The Scientific World Journal

Volume 2014 (2014), Article ID 283639, 11 pages

http://dx.doi.org/10.1155/2014/283639

Research Article

Novel Image Encryption Scheme Based on Chebyshev Polynomial and Duffing Map

Borislav Stoyanov and Krasimir Kordov

Faculty of Mathematics and Informatics, Konstantin Preslavski University of Shumen, 9712 Shumen, Bulgaria

Received 10 December 2013; Accepted 4 March 2014; Published 26 March 2014

Academic Editors: T. Cao, M. K. Khan, and F. Yu

Copyright © 2014 Borislav Stoyanov and Krasimir Kordov. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

We present a novel image encryption algorithm using Chebyshev polynomial based on permutation and substitution and Duffing map based on substitution. Comprehensive security analysis has been performed on the designed scheme using key space analysis, visual testing, histogram analysis, information entropy calculation, correlation coefficient analysis, differential analysis, key sensitivity test, and speed test. The study demonstrates that the proposed image encryption algorithm shows advantages of more than key space and desirable level of security based on the good statistical results and theoretical arguments.

1. Introduction

In recent years, the dynamical chaotic systems have been commonly used for the design of cryptographic primitives featuring chaotic behaviour and random-like properties. In his seminal work [1], Shannon pointed out the excellent possibilities of the dynamical chaotic maps in the communications. He identified two basic properties that the good data encryption systems should have to prevent (resist) statistical attacks: diffusion and confusion. Diffusion can propagate the change over the whole encrypted data, and confusion can hide the relationship between the original data and the encrypted data. Permutation, which rearranges objects, is the simplest method of diffusion, and substitution, that replaces an object with another one, is the simplest type of confusion. The consistent use of dynamical chaotic system based permutation and substitution methods is in the deep cryptographic fundamental.

The authors of [2] used Chebyshev polynomial to construct secure El Gamal-like and RSA-like algorithms. A new more practical and secure Diffie-Hellman key agreement protocol based on Chebyshev polynomial is presented in [3]. In [4], a stream cipher constructed by Duffing map based message-embedded scheme is proposed. By mixing the Lorenz attractor and Duffing map, a new six-dimensional chaotic cryptographic algorithm with good complex structure is designed [5]. In [6], an improved stochastic middle multibits quantification algorithm based on Chebyshev polynomial is proposed. Three-party key agreement protocols using the enhanced Chebyshev polynomial are proposed in [7, 8].

Fridrich [9] describes how to adapt Baker map, Cat map, and Standard map on a torus or on a rectangle for the purpose of substitution-permutation image encryption. In [10], a new permutation-substitution image encryption scheme using logistic, tent maps, and Tompkins-Paige algorithm is proposed. In [11], chaotic cipher is proposed to encrypt color images through position permutation part and Logistic map based on substitution. Yau et al. [12] proposed an image encryption scheme based on Sprott chaotic circuit. In [13], Fu et al. proposed a digital image encryption method by using Chirikov standard map based permutation and Chebyshev polynomial based diffusion operations.

In [14], a bit-level permutation scheme using chaotic sequence sorting has been proposed for image encryption. The operations are completed by Chebyshev polynomial and Arnold Cat map. An image encryption algorithm in which the key stream is generated by Chebyshev function is presented in [15]. Simulation results are given to confirm the necessary level of security. In [16], a new image encryption scheme, based on Chebyshev polynomial, Sin map, Cubic map, and 2D coupled map lattice, is proposed. The experimental results show the security of the algorithm.

In [17], a color image encryption scheme based on skew tent map and hyper chaotic system of 6th-order CNN is presented. An image encryption scheme based on rotation matrix bit-level permutation and block diffusion is proposed in [18].

A new chaos based image encryption scheme is suggested in this paper. The algorithm is a simple improvement of one round substitution-permutation model. The encryption process is divided in two major parts: Chebyshev polynomial based on permutation and substitution and Duffing map based on substitution. In Section 2, we propose two pseudorandom bit generators (PRBGs): one based on Chebyshev polynomial and the other based on Duffing map. In Section 3, in order to measure randomness of the bit sequence generated by the two pseudorandom schemes, we use NIST, DIEHARD, and ENT statistical packages. Section 4 presents the proposed image encryption algorithm, and some security cryptanalysis is given. Finally, the last section concludes the paper.

