The Scientific World Journal

Volume 2014 (2014), Article ID 536930, 21 pages

http://dx.doi.org/10.1155/2014/536930

Research Article

A Chaotic Cryptosystem for Images Based on Henon and Arnold Cat Map

Ali Soleymani,¹ Md Jan Nordin,² and Elankovan Sundararajan¹

¹Software Technology and Management Center (Softam), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia

²Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia

Received 30 April 2014; Accepted 15 July 2014; Published 28 August 2014

Academic Editor: Iftikhar Ahmad

Copyright © 2014 Ali Soleymani et al. 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.

The rapid evolution of imaging and communication technologies has transformed images into a widespread data type. Different types of data, such as personal medical information, official correspondence, or governmental and military documents, are saved and transmitted in the form of images over public networks. Hence, a fast and secure cryptosystem is needed for high-resolution images. In this paper, a novel encryption scheme is presented for securing images based on Arnold cat and Henon chaotic maps. The scheme uses Arnold cat map for bit- and pixel-level permutations on plain and secret images, while Henon map creates secret images and specific parameters for the permutations. Both the encryption and decryption processes are explained, formulated, and graphically presented. The results of security analysis of five different images demonstrate the strength of the proposed cryptosystem against statistical, brute force and differential attacks. The evaluated running time for both encryption and decryption processes guarantee that the cryptosystem can work effectively in real-time applications.

1. Introduction

Some researchers utilized conventional cryptosystems to directly encrypting images. But this is not advisable due to large data size and real-time constraints of image data. Conventional cryptosystems require a lot of time to directly encrypt thousands of image pixels value. On the other hand, unlike textual data, a decrypted image is usually acceptable even if it contains small levels of distortion. For all the above mentioned reasons, the algorithms that function well for textual data may not be suitable for multimedia data [1]. Many studies have been performed on the use of textual encryption algorithms for images by modifying the algorithms to adapt with image characteristics. One such option for encrypting an image is to consider a 2D array of image pixels value as a 1D data stream and to then encrypt this stream with any conventional cryptosystem [2, 3]. This would be considered a naïve approach and usually is suitable for text and occasionally for small images files that are to be transmitted over a fleet dedicated channel [4]. Subramanyan et al. [5] proposed an image encryption algorithm based on AES-128 in which the encryption process is a bitwise XOR operation on a set of image pixels. This method employs an initial 128-bit key and an AES key expansion process that changes the key for every set of pixels. The secret keys are generated independently at both the sender and the receiver sides based on the AES key expansion process. Therefore, the initial key alone is shared rather than the whole set of keys.

2. Chaos and Cryptography

Chaotic maps are simple functions and are iterated quickly. Chaos-based image encryption systems are therefore fast enough for real-time applications. Chaos is a natural phenomenon discovered by Edward Lorenz in 1963 while studying the butterfly effect in dynamical systems. The butterfly effect describes the sensitivity of a system to initial conditions as mentioned in Lorenz’s paper titled “Does the Flap of a Butterfly’s Wings in Brazil set off a Tornado in Texas?” [6]. The flapping wings represent a tiny variation in the initial conditions of the dynamic system that causes a chain of events leading to large-scale changes in the future. Had the butterfly not flapped its wings, the trajectory of the system might have been vastly different [7]. In general, this means that a small variance in the initial parameters (even in ten-millionth place value) could yield widely divergent results. Hence, for a chaotic system, rendering long-term prediction is impossible in general. This means that having initial conditions of these systems makes their future behavior predictable. This behavior, which derived from a natural phenomenon, is known as deterministic chaos or, simply, chaos and exhibits by chaotic maps. Such maps are classified as continuous maps and discrete maps.

In the 1990s, numerous researchers found that there are some relationships between properties that have counterparts in chaos and cryptography. A high sensitivity to initial conditions, with deterministic pseudorandom behavior, is an interesting similarity between chaotic maps and cryptographic algorithms. Furthermore, confusion and diffusion are two general principles in the design of cryptography algorithms that lead to the concealing of the statistical structure of pixels in a plain image and to a decrease in the statistical dependence of a plain image and the corresponding encrypted one. Applying a mixing property on chaos-based encryption algorithms will increase the complexity of the cipher image.

Chaotic maps are assigned to discrete and continuous time-domains. Discrete maps are usually in the form of iterated functions, which corresponded to rounds in cryptosystems. This similarity between cryptography and discrete chaotic dynamic systems is utilized to propose chaotic cryptosystems. Each map has some parameters that are equivalent to the encryption keys in cryptography. In stream cipher, a chaotic system is applied to generate a pseudorandom key stream but in block ciphers, the plaintext or the secret key(s) are used as the initial and control parameters. Finally, some iteration is applied on the chaotic systems to obtain the cipher-text. Security and complexity are significant concerns in cryptosystems. These should be considered when selecting a map and its parameters for use in cryptography [8].

