# An Efficient Modified Advanced Encryption Standard (MAES) Adapted for Image Cryptosystems

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**ABSTRACT:**This paper presents a new method for image encryption by integrated pixel scrambling plus diffusion technique [IISPD]. The algorithm makes use of full chaotic property of logistic map and reduces time complexity. The algorithm calculates the permuting address for row by bit xor’ing the adjacent pixel values of original image. Similarly, the algorithm calculates the permuting address for column by bit xor’ing the adjacent pixel values of original image. Therefore, the new technique does not require the knowledge of probability density function of the chaotic orbits a priory, thus it reduces the complexity of the proposed technique. The diffusion is performed after scrambling and is based on two chaotic maps. Therefore, the key space and security of the algorithm is increased. The security analysis and its experimental analysis show that the proposed technique is highly sensitive to initial conditions. It also has higher key space, and higher degree of scrambling.Procedia CS. 01/2011; 3:378-387. - [Show abstract] [Hide abstract]

**ABSTRACT:**Recently, compressive sensing-based encryption methods which combine sampling, compression and encryption together have been proposed. However, since the quantized measurement data obtained from linear dimension reduction projection directly serve as the encrypted image, the existing compressive sensing-based encryption methods fail to resist against the chosen-plaintext attack. To enhance the security, a block cipher structure consisting of scrambling, mixing, S-box and chaotic lattice XOR is designed to further encrypt the quantized measurement data. In particular, the proposed method works efficiently in the parallel computing environment. Moreover, a communication unit exchanges data among the multiple processors without collision. This collision-free property is equivalent to optimal diffusion. The experimental results demonstrate that the proposed encryption method not only achieves the remarkable confusion, diffusion and sensitivity but also outperforms the existing parallel image encryption methods with respect to the compressibility and the encryption speed.Multimedia Tools and Applications 09/2014; · 1.01 Impact Factor

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226

Manuscript received February 5, 2010

Manuscript revised February 20, 2010

An Efficient Modified Advanced Encryption Standard (MAES)

Adapted for Image Cryptosystems Adapted for Image Cryptosystems

An Efficient Modified Advanced Encryption Standard (MAES)

Abdulkarim Amer Shtewi†, Bahaa Eldin M. Hasan, Abd El Fatah .A. Hegazy

†Arab Academy for science &Technology College of Computing and Information Technology, Cairo branch, Egypt.

†† Arab Security Consultants (ASC)

††† Arab Academy for science &Technology College of Computing and Information Technology, Cairo branch, Egypt.

Abstract

Security in transmission storage of digital images has its

importance in today's image communications and confidential

video conferencing. Advanced Encryption Standard (AES) is a

well known block cipher that has several advantages in data

encryption. However, it is not suitable for real-time applications.

In this paper, we present a modification to the Advanced

Encryption Standard (MAES) to reflect a high level security and

better image encryption. The modification is done by adjusting

the ShiftRow phase. Experimental results verify and prove that

the proposed modification to image cryptosystem is highly

secure from the cryptographic viewpoint. The results also prove

that with a comparison to original AES encryption algorithm the

modified algorithm (MAES) gives better encryption results in

terms of security against statistical attacks.

Keywords:

AES, MAES, image encryption, security analysis

1. Introduction

With the continuing development of both computer and

Internet technology, multimedia data (images, videos,

audios, etc.) is being used more and more widely, in

applications such as video-on-demand, video conferencing,

broadcasting, etc. Now, multimedia data is closely related

to many aspects of daily life, including education,

commerce, and politics. Until now, various data

encryption algorithms have been proposed and widely

used, such as AES, RSA, or IDEA [1, 2], most of which

are used in text or binary data. It is difficult to use them

directly in multimedia data, for multimedia data [3] are

often of high redundancy, of large volumes and require

real-time interactions, such as displaying, cutting, copying,

bit rate conversion, etc. For example, the image shown in

Figure 1(a) is encrypted into that shown in Figure 1(b) by

AES algorithm directly (ECB mode). As can be seen,

Figure 1(b) is still intelligible to some extent.

This is because the adjacent pixels in an image are of close

relation which cannot be removed by AES algorithm.