2. Proposed Pseudorandom Bit Generators

2.1. Pseudorandom Bit Generator Based on the Chebyshev Polynomial

In this section, the real numbers of two Chebyshev polynomials [2, 19] are preprocessed and combined with a simple threshold function to a binary pseudorandom sequence.

The proposed pseudorandom bit generator is based on two Chebyshev polynomials, as described by

where and are control parameters. The initial values and and parameters are used as the key.

Step  1. The initial values , , , and of the two Chebyshev polynomials from (1) are determined.

Step  2. The first and the second Chebyshev polynomials from (1) are iterated for and times to avoid the harmful effects of transitional procedures, respectively, where and are different constants.

Step  3. The iteration of (1) continues, and, as a result, two decimal fractions and are generated.

Step  4. These decimal fractions are preprocessed as follows:

where returns the absolute value of , returns the value of to the nearest integers less than or equal to , and returns the reminder after division.

Step  5. The following threshold function from (3) is applied:

and a pseudorandom bit is produced.

Step  6. Return to Step 3 until pseudorandom bit stream limit is reached.

2.2. Pseudorandom Bit Generator Based on the Duffing Map

In this section, the real numbers of two Duffing maps are preprocessed and combined with a simple threshold function to a binary pseudorandom sequence.

The Duffing map is a 2D discrete dynamical system which takes a point in the plane and maps it to a new point. The proposed pseudorandom bit generator is based on two Duffing maps, given by the following equations:

The maps depend on the two constants and . These are usually set to and to produce chaotic nature. The initial values , , , and are used as the key.

Step  1. The initial values , , , and of the two Duffing maps from (4) are determined.

Step  2. The first and the second Duffing maps from (4) are iterated for and times, respectively, to avoid the harmful effects of transitional procedures, where and are different constants.

Step  3. The iteration of (4) continues, and, as a result, two real fractions and are generated.

Step  4. The following threshold function from (5) is applied:

and a pseudorandom bit is produced.

Step  5. Return to Step 3 until pseudorandom bit stream limit is reached.

3. Statistical Test Analysis of the Proposed Pseudorandom Bit Generators

In order to measure randomness of the zero-one sequence generated by the new pseudorandom generators, we used NIST, DIEHARD, and ENT statistical packages.

The Chebyshev polynomial and the Duffing map based pseudorandom bit schemes are implemented by software simulation in C++ language, using the following initial seeds: , , , , ,  ,  , and , stated as a key K1.

3.1. NIST Statistical Test Analysis

The NIST statistical test suite (version 2.1.1) is proposed by the National Institute of Standards and Technology [20]. The suite includes 15 tests, which focus on a variety of different types of nonrandomness that could exist in a sequence. These tests are frequency (monobit), block-frequency, cumulative sums, runs, longest run of ones, rank, fast Fourier transform (spectral), nonoverlapping templates, overlapping templates, Maurer’s “universal statistical,” approximate entropy, random excursions, random-excursion variant, and serial and linear complexity. The testing process consists of the following steps:

(1)state the null hypothesis; assume that the binary sequence is random;(2)compute a sequence test statistic; testing is carried out at the bit level;(3)compute the -value; -value ;(4)fix , where ; compare the -value to ; is declared whenever -value ; otherwise, is declared.

Given the empirical results from a particular statistical test, the NIST suite computes the proportion of sequences that pass. The range of acceptable proportion is determined using the confidence interval defined as

where and is the number of binary tested sequences. In our setup, . Thus the confidence interval is

The proportion should lie above 0.9805607.

The distribution of -values is examined to ensure uniformity. The interval between 0 and 1 is divided into 10 subintervals. The -values that lie within each subinterval are counted. Uniformity may also be specified through an application of test and the determination of a -values corresponding to the goodness-of-fit distributional test on the -values obtained for an arbitrary statistical test, -values of the -values. This is implemented by calculating

where is the number of -values in subinterval and is the sample size. A -values is calculated such that , where is the complemented incomplete gamma statistical function. If , then the sequences can be deemed to be uniformly distributed.

