A new secure and sensitive image encryption scheme basedon new substitution with chaotic function

Zahra Parvin & Hadi Seyedarabi & Mousa Shamsi

Received: 21 January 2014 /Revised: 16 May 2014 /Accepted: 16 May 2014# Springer Science+Business Media New York 2014

Abstract In this paper, a new image encryption scheme is proposed with high sensitivityto the plain image. In proposed scheme, two chaotic functions and logical operator xor areused. Image encryption process includes substitution of pixels and permutation. Using thenew method of substitution, algorithm sensitivity somewhat has elevated to changes in theplain image that by changing a single pixel of the plain image, amount of NPCR reaches100 %. Results of tests show that the cipher image does not give any information ofstatistical such as entropy, histogram and correlation of adjacent pixels to attackers. Alsothe proposed scheme has the wide key space and is so safe to the noise ratio andcompression.

Keywords Security . Image encryption . Chaotic function . Substitution . Sensitivity . NPCR

1 Introduction

With the rapid growth of technology and the development of science computers, images,videos and sounds are the styles of representation of information. These are the majortransmission data in the networks and internet. Therefore, the abuse to private and importantinformation has become an important issue during transmission and storing of digital data incommunication. Encryption, watermarking and steganography are the ways to protect data.

Multimed Tools ApplDOI 10.1007/s11042-014-2115-y

Z. Parvin (*)Department of Communication Engineering, East Azarbaijan Science and Research Branch, Islamic AzadUniversity, Tabriz, Irane-mail: z.p.parvin@gmail.com

H. SeyedarabiFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Irane-mail: seyedarabi@tabrizu.ac.ir

M. ShamsiElectrical Engineering Faculty, Sahand University of Technology, Tabriz, Irane-mail: shamsi@sut.ac.ir

Encryption has become one of the important methods and it is a suitable and efficient way toresist attacks and unauthorized accesses. Traditional encryption methods such as DES,1

IDEA2 and RSA3 are suitable for text. But they are not good methods for encryption ofimages and videos because of inherent features of images, like high volume and highcorrelation of adjacent pixels [1, 2, 9, 15].

An efficient image encryption scheme should have some conditions such as the randomnesscipher image, a wide key space, a high sensitivity to the initial conditions (keys and plainimage) and estimating the combined good speed. So because of chaotic features, methodsbased on chaotic functions are as fundamental structuring of encryption systems. Thesemethods are easily applicable in personal computers and microprocessors and have high speedand low cost. These properties make use them instead of traditional methods [3–5, 13, 14].

Two or more dimensional chaotic functions are more suitable than one dimensional becauseof more compression of encryption process [6]. In many schemes logical operators are used aswell as chaotic functions ; [11] By changing pixels value and replacing them, produces a newimage. If the image pixels rotated, correlation of adjacent pixels would reduce. But histogramof image and entropy of remain unchanged [12].

In [12] two chaotic functions are presented. Using combination of them, an imageencryption scheme is presented by high speed. In the encryption scheme, pixels values arechanged and then pixels are rotated. This scheme is able to deal all kinds of attacks.

In [17] an encryption method based on improved hyper chaotic sequences is designed andpresented. The method has been used for image encryption, in addition to use the key and thepixels of plain image; the previous encrypted pixels also are used to produce the cipher pixels.This scheme is one of the sensitive methods to key changes and plain image; because of highsecurity it has many applications in secure communication issues.

In proposed encryption method, the chaotic functions in [12] are used and a new method isintroduced for substitution of pixels. In new idea, to produce the first pixel of cipher matrix, allpixels of the plain image are used except the first pixel. To do this, the sensitivity of algorithm tochange the plain image increases so that with very little change (1 bit) in per pixel of the plainimage, the change of cipher image is 100 %. In other words the value of NPCR is 100 %.