Besides the security issue, encrypting images or videos

with these ciphers directly is time consuming and not

suitable for real-time applications. Therefore, for

multimedia data, some new encryption algorithms need to

be studied.

a)

Fig. 1 Application of the AES cipher to Mickey plain image/cipher image.

This paper proposes a new encryption scheme as a

modification of AES algorithm. The modification is

mainly focused on both ShiftRow phase. In the ShiftRow

phase, if the value in the first row and first column is even,

the first and fourth rows are unchanged and each bytes in

the second and third rows of the state are cyclically shifted

right over different number, else the first and third rows

are unchanged and each byte of the second and fourth

rows of the state are cyclically shifted left over different

number of bytes. This modification allows for greater

security and increased performance.

b)

2. AES Algorithm

2.1 Tables and Figures

The Advanced Encryption Standard (AES) algorithm is

capable of using cryptographic keys of 128, 192, and 256

bits to encrypt and decrypt data in blocks of 128 bits [4].

This standard is based on the Rijndael algorithm [5], a

symmetric block cipher. As the AES algorithm may be

used with three different key lengths, these three different

‘‘flavors’’ are generally referred to as ‘‘AES-128’’,

‘‘AES-192’’, and ‘‘AES-256’’.

The AES algorithm is divided into four different phases, which

are executed in a sequential way forming rounds. The encryption

is achieved by passing the plaintext through an initial round, 9

equal rounds and a final round. In all of the phases of each round,

the algorithm operates on a 4x4 array of bytes (called the State).

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In Fig. 2 we can see the structure of this algorithm. Let us see

every phase of the algorithm.

2.1 KeyExpansion phase

The AES algorithm takes the Master Key K, and performs

a Key Expansion routine to generate a key schedule. The

Key Expansion generates a total of 11 sub-key arrays of

16 words of 8 bits, denoted wi, taking into account that the

first sub-key is the initial key. To calculate every wi

(except w0) the routine uses the previous

tables, RCon and S-Box.

RCon[i] contains the

[

,{00},{00},{00}], with

denoted as{02}) in the field GF

Box is a non-linear and invertible substitution table used

to perform a one-by-one substitution of a byte value.

and two

values

being powers of x (x is

. On the other hand, S-

given by

2.2 AddRoundKey phase

The AddRoundKey phase performs an operation on the

State with one of the sub-keys. The operation is a simple

XOR between each byte of the State and each byte of the

sub-key.

2.3 SubByte phase

The SubByte transformation is a non-linear byte

substitution that operates independently on each byte of

the State using the SBox table.

2.4 ShiftRow phase

In the ShiftRow transformation, the bytes in the last three

rows of the State are cyclically shifted over 1, 2 and 3

bytes, respectively. The first row is not shifted.

2.5 MixColumns phase

The MixColumns transformation operates on the State

column by column, treating each column as a four-term

polynomial. The columns are considered as polynomials

over GF

and multi- plied by a fixed polynomial a(x)

modulo

given by

This can be written as a matrix multiplication as follows:

As a result of this multiplication, the four bytes in a

column are replaced as follows:

Where

modulo

is the XOR operation and the. is a multiplication

the irreducible polynomial

shows .Fig.2 the

implementation of the function

which will be used to make the multiplications of a

number by 2 modulo m(x).

So, we will only have binary operations as follows:

See [6] for a complete mathematical explanation of the

AES algorithm.

Fig. 2 Description of the AES cryptographic algorithm

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3. A Modified AES (MAES)

Here, we modify the AES to be more efficient and secure

way by adjusting the ShiftRow phase.

3.1 ShiftRow Phase:

Instead of the original Shiftrow, we modify it as:

a- Examine the value in the first row and first

column,(state [0][0]) is even or odd?

b- If it is odd, The ShiftRows step operates on the

rows of the state; it cyclically shifts the bytes in

each row by a certain offset. For MAES, the first

and third rows are unchanged and each byte of

the second row is shifted one to the left.

Similarly, the fourth row is shifted by three to

the left respectively.

c- If it is even, The ShiftRows step operates on the

rows of the state; it cyclically shifts the bytes in

each row by a certain offset. The first and fourth

rows are unchanged and each byte of the second

row is shifted three to the right. Similarly, the

third row is shifted by tow respectively on to the

right.

The pseudo code for ShiftRows is as follows.