2.1. Related Works

The first chaos-based cryptosystem was proposed by Matthews in 1989 [29]. Subsequently, the amount of research on chaotic cryptography increased rapidly, while trying to break (and find the weakness of) the proposed schemes in order to improve chaos-based cryptosystems.

The algorithm proposed by Wang et al. [30] for encrypting color images utilizing a logistic map was broken by Li et al. [31]. Another cryptosystem analyzed by Li et al. [32] is the recent work of Zhu [26]. Zhu applied hyperchaotic sequences to generate the key stream but Li in his work proved that the proposed algorithm was not sufficiently robust against a chosen plaintext attack. Another weak cryptosystem is the combination of the Lorenz map and perceptron model of the neural network proposed by Wang et al. [33]. This chaotic algorithm was cracked by Zhang et al. [34] after analyzing its security by simulated attacks. The experimental results show that the secret key can be reconstructed after one pair of known-plaintext/ciphertext attacks. Furthermore, the effect of changing one bit in the plain image is a change in only one bit at the same position in an encrypted image. This is another weakness of Wang’s proposed algorithm.

Many similar works have failed in security analysis. Hence, when designing and implementing a chaos-based cryptographic system, some important requirements should be kept in mind. A common framework was proposed by Alvarez and Li [35] for chaos-based cryptosystem designers. Implementation rules, key management tips, and security analysis approaches are three main issues suggested in their work. Adhering to these basic guidelines guarantees an acceptable level of security with the chaos-based cryptosystem scheme. Moreover, Alvarez and Li in [36] established a practical security analysis of a cryptosystem based on the Baker map [37]. In addition to breaking this cryptosystem due to vulnerability of the key, some countermeasures are introduced for improving and enhancing the security of similar cryptosystems. Alvarez and Li in another cryptanalysis work [38] presented that the nonlinear chaotic algorithm by Gao et al. [39] is insecure according to failure in the plaintext attack and statistical and key space analysis.

Chaos-based encryption algorithms are based on diverse types of chaotic maps and also on discrete maps. Most of these are a combination of two or more chaotic maps to achieve a greater level of complexity, security, and expanded key space. A combination of the Arnold cat map and the Chen map was the work of Guan et al. [40]. The Arnold cat map was applied to clutter the position of the pixels followed by XOR with the discrete output signal of the Chen map to modify the gray value of the cluttered pixels. This was analyzed and improved by Xiao et al. [41]. They found the weakness of the proposed algorithm and overcame the flaws.

To overcome the disadvantages of permutation-only cryptosystems, Fu et al. [12] proposed a novel shuffling algorithm which performs an efficient bit-level permutation in two stages of chaotic sequence sorting and Arnold cat map. Their analysis results show that this scheme is more secure and has much lower computational complexity than previous similar works.

Xu et al. [10] analyzed the improved work of Xiang et al. [42] and found two drawbacks. In their proposed letter, iterating Chen chaotic system generates random number sequence, which is more random in comparison with the sequence that was generated by logistic map in [42]. The second drawback is overcome by setting the parameter of Chen map using the last one byte of encrypted plaintext after every iteration that leads to a higher sensitivity of encrypted image to the plain one. This scheme is fast and secure according to simulation results and large size of key space, respectively.

To overcome the drawback of time-consuming real number arithmetic calculations in chaos-based image encryption techniques, a block cipher cryptosystem was proposed by Fouda et al. [27]. This fast and secure chaotic scheme is based on sorting the integer coefficients of linear diophantine equation (LDE), which is generated dynamically by only two rounds of any chaos map.

The scheme of Chen et al. [28] is another work that is proposed to enhance the efficiency of chaos-based encryption. They found that permutation-diffusion encryption approaches are produce with high computation of at least two chaotic maps and weak against known/chosen plaintext attacks. Hence, they proposed a dynamic mechanism to generate the state variables from the 3D or hyperchaotic maps for snake-like diffusion and pixel-swapping confusion. A tiny change (e.g., one pixel) will make a totally different key stream sequence at the first round of encryption.

Table 1 is a brief overview of some chaotic maps applied in image encryption. The Arnold cat map is the most commonly used map in chaos-based image encryption works with the main purpose of shuffling pixels of an image in a pseudorandom order.

Table 1: Applied chaos maps in some proposed image encryption techniques.