2 Chaotic functions and the generation of seeds

Two one-dimensional chaotic functions (1) and (2) and combination of them (3–4) are used inthis encryption scheme. Inputs and outputs of functions are located in range [−1,1]. Averageand cross correlation values of functions are 0 and these functions have δ-like auto correlationand white-noise statistical character and good pseudo-random. Proof of these properties andmore details of functions are in [12]. Also combination of them has more sensitivity to initialvalues and more complication degree than famous logistic chaotic map [12].

In this encryption scheme, 3 pseudorandom number matrices (seeds) are generated and thenare used. TwomatricesK1 andK2 are used for permutation andmatrixK3 is used for substitution.

Initial value of f1(xi) is located in range [−1,0] (In this algorithm x1 =−0.3) and the nextinputs are equal to the previous outputs (xi=f1(xi−1)). Number of outputs is equal to number ofrows of the plain image matrix (1). Generated f1(xi) in all of steps are as outputs and they aresorted in which they are produced. Finally they are mapped to the interval [0–255]. These

1 Data Encryption Standard2 International Data Encryption Algorithm3 Rivest Shamir Adelman

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numbers are scalars and they are used as numbers of matrix K1. m is number of rows of plainimage and size of matrix K1 is (m×1).

f 1 xið Þ ¼ 8xi4 − 8xi

2 þ 1x1 ¼ −0:3

i ¼ 2; 3;…;m xi ¼ f 1 xi−1ð Þð1Þ

Initial value of f2(xi) is located in [0,1] (In this algorithm x1 = 0.6) and the next inputs areequal to the previous outputs (xi= f2(xi−1)). Number of outputs is equal to number ofcolumns of the plain image matrix (2). Generated f2(xi) are sorted in which they areproduced and these outputs are then mapped into [0,255]. These numbers are scalars andthey used as numbers of matrix K2. n is number of columns of plain image and size ofmatrix K2 is (1×n).

f2 xið Þ ¼ 4xi3 − 3xi

x1 ¼ 0:6i ¼ 2; 3;…; n xi ¼ f 2 xi−1ð Þ

ð2Þ

Now combination of the two functions is used. In the beginning, two inputs located in[−1,1] are given. (In this algorithm, x1 =−0.2 and x2 = 0.7). Average of x1 and x2 is named a(3). One of two functions is selected based on a and f1 or f2 is calculated. New x1 or new x2 isdefined based on produced f1 or f2 (4). Again the new average (a) is calculated and thealgorithm is repeated until the numbers of outputs are equal to size of plain image (m×n).Generated f1 or f2 in all of steps are as outputs and they are sorted in which they are generated.The outputs are mapped to a level of 256 parts. These numbers are used as numbers of matrixK3. So size of matrix K3 is (m×n).

a ¼ x1 þ x22

ð3Þ

if a < 0; f 1 ¼ 8x14 − 8x1

2 þ 1x1 ¼ f 1

if a > 0; f 2 ¼ 4x23 − 3x2

x2 ¼ f 2

8>><>>:

ð4Þ

Numbers of repeated (3) and (4) = m×n

3 Encryption

In proposed algorithm, two stages permutation and one stage substitution are used forencryption of the plain image.

3.1 Permutation with pseudorandom numbers

This encryption scheme focuses in the new method of substitution. The new method ofsubstitution causes that results show the most reported NPCR (NPCR=%100). In this schemeis used permutation for more complexity and for raising the key space. Also should bepermuted to resist chosen plain text attack. So each row of plain image is rotated in horizontaldirection by a random number. After rotation of all of rows, the resulted matrix is rotated invertical directions.

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3.1.1 Circular shift by row

In circular shift by row, each row of plain image is shifted circularly using correspondingnumber in matrix K1. In other words the first row of plain image is shifted circularly using firstnumber of matrix K1 and the same action as first row is done in each row. So the rotationnumbers for different rows are different.