ShiftRows ( byte state [4, Nb] )

begin byte t[Nb]

if state[0][0] odd numbers

for r = 1 step 1, 3

x = r mod 4

if x = 0 step 0 to x + 1

for c = 0 step 1 to Nb – 1

t[c] = state[r, (c + x) mod Nb]

end for

for c = 0 step 1 to Nb – 1

state[r,c] = t[c]

end for

end for

else

for r = 2 step 2, 4

k = 0

x = r mod 4

if x = 0 step 0 to 3

for c = Nb - 1, c >= 0 , c -1

t[c] = state[x, (c + x) mod Nb , k + 1

end for

for c = 0 , c < Nb , c

state[x,c] = t[c]

end for

end for

end

1 +

4. Experimental results

Results of some experiments are given to prove its

efficiency of application to digital images. We use several

images as the original images (plain images). The

encrypted images are depicted in Figs. 3b-5b. As shown,

the encrypted images (cipher image) regions are totally

invisible. The decrypted images are shown in Figs. 3c-5c.

The visual inspection of Figs.3-5 shows the possibility of

applying the proposed MAES successfully in both

encryption and decryption.

effectiveness in hiding the information contained in them.

Also, it reveals its

a) Original image b) Encrypted image c) Decrypted image

Fig. 3 Application of the modified cipher to Lena plain image/cipher

image

a) Original image b) Encrypted image c) Decrypted image

Fig. 4 Application of the modified cipher to Mickey plain image/cipher

image

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a) Original image b) Encrypted image c) Decrypted image

Fig. 5 Application of the modified cipher to Baboon plain image/cipher

image

5. Security analysis

The security of an image cryptosystem is determined by its

confusion and diffusion capabilities. It is usually evaluated

by the following quantitative measures [7-15].

5.1 Key space analysis

Key space size is the total number of different keys that

can be used in the encryption. For a secure image

encryption, the key space should be large enough to make

brute force attacks infeasible [20]. The proposed cipher

has 2128 different combinations of the secret key. An

image cipher with such a long key space is sufficient for

reliable practical use.

5.2 Statistical analysis

It is well known that many ciphers have been successfully

analyzed with the help of statistical analysis and several

statistical attacks have been devised on them. Therefore,

an ideal cipher should be robust against any statistical

attack. To prove the robustness of the proposed cipher, we

have performed statistical analysis by calculating the

histograms and the correlations of two adjacent pixels in

the plainimage/cipherimage.

5.2.1 Histograms analysis

To prevent the leakage of information to an opponent, it is

also advantageous if the cipherimage bears little or no

statistical similarity to the plainimage. An image

histogram illustrates how pixels in an image are

distributed by graphing the number of pixels at each color

intensity level. We have calculated and analyzed the

histograms of the several encrypted images as well as its

original images that have widely different content. One

typical example among them is shown in Fig.6b. The

histogram of a plainimage (Mickey image(Fig.6a) of size

256x256 pixels) contains large spikes. The histogram of

the cipherimage as shown in Fig.6d, is uniform,

significantly different from that of the original image, and

bears no statistical resemblance to the plain image. It is

clear that the histogram of the encrypted image is fairly

uniform and significantly different from the respective

histograms of the original image and hence does not

provide any clue to employ any statistical attack on the

proposed image encryption procedure.

-50050100150200250300

0

2

4

6

8

10

12

x 10

4

a) Original image b)Histogram of original image

-50050100150200250300

0

100

200

300

400

500

600

700

800

900

c) Encrypted image d) Histogram of Encrypted image

Fig. 6 Histograms of the plain image and ciphered image

5.2.2 Correlation of adjacent pixels

In addition to the histogram analysis, we have also

analyzed the correlation between two vertically adjacent

pixels, two horizontally adjacent pixels and two diagonally

adjacent pixels in plainimage/cipherimage respectively.