Using the proposed pseudorandom Using the proposed pseudorandom bit generators were generated 1000 sequences of 1000000 bits. The results from all statistical tests are given in Table 1.

Table 1: NIST statistical test suite results for sequences of size 10⁶-bit each generated by the proposed Chebyshev polynomial based pseudorandom bit generator and Duffing map based pseudorandom bit generator.

NIST statistical test
Chebyshev PRBG
Duffing PRBG

-value
Pass rate
-value
Pass rate

Frequency (monobit)
0.649612
990/1000
0.490483
989/1000

Block-frequency
0.455937
991/1000
0.777265
992/1000

Cumulative sums (forward)
0.877083
990/1000
0.660012
988/1000

Cumulative sums (reverse)
0.983938
992/1000
0.284024
987/1000

Runs
0.062427
995/1000
0.490983
993/1000

Longest run of ones
0.215574
993/1000
0.612147
992/1000

Rank
0.848027
991/1000
0.212184
988/1000

FFT
0.194813
993/1000
0.013474
993/1000

Nonoverlapping templates
0.504571
990/1000
0.458442
990/1000

Overlapping templates
0.219006
992/1000
0.279844
988/1000

Universal
0.660012
986/1000
0.278461
991/1000

Approximate entropy
0.000478
990/1000
0.363593
991/1000

Random excursions
0.508738
597/603
0.671829
612/618

Random excursions variant
0.614825
596/603
0.490932
612/618

Serial 1
0.585209
991/1000
0.779188
990/1000

Serial 2
0.767582
989/1000
0.713641
993/1000

Linear complexity
0.711601
986/1000
0.699313
991/1000

The entire NIST test is passed successfully: all the -values from all 2 1000 sequences are distributed uniformly in the 10 subintervals and the pass rate is also in acceptable range. The minimum pass rate for each statistical test with the exception of the random excursion (variant) test is approximately 980 for a sample size of 1000 binary sequences for both of pseudorandom generators. The minimum pass rate for the random excursion (variant) test is approximately 589 for a sample size of 603 binary sequences for Chebyshev polynomial based PRBG and 604 for a sample size of 618 binary sequences for Duffing map based PRBG. This shows that the generated pseudorandom sequences feature reliable randomness.

Overall, the results confirm that the novel chaotic cryptographic schemes based on Chebyshev polynomial and Duffing map accomplish a very high level of randomness of the bit sequences.

3.2. DIEHARD Statistical Test Analysis

The DIEHARD suite [21] consists of a number of different statistical tests: birthday spacings, overlapping 5-permutations, binary rank (31 × 31), binary rank (32 × 32), binary rank (6 × 8), bit stream, Overlapping Pairs Sparse Occupancy, Overlapping Quadruples Sparse Occupancy, DNA, stream count-the-ones, byte count-the-ones, 3D spheres, squeeze, overlapping sums, runs up, runs down, and craps. For the DIEHARD tests, we generated two files with 80 million bits each, from the proposed chaotic pseudorandom bit generators. The results are given in Table 2. All -values are in acceptable range of . The proposed pseudorandom bit generators passed all the tests of DIEHARD software.

Table 2: DIEHARD statistical test results for two 80 million bits sequences generated by the proposed Chebyshev polynomial based pseudorandom bit generator and Duffing map based pseudorandom bit generator.