3.1.2 Circular shift by column

After horizontal rotation for all rows of plain image, the resulted matrix is then shiftedcircularly in the vertical direction. For vertical rotation, every column is shifted circularly withcorresponding number of matrix K2.

3.2 Substitution with pseudorandom numbers and operator logical xor

In the beginning, result matrix of previous stage is converted into one-dimensional array (5) andK3 is converted too (6). All of the matrix M elements except m1 are used to generate the firstelement of the cipher matrix (7–8).mod (s,256) is the remaining of divide of s by 256. Equation(9) is used to generate other cipher pixels. All of ci are used as numbers of cipher matrix and forshowing the cipher matrix, it is converted into two-dimensional array and is named CC (10). mis number of rows of plain image and n is number of columns of plain image.

M ¼ m1;m2;…;mm�n½ � ð5Þ

K ¼ k1; k2;…; km�n½ � ð6Þ

s ¼X

i¼2

m�nmi ð7Þ

c1 ¼ bitxorðm1; bixor mod s; 256ð Þ; k1ð Þ ð8Þ

ci ¼ bitxor mi; bitxor mod ci−1 þ ki; 256ð Þ; kið Þð Þi ¼ 2; 3;…;m� n

ð9Þ

CC ¼ cci; j���i ¼ 1; 2;…;m and j ¼ 1; 2;…; n

n oð10Þ

4 Decryption

To extract the original image and decrypting the cipher image, reverse the encryption processis used with little change on the cipher image. Namely cipher matrix is converted into one-dimensional array (11). Equation (12) is used to generate all decrypted pixels excluding first

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pixel. Then all of these decrypted pixels are added together (13). The first decrypted pixel iscalculated with (14). Matrix D contains decrypted pixels but to extract the plain image, itshould be converted to two dimensional array and circular shift along vertical and horizontalshould be applied in reverse order to the encryption mode.

C 0 ¼ c 01; c

02;…; c 0

m�n

� � ð11Þdm�n−i ¼ bitxor c 0

m�n−i; bitxor mod c 0m�n−i−1 þ km�n−i; 256

� �; km�n−i

� �� �i ¼ 0; 1;…;m� n − 2

ð12Þ

s ¼X

i¼2

m�ndi ð13Þ

d1 ¼ bitxorðc1; bixor mod s; 256ð Þ; k1ð Þ ð14Þ

5 Experimental results and security analysis

In this section security of the proposed algorithm is checked. 7 standard images are selected fortests. Size of them is 256×256. Figure 1 shows that the proposed algorithm can correctlyencrypt an image and decrypt it. The decrypted image exactly equal to plain image and theyhave the same histograms.

5.1 Statistical analysis

No statistical similarity exists between plain image and cipher image to prevent any attacks[1–17].

(a) (b) (c)

(d) (e) (f)

Fig. 1 a plain image, b cipher image with proposed algorithm, c decrypted image and their histograms

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5.1.1 Histogram analysis

Histogram analysis shows the distribution of pixels value in the image. To prevent the attackerfrom obtaining any useful statistical information, the histogram of the cipher image should beuniform. Figure 1a is histogram of plain image Lena and Fig. 1b is histogram of encryptedLena. Other standard images encrypted using the proposed algorithm and results have shownin Fig. 2. All of the cipher image histograms are almost uniform and show that this algorithm issecure enough for image encryption.

(a) (b) (c) (d)

Fig. 2 a plain images, b histograms of plain images, c cipher images, d histograms of cipher images

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5.1.2 Information entropy analysis

Information entropy defines uncertainty and degree of ambiguity in the image. Entro-py can measure the distribution of pixels gray values in the image. A good cipherimage has entropy very close to 8. Entropy of image is calculated with (15). In (15) sis the amount of gray level in each image and p(s) is probability of s. Table 1 isreported entropy values of 7 standard images and cipher images using the proposedalgorithm.