The procedure is as follows: First, randomly select 1000

pairs of two adjacent pixels from an image. Then,

calculate their correlation coefficient using the following

two formulas:

cov( , )

x y

(

x y

( ))(

E x

)

()

D y

( )),

E yE xy

=−−

(5)

cov( ,

(

D x

,

)

xy

r

=

(6)

where x and y are grey-scale values of two adjacent pixels

in the image. In numerical computations, the following

discrete formulas were used:

1

N

1

( )

N

i

i

E xx

=

= ∑

(7)

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230

2

1

N

1

( )( ( )) ,

E x

N

i

i

D xx

=

=−

∑

(8)

1

N

1

cov( , )

x y

(( ))(

E x

( )),

E y

N

ii

i

xy

=

=−−

∑

(9)

Fig. 7 shows the correlation distribution of two

horizontally adjacent pixels in plainimage cipherimage

(Micky image of size 256x256) for the modified cipher.

The correlation coefficients are 0.9452 and -0.0112

respectively for both plainimage cipherimage, which are

far apart. Similar results for diagonal and vertical

directions were obtained as shown in Table 1. It is clear

that from the Fig.7 and Table 1 that there is negligible

correlation between the two adjacent pixels in the

cipherimage. However, the two adjacent pixels in the

plaintext are highly correlated.

Table 1 : Correlation coefficient of two adjacent pixels in original and

encrypted image

Direction Plainimage

Horizontal 0,9452

Vertical 0,9471

Diagonal 0,9127

Cipherimage

-0,0112

-0,0813

0,0009

050100150200 250

0

50

100

150

200

250

Pixel gray value on location(x,y)

Pixel gray value on location(x+1,y)

Corr of original image (Horizontal)

050100

Pixel gray value on location(x,y)

150 200250300

0

50

100

150

200

250

300

Pixel gray value on location(x+1,y)

Corr of cipher image (Horizontal)

Fig. 7 Tow horizontally adjacent pixels Correlation in

plainimage/cipherimage, respectively

5.3 Information entropy analysis

Information theory is the mathematical theory of data

communication and storage founded in 1949 by C.E.

Shannon [16]. Modern information theory is concerned

with error- correction, data compression, cryptography,

communications systems, and related topics. To calculate

the entropy H (m) of a source m, we have:

N

H m P m

P m

=

Where p (mi) represents the probability of symbol mi and

the entropy is expressed in bits. Let us suppose that the

source emits 28 symbols with equal probability, i.e.,

{ ,,.....,}

mm mm

=

after evaluating Eq. (10), we

obtain its entropy H (m) = 8, corresponding to a truly

random source. Actually, given that a practical

information source seldom generates random messages, in

general its entropy value is smaller than the ideal one.

However, when the messages are encrypted, their entropy

should ideally be 8. If the output of such a cipher emits

symbols with entropy less than 8, there exists certain

degree of predictability, which threatens its security.

Let us consider the ciphertext of image encryption using

the proposed block cipher, the number of occurrence of

each ciphertext block is recorded and the probability of

occurrence is computed. We illustrate the entropy analysis

of our scheme kept at the same word size w=32, number

of rounds r= 10, and secret key length b=16 respectively,

and compare it with other schemes. Table 2 indicates the

various values of the entropies for encrypted images. It

can be noted that the entropy of the encrypted image of

MAES are very near to 8 compared to the other schemes.

21

2

0

1

()( )log

()

i

i

i

bits

−

=∑

(10)

8

12

2

Table 2: Entropies of the encrypted images of Mickey image

(mickey.bmp)

Encryption

algorithm

AES

MAES

Entropy

Value

7.9989

7.9992

5.4 Differential attack

Two common measures, NPCR and UACI [17-19], are

used to test the influence of changing a single pixel in the

original image on the whole image encrypted by the

proposed scheme. NPCR stands for the number of pixels

change rate while–pixel of plain image are changed.

Unified Average Changing Intensity (UACI) measures the

average intensity of difference between the plain image

and cipher image. For calculation of NPCR and UACI, let

us assume two ciphered images C1 and C2 whose

corresponding plain images have only one-pixel difference.

The gray-scale values of the pixels of the ciphered image

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C1 and C2 at grid (i,j) are labeled as C1(i,j) and C2(i,j),

respectively. Define a bipolar array, D, with the same size

as images C1 and C2. Then, D(i,j) is determined by C1(i,j)

and C2(i,j), namely, if C1(i,j) = C2(i,j) then D(i,j) = 1;

otherwise, D(i,j) = 0. NPCR and UACI are defined

through the following formulas:

( , )

100%,

NPCR

WH

×

( , )( , )1

255

WH

×

⎣

Where W and H are the width and height of C1 or C2.