DIEHARD statistical test
Chebyshev PRBG
Duffing PRBG

-value
-value

Birthday spacings
0.377207
0.640772

Overlapping 5-permutation
0.410588
0.051538

Binary rank ()
0.551701
0.900609

Binary rank ()
0.940609
0.604265

Binary rank ()
0.530332
0.504383

Bit stream
0.428729
0.461876

OPSO
0.493583
0.498226

OQSO
0.582980
0.478843

DNA
0.632916
0.505181

Stream count-the-ones
0.759561
0.853126

Byte count-the-ones
0.605761
0.479987

Parking lot
0.425621
0.412316

Minimum distance
0.522822
0.486276

3D spheres
0.468043
0.414503

Squeeze
0.236035
0.416625

Overlapping sums
0.543661
0.439732

Runs up
0.234988
0.775408

Runs down
0.527703
0.679825

Craps
0.128550
0.423157

3.3. ENT Statistical Test Analysis

The ENT package [22] performs 6 tests (entropy, optimum compression, distribution, arithmetic mean value, Monte Carlo estimation, and serial correlation coefficient) to sequences of bytes stored in files and outputs the results of those tests. We tested output of the two strings of 125000000 bytes of the proposed Chebyshev polynomial based pseudorandom bit generator and Duffing map based pseudorandom bit generator, respectively. The results are summarized in Table 3. The proposed pseudorandom bit generators passed all the tests of ENT.

Table 3: ENT statistical test results for two 80 million bits sequences generated by the proposed Chebyshev polynomial based pseudorandom bit generator and Duffing map based pseudorandom bit generator, respectively.

ENT statistical test
Chebyshev PRBG results
Duffing PRBG results

Entropy
7.999999 bits per byte
7.999999 bits per byte

Optimum compression
OC would reduce the size of this 125000000 byte file by 0%
OC would reduce the size of this 125000000 byte file by 0%

distribution
For 125000000 samples it is 222.98 and randomly would exceed this value 92.68% of the time
For 125000000 samples it is 228.17 and randomly would exceed this value 88.54% of the time

Arithmetic mean value
127.49810 (127.5 = random)
127.5050 (127.5 = random)

Monte Carlo estimation
3.142062386 (error 0.01%)
3.140968178 (error 0.02%)

Serial correlation coefficient
−0.000026 (totally uncorrelated = 0.0)
0.000018 (totally uncorrelated = 0.0)

4. Image Encryption Based on Chebyshev polynomial and Duffing Map

Here, we describe an image encryption scheme based on the proposed Chebyshev polynomial and Duffing map based pseudorandom bit generators. We also provide security analysis of the encrypted images.

4.1. Encryption Scheme

The proposed image encryption algorithm is modification of the classical substitution-permutation scheme [9], column by column shuffling and masking procedures [23], and the diffusion-substitution model [24]. Here, every single pixel relocation is based on random permutation at once with substitution. The novel derivative scheme has the features of a two-round permutation-substitution color image encryption algorithm. The image encryption method is based on the unique combination of the output bits of the new proposed pseudorandom bit generators.

Without loss of generality, we assume that the dimension of the plain images is size, where is the number of rows and is the number of the columns. The binary lengths of and are and , respectively. The encryption process is divided into two stages. In the first stage, we generate buffer image of size by rearranging and modifying the pixel values of the plain image by Chebyshev polynomial based PRBG. In the second stage, we generate ciphered image of size by modifying the buffer pixel values by Duffing map based PRBG. The encryption process starts with empty buffer image. The plain image pixels are passed sequentially left to right and top to bottom. The entire encryption process is given below.

Step  1. The Chebyshev polynomial based PRBG is iterated continuously to produce and bits pseudorandom numbers and which are transformed modulo and , respectively.

Step  2. Repeat Step 1 until an empty pixel with () coordinates in the buffer image is detected.

Step  3. Continue to do iteration of Chebyshev polynomial based PRBG until 24 bits are produced.

Step  4. To produce buffered image pixel , do XOR between a plain image pixel and the last generated 24 bits.

Step  5. Repeat Steps 1–4 until all of the plain image pixels are processed.

Step  6. Iterate the Duffing map based PRBG to produce bits. Then, do XOR operation between the pseudorandom bit sequence and all of the buffer pixels in the buffered image to produce the encryption image .

For the reasons of security, we propose several overall rounds of the encryption procedure.

4.2. Security Analysis

The novel image encryption algorithm is implemented in C++ language. All experimental results discussed in the next subsections have been taken by using one iteration of the scheme.

Sixteen 24-bit color images have been encrypted for the security tests. The images are selected from the USC-SIPI image database, miscellaneous volume, available and maintained by the University of Southern California Signal and Image Processing Institute (http://sipi.usc.edu/database/). The image numbers are from 4.1.01 to 4.1.08, size 256 256 pixels, from 4.2.01 to 4.2.07, size 512 512 pixels, and House, size 512 512 pixels. The chosen images are currently stored in TIFF format and we have converted them into BMP format (24 bits/pixel).