H sð Þ ¼ −X

i¼0

2N−1p sð Þlog2 sð Þ ð15Þ

5.1.3 Correlation coefficient analysis

High correlation exists between adjacent pixels in every normal image. A secure encryptionalgorithm should produce cipher images that correlation of adjacent pixels is very low. Usually1,000 or 2,000 pixels are selected for correlation analysis and correlation is calculated inhorizontal, vertical and diagonal directions (16–19). x and y are gray values of adjacent pixelsin every image. r is correlation of pixels. Correlation of pixels along horizontal, vertical anddiagonal direction is reported in Table 2. Figure 3 shows correlation coefficient analysis inhorizontal, vertical and diagonal directions for plain and cipher images of Lena. Results showthat strong correlation exists between adjacent pixels in plain image wich is decresed afterencryption.

E xð Þ ¼ 1

N

Xi¼1

Nx2i ð16Þ

D xð Þ ¼ 1

N

Xi¼1

Nx2i − E xð Þ� �2 ð17Þ

cov x; yð Þ ¼ 1

N

Xi¼1

Nxi − E xð Þð Þ yi − E yð Þð Þ ð18Þ

rxy ¼ cov x; yð ÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiD xð ÞD yð Þp ð19Þ

Table 1 Results of information entropy of images

Image Lena cameraman peppers baboon boat airplane black

Entropy of plain image 7.5925 6.9719 7.5327 7.4125 7.1701 6.7942 0

Entropy of cipher image 7.9969 7.9969 7.9974 7.9975 7.9973 7.9970 6.9980

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5.2 Spectral characteristic analysis

In this section, spectral characteristic of cipher images are compared with spectral charactristicof plain images. Spectral analysis is visual analysis and it is done with fourier transform. Dueto the large numbers, logaritm transform is used for representation of results. Figure 4 showsresults for plain images of Lena. This test is done for other images and results in Fig. 5 showthat spectral charactristic of different images are same after encyption.

Table 2 Correlation coefficient of pair adjacent pixels

Horizontal Vertical Diagonal

Lena Plain image 0.9476 0.9479 0.8716

Cipher image −0.0018 0.0345 0.0202

Cameraman Plain image 0.9801 0.8773 0.8885

Cipher image −0.0410 −0.0001 −0.0004Peppers Plain image 0.9928 0.9726 0.9166

Cipher image 0.0345 0.0174 0.0267

Baboon Plain image 0.7705 0.5319 0.6236

Cipher image 0.0164 0.0235 0.0432

Boat Plain image 0.9263 0.8941 0.9213

Cipher image −0.0020 −0.0134 0.1398

Airplane Plain image 0.8880 0.9390 0.7159

Cipher image −0.0168 −0.0265 0.0314

Black Plain image NAN NAN NAN

Cipher image −0.0526 −0.0076 −0.0282

(a) (b) (c)

(d) (e) (f)

Fig. 3 Correlation coefficient analysis in plain image ‘Lena’: a in horizontal direction, b in vertical direction, cin diagonal direction Correlation coefficient analysis in cipher image: d in horizontal direction, e in verticaldirection, f in diagonal direction

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5.3 Sensitivity analysis

A secure encryption algorithm is too sensitive to the key and the plain image so that acompletely different cipher image is made with a small change in the key or the plain image[1–6, 8, 9, 11–15, 17].

5.3.1 Differential analysis

Differential analysis is used for assess the algorithm sensitivity to the plain image. Forthis test, usually one pixel in the plain image is changed (one bit increment or decrement)and then encrypted. Result of this encryption is compared by the first cipher imagebefore changing. A secure encryption algorithm with one minor change in the plainimage (one pixel) causes significant changes in the cipher image so can resist chosenplain text attack, known plain text attack and more advanced adaptive chosen plain textattack.