Tests have been performed on the proposed algorithm,

about one-pixel change influence on a 256 gray-scale

image of size256 256

×

.We obtained NPCR=99.58% and

UACI= 29.63 %. The results show that a swiftly change in

the original image will result in a significant change in the

ciphered image, so the algorithm proposed has a good

ability to anti differential attack.

,

i jD i j

=×

∑

(11)

12

,

100%,

×

i j

C i j C i j

UACI

−

⎡

⎢

⎤

⎥

⎦

=

∑

(12)

5.5 Performance of MAES w/r/b Encryption

Apart from security considerations, some other issues for

image cryptosystem algorithm are also important. This

includes the running speed, particularly for real time

Internet multimedia application. Some experimental tests

are given to demonstrate the efficiency of our scheme. An

indexed image of a "Mickey" (see Fig. 4a) is used as a

plainimage and encryption of this image is shown in Fig.

4b. the personal computer used in all programs and test

was Intel(R) Core™ 2Duo CPU T5800 2.00GHz with

3.00GB of memory and 230GB hard-disk capacity. Table

3 and Fig.8 shows Performance of AES and MAES w/r/b

Encryption on 256 grey-scale image of different sizes,

kept at the same word size w=32, number of round r=12

and secret key length b=16 and kept at CBC mode of

operation.

Table 3 : Performance of AES and MAES w/r/b Encryption

Image size (in

pixels)

Image size

on disk

Encryption

time in ms

with AES

Encryption

time in ms

with MAES

256x256 192KB 6.443 6.349

512x512 257KB 8.643 8.565

512x512 768KB 25.256 25.007

1024x1024 2.25MB 75.862 75.114

Fig. 8 Performances of AES and MAES

6. Conclusion

In this paper a modified version of AES, namely MAES, is

proposed. The modification is done by adjusting ShiftRow

phase. The proposed cryptosystem does not require any

additional operations rather than the original AES. We

have shown that MAES gives better encryption results in

terms of security against statistical attacks.

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Abdulkarim Amer Shtewi received

the Bachelor's degree in Engineering

Sea Officer with the degree Excellent

from Stralsund College graduated

Engineer in Germany in 1985.

Bachelor's degree in System Analysis

with the degree very good from Dar

Alalem University, College computer

Science. in Libya in 2006. Now a

Master student at college of

computing and information technology at the Arab Academy for

science &Technology in Egypt.

Bahaa Eldin M. Hasan received the

B.Sc. and M.Sc. degrees, from faculty

of Engineering (Shoubra), Zagazig

University

respectively. He received the Dr. Eng.

degree from Ain Shams University

under supervision of Tokyo institute

of Technology in 1994. Bahaa has

served the National Defense Council

Service for 26 years. During his 26

years, he was engaged in general

National Defense Council duties. He

awarded the Order of Merit- Second grade from the president of

Egypt. Bahaa left the National Defense Council in 2006 and went

to work for his own privet business “Arab Security Consultants

(ASC)”.Bahaa is an expert specializing in such areas as: Data

security, network security, computer security, Ethical hacking

and countermeasures, and Smart card and smart token

applications for securing the data and information. Bahaa is still

involved in the training of security officers as well as for security

staff for several Arab world organizations.

in 1978 and 1987,

Abdelfatah A. Hegazy received the

B.E. degrees, from the Military

Technical Collage, Cairo, Egypt,

1978. In 1982 he received the M.Sc.

In Computer Sciences from George

Washington University, USA. Dr.

Hegazy received the Ph.D. Degree

Computer Sciences from George

Washington University, USA, in

1985. After working as an assistant

professor (from 1985) in the Dept. of

computer enginering operation

research, the Military Technical Collage., and an associate

professor (from 1990), he has been a professor at College of

Engineering at the Arab Academy for Science and technology.

Since 1998. His research interest includes: Information Systems

Planning; E-Commerce, E-Government, Information Systems

Security, network security ,knowledge Management, Web

Intelligent Systems and Enterprise Resource Planning Systems.

He is a member of IEEE, ACM, AIS, AANIS, and CSS-

Computer Scientific Society Egypt.