4.2.1. Key Space Analysis

The key space is the set of all possible keys that can be used in encryption/decryption algorithm. The key of the proposed image encryption scheme is that it is produced by the combination of Chebyshev polynomial based PRBG and Duffing map based PRBG. The novel scheme has eight secret keys , , , , , , , and . According to the IEEE floating-point standard [25], the computational precision of the 64-bit double-precision number is about . If we assume the precision of , the secret key’s space is more than . This is large enough to defeat brute-force attacks [26] and it is larger than key space size of the image encryption algorithms proposed in [10, 27–29].

Moreover, the initial iteration numbers , , , and can also be used as a part of the secret key.

4.2.2. Visual Testing

The new algorithm is tested using simple visual inspection of the plain images and corresponding encrypted images. The visual observation does not find convergences between every plain image and its encrypted versions. As an example, Figure 1 shows the plain image 4.2.05 Airplane (F-16), Figure 1(a), and its encrypted version, Figure 1(b). The encrypted image does not contain any constant regions in representative color or texture. The proposed chaos based image encryption breaks any visual data from the plain images.

(a)

(b)

Figure 1: Comparison of the plain image and the encrypted image: (a) original picture 4.2.05 Airplane (F-16); (b) encrypted image of 4.2.05 Airplane (F-16).

4.2.3. Histogram Analysis

An image histogram of pixels is a type of a bar graph. It illustrates the visual impact of a distribution of colors that are at certain intensity. We have calculated histograms of red, green, and blue channels of both plain images and their encrypted version by the new image encryption scheme. One representative example among them is shown in Figure 2. The histograms of encrypted image are completely uniformly distributed and considerably different from that of the plain image.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 2: Histogram analysis of plain image and encrypted image: (a), (c), and (e) show the histograms of red, green, and blue channels of plain picture 4.2.05 Airplane (F-16); (b), (d), and (f) show the histograms of red, green, and blue channels of encrypted picture 4.2.05 Airplane (F-16).

In addition, the average pixel intensity calculations in Table 4, for all of the encrypted images, confirmed the uniformity in distribution of red, green, and blue channels.

Table 4: Average pixel intensity of plain image colors and encrypted image colors.

File name
Plain image color
Encrypted image color

Red
Green
Blue
Red
Green
Blue

4.1.01
75.827
52.559
46.305
127.661
127.203
127.458

4.1.02
42.075
30.086
27.540
128.033
127.233
127.413

4.1.03
137.603
139.958
144.018
127.265
127.394
127.697

4.1.04
129.218
99.267
125.199
127.393
127.275
127.021

4.1.05
146.564
133.000
142.023
127.779
127.268
126.878

4.1.06
132.202
124.902
143.263
127.563
127.377
127.346

4.1.07
179.204
180.650
142.348
127.152
127.325
127.378

4.1.08
174.897
170.866
128.346
126.856
127.426
127.563

4.2.01
176.270
70.494
108.898
127.562
127.118
127.468

4.2.02
234.195
208.644
163.552
127.101
127.729
127.722

4.2.03
137.391
128.859
113.117
127.199
128.126
127.392

4.2.04
180.224
99.051
105.410
127.266
127.311
127.519

4.2.05
177.577
177.852
190.214
127.331
127.565
127.654

4.2.06
131.007
124.304
114.893
127.254
127.850
127.450

4.2.07
149.821
115.568
66.534
127.368
127.379
127.257

House
155.436
168.226
142.209
127.419
127.659
127.525

4.2.4. Information Entropy

The entropy is statistical measure of uncertainty in information theory [1]. It is defined as follows:

where is a random variable and is the probability mass function of the occurrence of the symbol . Let us consider that there are 256 states of the information source in red, green, and blue colors of the image with the same probability. We can get the ideal , corresponding to a truly random source.