There are two scales for measuring, NPCR4 and UACI.5 NPCR is the number ofpixels change rate between two cipher images after changing of one pixel in the plainimage (20–21). W and H are width and length of the cipher images. ci is the pixel of

4 Number of Pixels Change Rate5 Unified Average Changing Intensity

(a) (b)

(c) (d)

Fig. 4 Spectral characteristic analysis: for plain image ‘Lena’: a log transform, b moving a to the center Forcipher of Lena: c log transform, d moving c to the center

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first cipher image and ci' is the pixel of second cipher image. UACI is the difference

average intensity of gray level of two cipher images (22). Large amounts of NPCRand UACI show that the algorithm is very sensitive to the changing of plain image.

cameraman

peppers

baboon

boat

airplain

black

(a) (b) (c) (d)

Fig. 5 Spectral characteristic analysis for other plain images: a log transform of plain images, b moving imagesof columns a to the center c log transform of cipher images, d moving images of column c to the center

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NPCR ¼X

i; jD i; jð Þ

W � H� 100% ð20Þ

D i; jð Þ ¼ 0; c i; jð Þ ¼ c 0 i; jð Þ1; c i: jð Þ ≠ c 0 i; jð Þ

�ð21Þ

UACI ¼ 1

W � H

Xi; j

c i; jð Þ − c 0 i; jð Þ�� ��255

� 100%

ð22Þ

In this section, 1 bit is added to a random pixel of Lena and the amount of NPCRand UACL are measured. Also this test is applied for 7 standard images for pixel(51,51). Table 3 and 4 report the results. The results show that the proposed schemeis too sensitive to the changing of plain image. NPCR is 100 % which is highestreported NPCR.

5.3.2 Key sensitivity analysis

An efficient encryption scheme is highly sensitive to changes in the encryption anddecryption keys. Applying a minor change to the encryption key, the second encryptedimage should be quite different from the first encrypted image. Also if a vary smalldifference exists between the encryption and decryption keys, the cipher image cannotbe decrypted correctly. The results of difference between two cipher images when theplain image is encrypted with two encryption keys by 10−15 difference are reported in

Table 4 Results of NPCR andUACI for 1 bit change in pixel(51,51) of 6 images

image NPCR UACI

Lena 100 % 34.09 %

Cameraman 100 % 33.55 %

Peppers 100 % 33.45 %

Baboon 100 % 33.50 %

Boat 100 % 33.57 %

Airplane 100 % 33.48 %

Black 100 % 33.04 %

Table 3 Results of NPCR and UACI for 1 bit change in 1 pixel of plain image Lena

pixel (1,1) (20,92) (30,50) (68,250) (150,40) (170,30) (200,111) (240,250) (256,256)

NPCR 100 % 100 % 100 % 100 % 100 % 100 % 100 % 100 % 100 %

UACI 33.96 % 33.68 % 33.10 % 34.02 % 36.13 % 34.10 % 33.71 % 33.85 % 33.59 %

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Table 5. Figures. 6 and 7 are results of very little change in the encryption anddecryption keys.

5.4 The key space

The key space of algorithm includes all of the sensitivity of different keys that are used in eachscheme. A good encryption scheme has the expanded key space. For a good level of securitythe key space should be more than 2100 [8, 11, 12, 17]. In this algorithm, 4 keys are used andthe sensitivity of each key is 10−15 . Four different states are to produce different scheme so thekey space of this algorithm is 4×1060 and it is much larger than 2100.

5.5 Noise

This section deals with issues such as encrypted image sensitivity to compression, data lossand being smeared noise. The results of these tests show that how much data can reconstructafter decryption. There are two scales for measuring, MSE (Mean Square Error) and PSNR(Peak Signal to Noise Ratio) (23_24). P is matrix of the plain image and D is the matrix of thedecrypted image for cipher that was destroyed. The higher PSNR represents the highersimilarity between the decrypted image and the plain image [7, 8, 16].

