
ge
A
f
te
r
a
dd
Q
R
ke
y
A
f
te
r
a
dd
L
ogi
s
ti
c
ke
y
F
igur
e
5.
C
or
r
e
lation
of
two
ne
ighbor
ing
pixels
in
plain
a
nd
e
nc
r
ypted
i
r
is
im
a
ge
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
I
r
is
image
s
e
nc
r
y
pti
on
bas
e
d
on
QR
c
ode
and
c
ha
oti
c
map
(
R
aniah
A
li
M
us
taf
a
)
297
T
a
ble
6.
C
ompar
ing
c
or
r
e
lation
c
oe
f
f
icie
nts
of
two
ne
ighbor
ing
pixels
in
the
p
lain
a
nd
e
nc
r
ypted
im
a
ge
s
be
twe
e
n
pr
opos
e
d
s
ys
tem
a
nd
[
8
]
I
ma
ge
s
C
or
r
e
la
ti
on of
pr
opos
e
d s
ys
te
m
C
or
r
e
la
ti
on of
A
. H
us
a
in
[
8]
V
e
r
ti
c
a
l
H
or
iz
ont
a
l
D
ia
gona
l
V
e
r
ti
c
a
l
H
or
iz
ont
a
l
D
ia
gona
l
0.0738
0.0836
0.0743
0.0138
-
6.6318e
-
04
0.01
-
0.0026
-
0.0015
0.0043
0.0743
0.0654
-
0.0529
0.0040
-
3.1145e
-
04
0.0027
-
0.0041
0.0280
-
0.0143
3.5373e
-
04
0.0046
0.0034
-
0.0184
0.0544
-
0.0165
4.
4.
I
n
f
or
m
a
t
ion
e
n
t
r
op
y
an
a
lys
is
One
of
the
ve
r
y
im
po
r
tant
mea
s
ur
e
to
c
omput
e
t
he
r
a
ndomnes
s
is
inf
o
r
mation
e
ntr
opy.
I
t
c
a
n
be
c
omput
e
d
by:
H
(
m
)
=
∑
p(
m
i
)
log
2
1
p(
m
i
)
2
n
-
1
i=0
(
11)
in
(
11)
,
m
is
a
s
a
mpl
e
,
n
is
the
number
o
f
s
a
mpl
e
s
,
a
nd
p(
m)
is
the
p
r
oba
bil
it
y
of
s
ymbol
m.
we
c
a
n
ge
t
the
idea
l
va
lue
of
H(
m
)
a
c
c
or
ding
to
(
11)
is
8,
thi
s
mea
n
that
r
a
ndom
in
f
or
mation
in
im
a
ge
[
21
]
.
T
he
va
lues
that
we
obtaine
d
o
f
in
f
or
mation
e
ntr
opy
a
r
e
c
los
e
r
to
e
ight
,
thi
s
pr
oo
f
that
the
pr
opos
e
d
s
c
he
me
ha
s
we
ll
r
a
ndom
.
T
a
ble
7
il
lus
tr
a
te
the
va
lues
of
inf
o
r
m
a
ti
on
e
ntr
opy
f
o
r
the
va
r
ious
plain
a
nd
e
nc
r
ypted
ir
i
s
im
a
ge
s
a
nd
c
ompar
e
d
it
wi
th
[
8
]
.
4.
5.
Re
s
is
t
in
g
d
i
f
f
e
r
e
n
t
ial
at
t
ac
k
an
alys
is
T
he
a
tt
a
c
ke
r
s
typ
ic
a
ll
y
make
a
s
mall
c
ha
nge
on
the
s
e
lec
ted
plain
im
a
ge
a
nd
then
note
the
c
ha
nge
s
in
the
e
nc
r
ypted
im
a
ge
.
T
hus
,
they
may
be
a
ble
to
f
ind
a
r
e
lations
hip
be
twe
e
n
the
plain
a
nd
e
nc
r
ypted
im
a
ge
[
22
]
.
I
n
or
de
r
to
know
the
e
f
f
e
c
t
of
c
h
a
nging
a
tee
ny
por
ti
on
of
pixels
in
the
nor
ma
l
im
a
ge
on
the
e
nc
r
ypted
im
a
ge
,
in
thi
s
pa
pe
r
we
us
e
d
the
number
of
pixels
c
ha
nge
r
a
te
(
NPC
R
)
a
nd
unif
ied
a
ve
r
a
ge
d
c
ha
nge
d
int
e
ns
it
y
(
UA
C
I
)
.
T
he
N
P
C
R
indi
c
a
tor
c
a
n
be
us
e
d
to
know
the
number
of
dif
f
e
r
e
nt
pixels
t
ha
t
ha
ve
the
s
a
me
loca
ti
on
in
the
or
igi
na
l
im
a
ge
a
nd
in
it
s
e
nc
r
ypted
im
a
ge
,
a
nd
it
is
de
f
ined
a
s
f
ol
lows
:
N
P
C
R
=
∑
D
(
i,
j)
i
,
j
w
x
h
x
1
0
0
%
(
12)
he
r
e
,
w
a
nd
h
a
r
e
the
width
a
nd
he
ight
o
f
the
im
a
g
e
,
C
1(
i
,
j
)
a
nd
C
2(
i,
j)
a
r
e
the
two
e
nc
r
yp
ted
im
a
ge
s
whos
e
c
or
r
e
s
ponding
plain
im
a
ge
s
I
1
(
i
,
j
)
a
nd
I
2
(
i,
j
)
ha
ve
on
ly
one
-
pixel
va
lue
dif
f
e
r
e
nc
e
.