The information entropy of red, green, and blue channels of the plain images and their corresponding encrypted images are computed and displayed in Table 5. From the obtained values, it is clear that the entropies of red, green, and blue colors of the encrypted images are very close to the best possible theoretical value, which is an indication that the new chaos based image encryption scheme is trustworthy and secure upon information entropy attack.

Table 5: Entropy results of plain images and encrypted images.

File name
Plain image color
Encrypted image color

Red
Green
Blue
Red
Green
Blue

4.1.01
6.42005
6.44568
6.38071
7.96418
7.96805
7.96648

4.1.02
6.24989
5.96415
5.93092
7.96622
7.96734
7.96629

4.1.03
5.65663
5.37385
5.71166
7.96606
7.96740
7.96398

4.1.04
7.25487
7.27038
6.78250
7.96692
7.96552
7.96741

4.1.05
6.43105
6.53893
6.23204
7.96587
7.96618
7.96776

4.1.06
7.21044
7.41361
6.92074
7.96598
7.96697
7.96786

4.1.07
5.26262
5.69473
6.54641
7.96392
7.96634
7.96632

4.1.08
5.79199
6.21951
6.79864
7.96515
7.96651
7.96782

4.2.01
6.94806
6.88446
6.12645
7.96799
7.96762
7.96848

4.2.02
4.33719
6.66433
6.42881
7.96825
7.96838
7.96582

4.2.03
7.70667
7.47443
7.75222
7.96999
7.96778
7.96859

4.2.04
7.25310
7.59404
6.96843
7.96777
7.96932
7.96998

4.2.05
6.71777
6.79898
6.21377
7.96715
7.96807
7.96883

4.2.06
7.31239
7.64285
7.21364
7.96799
7.96749
7.96791

4.2.07
7.33883
7.49625
7.05831
7.96864
7.96756
7.96730

House
7.41527
7.22948
7.43538
7.96849
7.96825
7.96735

4.2.5. Correlation Coefficient Analysis

The adjacent pixels in plain images are strongly correlated in either horizontal, vertical, or diagonal direction. The correlation coefficient between two adjacent pixels is computed in accordance with the way described in [30]. Consider

where

is the total number of couples , obtained from the image, and , are the mean values of and , respectively. Correlation coefficient can range in the interval .

Table 6 shows the results of horizontal, vertical, and diagonal adjacent pixels correlation coefficients calculations of the plain images and the corresponding encrypted images. It is clear that the novel image encryption scheme does not retain any linear dependencies between observed pixels in all three directions: the inspected horizontal, vertical, and diagonal correlation coefficients of the encrypted images are very close to zero. Overall, the correlation coefficients of the proposed algorithm are similar to results of four other image encryption schemes [27–30].

Table 6: Horizontal, vertical and diagonal correlation coefficients of adjacent pixels in plain images and encrypted images.

File name
Plain image correlation
Encrypted image correlation

Horizontal
Vertical
Diagonal
Horizontal
Vertical
Diagonal

4.1.01
0.956725
0.952503
0.937836
0.001274
0.001785
0.003044

4.1.02
0.908923
0.944135
0.889084
−0.007292
0.007162
0.004493

4.1.03
0.970861
0.916864
0.895799
−0.005882
−0.004236
0.002140

4.1.04
0.956759
0.964448
0.930833
−0.004143
−0.006414
0.008894

4.1.05
0.982138
0.974908
0.962532
0.005939
−0.001269
−0.002035

4.1.06
0.959183
0.934498
0.926566
0.003809
0.011929
−0.002274

4.1.07
0.988603
0.987932
0.979855
−0.008391
0.001379
−0.000308

4.1.08
0.977248
0.979839
0.958275
0.000991
−0.000089
−0.002968

4.2.01
0.978507
0.970863
0.964947
0.001250
−0.000860
0.001454

4.2.02
0.896888
0.909936
0.863983
0.000449
0.001230
−0.000765

4.2.03
0.907119
0.877498
0.839639
−0.001320
−0.000628
−0.000366

4.2.04
0.933223
0.958036
0.918587
−0.004386
0.000342
0.000569

4.2.05
0.962496
0.915378
0.914867
0.004689
0.000547
0.000136

4.2.06
0.969769
0.968659
0.953038
0.000850
0.005358
0.003821

4.2.07
0.964885
0.961169
0.948114
0.001554
−0.001897
0.002504

House
0.975076
0.959036
0.944382
0.001099
−0.002301
0.001799

4.2.6. Differential Analysis

In the main, a typical property of an image encryption scheme is to be sensitive to minor alterations in the plain images. Differential analysis supposes that an enemy is efficient to create small changes in the plain image and inspect the encrypted image. The alteration level can be measured by means of two metrics, namely, the number of pixels change rate (NPCR) and the unified average changing intensity (UACI) [30, 31].