MSE ¼ 1

m� n

Xi¼1

m Xj¼1

np i; jð Þ − d i; jð Þ

� �2ð23Þ

PSNR ¼ 10� log10I2maxMSE

ð24Þ

Table 5 Changing the key encryption as (10−15) decrease

key −0.3 0.6 −0.2 0.7

NPCR 99.59 % 88.72 % 99.15 % 99.14 %

UACI 33.50 % 29.74 % 33.42 % 33.51 %

(a) (b)

Fig. 6 a encrypted image with encryption key −0.3000000000000001, b the difference between Figs. 1b and 6a

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5.5.1 The JPEG compression for cipher images

If the cipher image as a JPEG, saved and sent then decrypted, the resulting image is not verydifferent from the plain image. Table 6 shows that PSNR>34 so the result of decryption forcompressed cipher image is very good. Figure 8 shows the JPEG cipher image, decryptedimage and their histograms.

5.5.2 Noise attacks

To test the security of the method against noise, Gaussian and pepper & salt noises are added tothe encrypted image zero mean. Gaussian noise with 0.01variance and 0.1variance are added.Also pepper and salt by density 0.05 and density 0.1 are added. The results are showed inFig. 9 and Table 7. Results of decryption with peppers & salt noise are better than Gaussian

(a) (b)

Fig. 7 a decrypted image with decryption key −0.3000000000000001, b the difference between Fig. 7 a and theplain image

Table 6 Compression of cipherimage and result of decryption of it MSE PSNR

JPEG 2110.0364 34.2807

(a) (b) (c) (d)

Fig. 8 a the cipher image as a JPEG file, b histogram of a, c decrypted image for a, d histogram of c

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noise because PSNR is more than 35.75 and histogram of decrypted image is very similar tothe histogram of plain image.

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (l) (m)

(n) (o) (p) (q)

Fig. 9 Results of noise attacks: a Zero mean Gaussian noise with 0.01 variance added to cipher image, bhistogram of a, c decrypted image of a, d histogram of c, e Zero mean Gaussian noise with 0.1 variance added tocipher image, f histogram of e, g decrypted image of e, h histogram of g, i Pepper and salt noise with density 0.05added to cipher image, j histogram of i, l decrypted image of i, m histogram of l, n Pepper and salt noise withdensity 0.1 added to cipher image, o histogram of n, p decrypted image of n, q histogram of p

Table 7 Results of noise attacks

noise MSE PSNR

Gaussian noise with0.01 variance 4410.09994 26.9087

Gaussian noise with0.1 variance 5631.4354 24.4641

Salt & peppers noise by density 0.05 869.8890 43.1416

Salt & peppers noise by density 0.1 1829.6416 35.7065

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5.5.3 Data loss analysis

For this test, a portion of the encrypted image has been replaced with a zero box and the resultsin Fig. 10 and Table 8 are given. The results show that despite the loss of a large amount ofcipher images, the images are repaired and reconstructed very well.

(a) (b) (c) (d)

Fig. 10 a cipher images after data loss, b histograms of first column images, c decrypted images for first columnimages, d histogram of third column images

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6 Comparision

1) The proposed scheme comparing to other schemes has more sensitivity to the encryptionkeys and to the plain image (Table 9).

& In paper [12] key sensitivity of each key is 10−14 in the event that the key sensitivityof the proposed scheme for each key is 10−15.

& The key space compared to [12] is more expended. In [12] as the proposed scheme1stage substitution and 2 stages permutation are used but the key space is 4×1028, butthe key space of proposed scheme is 4×1060.

& The proposed image encryption scheme reaches NPCR=100 % which is the highestreported NPCR.

2) The proposed scheme can reconstruct the cipher images that destroyed by noise or looseddata. Also it can reconstruct the compressed cipher images.