ACM
Logistic
Henon
Lorenz
Baker
Chen
Tent
CML
Standard map

Zhu et al. [9]
×
×

Xu et al. [10]

×

Zhang and Cao [11]
×

×

Fu et al. [12]
×

Zhang et al. [13]
×
×
×

Ghebleh et al. [14]
×

×

Elshamy et al. [15]

×

Ye and Zhou [16]

×
×

Ye and Zhou [17]

×
×

Wang et al. [18]

×

Al-Maadeed et al. [19]

×

Patidar et al. [20]

×

×

Wong et al. [21]

×

Guanghuia et al. [22]

×

Zhang et al. [23]

×

Liu et al. [24]
×
×

2.2. Henon Map

Henon is a two-dimensional dynamic system proposed [43] to simplify the Lorenz map [44] with the same properties and is defined by (1). This might be easier to implement than the differential equations of the Lorenz system. Consider

The initial parameters are and the initial point is . Each point is mapped to a new point through the Henon map. For and , the Henon function has chaotic behavior and the iterations have a boomerang-shaped chaotic attractor. Figure 1 is the outline on a two-dimensional plane for the Henon map obtained from a distinct number of iterations starting from the chosen initial point . Minute variations in the initial point will lead to major changes and different behavior.

(a)

(b)

(c)

Figure 1: Henon map attractor after (a) 500, (b) 5000, and (c) 50000 iterations with initial parameters and and initial point .

2.3. Arnold Cat Map

ACM is a mixing discrete ergodic system that performs an area preserving stretch and fold mapping discovered by V. Arnold in 1968 using the image of a cat. This 2D transformation is based on a matrix with a determinant of 1 that makes this transformation reversible and described as

Here, and are integers and is the original position that is mapped to the new position . This transformation randomizes the original order of pixels or bits in an image. However, after sufficient iterations, the original image is reconstructed. Reverse mapping using (3) is a phase in decryption process to transform the shuffled image into the input image. The number of iterations in the permutation step must be equal to that of the reverse transformation. Consider

3. Proposed Cryptosystem Model

3.1. Initializing Prerequisite Values

In addition to , , and initial point in (1), there are some other variables that must be initialized before running the algorithm. The proposed encryption architecture is shown in Figure 2. This scheme is based on two secret images and permutation steps in the bit level and pixel level. In the bitwise permutation, the pixel values are distorted but, in the pixel permutation, the pixels are shuffled without any alteration in value and histogram.

Figure 2: Proposed encryption scheme architecture.

Creating the secret images and a set of parameters and for the Arnold cat map are prerequisites for the encryption and decryption processes. Secret images have pseudo-random-like gray pixel distributions and are created using coordinates and generated by a Henon map. The secret images are the same as the plain image in height and width; therefore, the number of iterations for the Henon map depends on the total pixels in the plain image. In this work, experiments are performed on gray-level images. Hence, the minimum iterations of Henon map should be . The first few iterations seem fairly close together. Therefore, the total number of iterations is , but the first 100 points are discarded to achieve higher randomness. Secret image pixels are generated using (4) and (6). The and are sets of pseudorandom numbers (, created by -coordinates and -coordinates of the Henon map and are considered pixel values. To shape the one-dimensional pixel values into an image, (5) and (7) are applied to create the 2D secImgX and secImgY secret image. The final secret image is generated by a combination of secImgX and secImgY by performing the XOR operation on the corresponding pixels as described by (8). Consider

The permutation steps by the Arnold cat map are based on the parameters and . The -coordinates and -coordinates that result from the iterations of the Henon map are applied to generate parameters for the ACM. The pixels or bits of an input image that are permuted by the Arnold cat map return to its preliminary position after finite iterations. Attackers may be able to restore the original image by using this periodicity. To avoid such reconstruction of the input image, iteration is repeated for rounds with different values for the parameters and in each round. Equations (9) and (10) generate parameter values for the Arnold cat map. The number of generated parameters is equal to the total number of permutation rounds:

3.2. Encryption Process

Figure 2 presents the architecture of the proposed encryption scheme. This scheme has three inputs and three main functions, and the final result is the encrypted image. The plain image, secImgX, and secImgY are the three main inputs for this model. The primary functions are bit permutation, pixel permutation, and pixel modification. As illustrated in Figure 2, at the first step, secImgX and secImgY are XORed pixel by pixel to generate secImg. Then, pixels of the secret image are permuted rounds. A simultaneous step is rounds of bit-level permutation of the plain image. The outputs of these two phases are applied to pixel modification, which is a sequence XOR of consecutive pixels. The result is fed back to the bit permutation function (instead of the plain image) for additional rounds while the secImg is permuted with new parameters at each round. The functions details are described in the following sections.