Suppose encrypted images before and after one pixel change in plain image are and . The NPCR and UACI are defined as follows:

where is a two-dimensional array, having the same size as image or , and and are the width and height of the image. The array is defined by and ; if , then ; otherwise, . The NPCR and UACI test results from the proposed chaos based algorithm are shown in Table 7.

Table 7: NPCR and UACI results of encrypted plain images and encrypted with one pixel difference plane images.

File name
NPCR test
UACI test

Red
Green
Blue
Red
Green
Blue

4.1.01
99.5701
99.5911
99.6155
33.6394
33.3493
33.4648

4.1.02
99.6613
99.5743
99.5804
33.4397
33.3669
33.4438

4.1.03
99.6094
99.6216
99.6109
33.3171
33.5476
33.3226

4.1.04
99.6323
99.6384
99.6155
33.4149
33.5338
33.3298

4.1.05
99.5987
99.6201
99.5743
33.4149
33.5670
33.4883

4.1.06
99.5693
99.5972
99.5705
33.4311
33.4601
33.4934

4.1.07
99.5941
99.6094
99.5972
33.5586
33.4576
33.4826

4.1.08
99.6490
99.6414
99.6536
33.4083
33.5591
33.4937

4.2.01
99.6346
99.6033
99.6311
33.4646
33.4548
33.4644

4.2.02
99.6120
99.6056
99.5894
33.4073
33.5304
33.4401

4.2.03
99.6197
99.6021
99.6273
33.4377
33.3873
33.4169

4.2.04
99.6109
99.6094
99.6185
33.4668
33.5337
33.3924

4.2.05
99.6025
99.5975
99.6101
33.4869
33.4080
33.4413

4.2.06
99.6136
99.5953
99.6361
33.3937
33.5238
33.5039

4.2.07
99.6426
99.6231
99.6300
33.4659
33.5129
33.4368

House
99.6357
99.6082
99.6220
33.3519
33.4210
33.4300

The obtained NPCR values for the images from 4.1.01 to 4.1.08 are larger than critical values , , and and, for the images from 4.2.01 to 4.2.07 and House image, are larger than critical values , , and [31].

The obtained UACI values for the images from 4.1.01 to 4.1.08 are in the intervals from to , from to , and from to . The obtained UACI values for the images from 4.2.01 to 4.2.07 and House image are in the intervals from to , from to , and from to [31].

The results from NPCR and UACI computations indicate that the new image encryption scheme is highly sensitive with respect to small changes in the plain images and has a strong ability of resisting differential cryptanalysis.

4.2.7. Key Sensitivity Test

Another important component of correlation analysis is the key sensitivity test. A good image encryption algorithm should be sensitive with respect to the secret key, that is, a slight modification of the secret key. We encrypted the 16 images with three similar secret keys: K1, K2 (, , , , , , , and ), and K3 (, , , , , , , and ). The results are shown in Table 8. It is evident that the proposed image encryption is highly key sensitive: the calculated correlation coefficients are very close to 0.00.

Table 8: Correlation coefficients between the corresponding pixels of the 48 different encrypted images obtained from the 16 plain images by using the three slightly different secret keys: K1, K2, and K3.