7 Conclusions

The proposed image encryption scheme is based on chaotic function and logical operator xor.The scheme creates pseudorandom cipher images. Histograms of cipher images are completelyuniform and represent uncertainly of these images. The high correlation of adjacent pixels inthe normal images after encryption is very low and close to zero. The proposed algorithm isvery sensitive to each key. The key sensitivity is 10−15 for each key. The key space is 4 ×1060.The proposed algorithm is too sensitive to the plain image such that with one bit change ineach pixel, NPCR reaches 100 % and UACI ≥34.02%. If the cipher image is compressed or bedegraded by noise or even some data of cipher image missed, the proposed algorithm canreconstruct the image so good. All of the results show that the proposed image encryptionscheme is very safe and efficient for secure communication.

Table 8 Results of date loss

Loss cipher data MSE PSNR

(10:40,20:50) & (100:130,170:200)=0 345.3406 52.3800

(100:220,110:230)=0 2894.6596 31.1190

(90:110,:)=0 1073.0810 41.0424

(100:240,:)=0 6813.5770 22.5585

(:,100:120)=0 946.0235 42.3026

(:,10:150)=0 6560.0227 22.9378

Table 9 Camparision of NPCR AND UACI by previous methods

papers Theproposedscheme

[9] [15] [1] [4] [13] [3] [11] [12] [17] [8]

NPCR 100 % 96 % 99.62 % 99.5 % 46 % 99.6 % 99.46 % 99.66 % 99.60 % 99.61 % 99.65 %

UACI 34.02 % 33 % 33.48 % 33.3 % 39 % 33.3 % 33.22 % 33.52 % 33.56 % 33.46 % 33.55 %

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Zahra Parvin received the B.S. degree in Electronic Engineering from Department of Electrical and ComputerEngineering, Islamic Azad University Central Tehran Branch, Iran in 2008 and the M.S. degree in Communi-cation Engineering from Department of Communication Engineering, East Azarbaijan Science and ResearchBranch, Islamic Azad University, Tabriz, Iran in 2013. Her research interest includes: Multimedia Encryption,Image Processing and Cryptography. Email: z.p.parvin@gmail.com

Multimed Tools Appl

Hadi Seyedarabi Received B.S. degree from University of Tabriz, Iran, in 1993, the M.S. degree from K.N.T.University of technology, Tehran, Iran in 1996 and Ph.D. degree from University of Tabriz, Iran, in 2006 all inElectrical Engineering. He is currently an associate professor of Faculty of Electrical and Computer Engineeringin University of Tabriz, Tabriz, Iran. His research interests are image processing, computer vision, Human-Computer Interaction, facial expression recognition and facial animation.

Mousa Shamsi was born in Tabriz, Iran, in 1972. He graduated from high school (major: Mathematics–Physics)in Tabriz, Iran, in 1990. He passed the university entrance examination for engineering studies and joined TabrizUniversity, Tabriz, Iran, in 1990. He received his B.Sc. degree in Electrical Engineering (major: Electronics) fromTabriz University, in 1995. In 1996, he joined the University of Tehran, Tehran, Iran. He received his M.Sc.degree in Electrical Engineering (major: Biomedical Engineering) from this university in 1999. From 1999 to2002, he taught as a lecturer at the Sahand University of Technology, Tabriz, Iran. In 2002, he entered theUniversity of Tehran as a Ph.D. candidate. From 2002 to 2008, he was a Ph.D. student at the University of Tehranin Bioelectrical Engineering. In 2006, he was granted with the Iranian government scholarship as a visitingresearcher at the Ryukyus University, Okinawa, Japan. From December 2006 to May 2008, he was a visitingresearcher at this University. He received his PhD degree in Electrical Engineering (major: Biomedical Engi-neering) from University of Tehran in December 2008. From December 2008 to April 2013, he was an assistantprofessor at Faculty of Electrical Engineering, Sahand University of technology, Tabriz, Iran. From April 2013,he is an associate professor at Faculty of Electrical Engineering, Sahand University of technology, Tabriz, Iran.His research interests include biomedical image and signal processing, genomic signal processing, patternrecognition and facial surgical planning.

Multimed Tools Appl