D
(
i
,
j)
=
0
,
if
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
1
,
F
e
br
ua
r
y
2020
:
289
-
300
298
C1
(
i,
j)
=
C2
(
i
,
j
)
;
e
ls
e
D(
i
,
j
)
=
1.
T
he
UA
C
I
ind
ica
tor
is
us
e
d
to
know
the
e
f
f
e
c
t
on
e
nc
r
ypted
im
a
ge
if
one
pixel
is
c
ha
nge
d
in
plain
im
a
ge
,
a
nd
it
is
de
f
ined
a
s
f
oll
ows
:
U
A
CI
=
1
w
x
h
(
∑
|
C
1
(
i
,
j
)
−
C
2
(
i
,
j
)
|
255
i
,
j
)
x
100%
(
13)
the
idea
l
va
lue
of
NPC
R
a
nd
UA
C
I
a
r
e
99.
61
a
nd
3
3.
46
[
23
,
24
]
.
I
n
thi
s
pa
pe
r
we
im
pleme
nt
NPC
R
a
n
d
UA
C
I
mea
s
ur
e
s
on
f
ou
r
c
olor
ir
is
im
a
ge
s
a
nd
th
e
r
e
s
ult
s
of
the
two
ind
ica
tor
s
a
r
e
c
los
e
to
idea
l
va
lue.
T
a
ble
8
s
hown
the
r
e
s
ult
s
of
NPC
R
a
nd
UA
C
I
in
p
r
opos
e
d
s
c
he
me
a
nd
c
ompar
e
it
with
[
8
].
4.
6.
P
e
ak
s
ign
al
t
o
n
o
is
e
r
at
io
(
P
S
NR
)
P
S
NR
(
pe
a
k
s
ignal
to
nois
e
r
a
ti
o
)
a
r
e
mor
e
popular
tes
ts
f
or
im
a
ge
e
nc
r
ypti
on
a
lgo
r
it
hms
;
P
e
a
k
s
ignal
-
to
nois
e
r
a
ti
o
c
a
n
be
uti
li
z
e
d
to
e
va
lu
a
te
a
n
e
nc
ipher
ing
s
c
he
me.
I
t
is
a
mea
s
ur
e
ment
th
a
t
point
s
the
c
ha
nge
s
in
pixel
va
lues
be
tw
e
e
n
the
plain
im
a
g
e
a
nd
the
c
ipher
im
a
ge
.
T
he
lowe
r
va
l
ue
of
P
S
NR
r
e
pr
e
s
e
nts
be
tt
e
r
e
nc
ipher
ing
qua
li
ty
.
T
he
P
S
NR
f
or
mul
a
is
e
xpr
e
s
s
e
d
in
e
qua
ti
on
be
ll
ow:
P
S
N
R
=
10
∙
log
10
[
M
×
N
×
255
2
∑
∑
(
P
(
i
,
j
)
−
C
(
i
,
j
)
)
2
N
−
1
j
=
0
M
−
1
i
=
0
]
(
14)
whe
r
e
M
is
the
width
a
nd
N
is
the
he
ight
of
digi
tal
im
a
ge
.
P
(
I
,
j
)
is
pixel
va
lue
of
the
p
lain
im
a
ge
a
nd
C
(
I
,
j)
is
pixel
va
lue
o
f
the
c
ipher
im
a
ge
[
2
5
]
.
T
a
ble
8
s
hown
the
r
e
s
ult
s
of
NPC
R
a
nd
UA
C
I
in
pr
opos
e
d
s
c
he
me
a
nd
c
ompar
e
it
with
[
8
].
4.
7.
E
n
c
r
yp
t
ion
an
d
d
e
c
r
yp
t
ion
t
im
e
an
alys
is
T
he
e
xe
c
uti
on
ti
me
of
im
a
ge
e
nc
r
ypt
ion
a
nd
de
c
r
ypti
on
in
pr
opos
e
d
s
ys
tem
a
nd
the
c
ompar
is
on
with
[
8]
a
r
e
e
xplains
in
T
a
ble
9
.
T
a
ble
7.
C
ompar
ing
I
nf
or
mation
E
nt
r
opy
o
f
plain
a
nd
e
nc
r
ypted
ir
is
i
mage
be
twe
e
n
pr
opos
e
d
method
a
nd
[
8
]
I
ma
ge
s
E
nt
r
opy of
pl
a
in
i
ma
ge
s
E
nt
r
opy of
p
r
opos
e
d s
ys
te
m
E
nt
r
op
y of
A
. H
us
a
in
[
8]
7
.2
3
4
2
7
.9
9
8
0
7
.9
9
7
4
7
.1
2
8
8
7
.9
9
8
9
7
.9
8
4
1
7
.3
2
0
4
7
.9
9
9
0
7
.9
9
7
1
7
.1
7
7
2
7
.9
9
9
1
7
.9
9
7
4
Evaluation Warning : The document was created with Spire.PDF for Python.