Encrypted image 1
Encrypted image 2
Correlation coefficient
Encrypted image 1
Encrypted image 2
Correlation coefficient

4.1.01.K1
4.1.01.K2
0.006252
4.2.01.K1
4.2.01.K3
0.001802

4.1.02.K1
4.1.02.K2
0.002129
4.2.02.K1
4.2.02.K3
0.000867

4.1.03.K1
4.1.03.K2
0.006434
4.2.03.K1
4.2.03.K3
0.001430

4.1.04.K1
4.1.04.K2
0.001634
4.2.04.K1
4.2.04.K3
0.000064

4.1.05.K1
4.1.05.K2
−0.001745
4.2.05.K1
4.2.05.K3
0.003107

4.1.06.K1
4.1.06.K2
−0.005686
4.2.06.K1
4.2.06.K3
−0.001260

4.1.07.K1
4.1.07.K2
0.000907
4.2.07.K1
4.2.07.K3
−0.001401

4.1.08.K1
4.1.08.K2
−0.003864
House.K1
House.K3
−0.001986

4.2.01.K1
4.2.01.K2
0.000299
4.1.01.K2
4.1.01.K3
−0.002088

4.2.02.K1
4.2.02.K2
0.001053
4.1.02.K2
4.1.02.K3
−0.001454

4.2.03.K1
4.2.03.K2
0.000103
4.1.03.K2
4.1.03.K3
−0.003497

4.2.04.K1
4.2.04.K2
0.001290
4.1.04.K2
4.1.04.K3
−0.002121

4.2.05.K1
4.2.05.K2
0.000557
4.1.05.K2
4.1.05.K3
−0.002167

4.2.06.K1
4.2.06.K2
−0.000290
4.1.06.K2
4.1.06.K3
0.000598

4.2.07.K1
4.2.07.K2
0.001601
4.1.07.K2
4.1.07.K3
0.004454

House.K1
House.K2
0.000905
4.1.08.K2
4.1.08.K3
0.001396

4.1.01.K1
4.1.01.K3
−0.001953
4.2.01.K2
4.2.01.K3
0.004092

4.1.02.K1
4.1.02.K3
0.002054
4.2.02.K2
4.2.02.K3
−0.000099

4.1.03.K1
4.1.03.K3
0.004989
4.2.03.K2
4.2.03.K3
0.000007

4.1.04.K1
4.1.04.K3
0.001796
4.2.04.K2
4.2.04.K3
0.000170

4.1.05.K1
4.1.05.K3
−0.000826
4.2.05.K2
4.2.05.K3
0.002596

4.1.06.K1
4.1.06.K3
0.004114
4.2.06.K2
4.2.06.K3
0.003894

4.1.07.K1
4.1.07.K3
−0.000977
4.2.07.K2
4.2.07.K3
−0.001332

4.1.08.K1
4.1.08.K3
−0.000203
House.K2
House.K3
−0.000282

Moreover, in Figure 3, the results of two tests are shown to decrypt the Figure 1(b), with the secret keys K2 and K3.

(a)

(b)

Figure 3: Decryption of Image 4.2.05 Airplane (F-16): (a) encrypted with key K1 and decrypted using key K2 and (b) encrypted with key K1 and decrypted using key K3.

We observed that the two decrypted images (Figure 3(a) and Figure 3(b)) have no relation with the plain image 4.2.05, Figure 1(a).

4.2.8. Speed Test

We have measured the encryption time for 256 256 and 512 512 sized images by using the novel image encryption algorithm. Speed analysis has been done on 2.8 GHz Pentium IV personal computer. In Table 9, we compared the speed of our method with [24, 32, 33]. The data show that the proposed image encryption scheme has a satisfactory speed.

Table 9: Time test (seconds).

Image size
Reference [24]
Reference [32]
Reference [33]
Our scheme

0.22
1.34
0.35
0.19

1.04
5.26
0.72
0.61

5. Conclusions

A novel image encryption algorithm based on dynamical chaotic systems is proposed in this paper. The developed encryption scheme combines Chebyshev polynomial based permutation and substitution and Duffing map based substitution. A precise security analysis on the novel encryption algorithm is given. Based on the experimental results of our computations, we conclude that the proposed chaos based image encryption technique is perfectly suitable for the practical image encryption.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This paper is supported by the Project BG051PO001-3.3.06-0003 “Building and steady development of PhD students, post-PhD and young scientists in the areas of the natural, technical and mathematical sciences.” The project is realized by the financial support of the Operative Program “Development of the human resources” of the European social fund of the European Union.

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