3.2.1. Bit Permutation

For a gray-level image with a size of pixels, the total bits are . Prior to bit permutation, the input image is divided into eight subimages. Each subimage is pixels or bits in height and width as shown in Figure 3. Matrix (11) shows how the th subimage is created. Replacing the corresponding pixel values of the input image at the proper position of the matrix would create the subimage. A pixel in the position is an 8-bit value in the form of (12), where is the most significant bit (MSB) and is the least significant bit (LSB) of the pixel value in binary form. Every pixel value of the subimage converts to its binary format and creates the bit-plane. The bit-plane is a matrix with rows and columns and each element is one bit 0 or 1. Matrix (13) shows how to create the matrix for the bit-plane. Each subimage is converted to the equivalent bit-plane and each bit-plane is permuted separately and independently:

Figure 3: Splitting an image to 8 subimages.

After creating the bitPlane matrices, the Arnold cat map was applied to these matrices to permute the bits. In the permutation phase, the new location of each bit is calculated by (14). The pair of is the new position of . At the first phase, the input image is permuted times with different parameters and where . Consider

After finishing this phase, the bitPlane matrices are changed to decimal values to reconstruct the image pixels.

3.2.2. Pixel Permutation

Concurrent with bit permutation of the plain image, the secret image is permuted for rounds at the pixel level to change the position of the pixels in a random manner. In contrast to bit permutation, pixel permutation does not affect the pixel values. Therefore, histograms of the plain image and the shuffled image are entirely the same. The permutation is performed times with different parameters and with . Consider

3.2.3. Pixel Modification

The concluding phase is a sequence XOR to modify the pixel values. This step will cause extreme changes in the pixels of cipher image with even one bit change in a pixel in plain image. This step is based on the shuffled secret image and the bit-level permuted plain image. Equations (16) and (17) are used to change pixels consecutively. The output of this step is a modified image. For more confusion and modification, this step will repeat for rounds. After each round, the result is replaced with a plain image, as input, and the secret image will permute rounds. After completing rounds, the final output is the cipher image. Consider

3.3. Decryption Process

Figure 4 shows the decryption process. On the receiving side, the secret image is generated by XORing secImgX and secImgY, which are recreated using private parameters. Inverse pixel modification is performed on the cipher image and the secret image after rounds of pixel permutations. The result of the secret image pixel permutation in the first step is saved for subsequent steps and at each round this is inverse permuted for rounds and applied as an input to the inverse pixel modification function. The additional input is (the feedback of) the output of the inverse pixel modification function after rounds of inverse bit permutation.

Figure 4: Decryption model architecture.

4. Experiments and Security Analysis

Experiments are carried out to analyze the proposed algorithm and evaluate their security and robustness. A vigorous encryption algorithm is resistant to attacks by an opponent or to unauthorized access. Due to the various types of attacks, a comprehensive security analysis is inevitable. This section analyzes the results of simulated attacks such as a statistical attack, a differential attack, a brute-force attack, and a known/chosen plaintext attack to demonstrate the strength of the proposed technique. Key space, encryption time, and decryption time are additional parameters that will affect the decision-making regarding the choice of applied cryptosystem. The selected images for the experiments are “Peppers,” “Baboon,” and “Fingerprint,” which are 512 512 gray images. “Cameraman” and “Chess-plate” are two additional images of size 256 256. The proposed algorithm is implemented using the MATLAB programming language on a PC with a 64-bit OS, Core i5 CPU, and 8 GB installed RAM.

4.1. Initial Values

The Henon map is the main function in this cryptosystem and the generated chaotic sequence is employed to produce both the secret image and the parameters for the Arnold cat map. Its initial value is one of the secret keys in this scheme. The chosen point (1.210000001, 0.360000001) is the starting point for generating the Henon chaotic sequence. and have fixed values of 1.4 and 0.3, respectively. As mentioned above, the test images sizes are 512 512 and 256 256 pixels. The number of iterations for the Henon map is based on the image size. The larger image has a total of 262144 pixels. The Henon map (18) is iterated 262244 times, but the first 100 points are discarded. Consider

Having obtained the set of and , we can create pixels of secImgX and secImgY by setting the values and in (4) and (6) as shown in (19) and (21), respectively. Then, we reshape them to form an image with the same size as the plain image using (20) and (22). The final secret image is the result of (23). Consider

The parameters for the Arnold cat map that perform permutations on the plain image and secret images are the set of and . Each pair of and is a modified value of a point on the Henon sequence. These coordinates are real numbers. They converted to integer numbers using multiplied, modular, and absolute operations as described in (8) and (9). By setting quantities and , and are in the form of

4.2. Running Time

Pixel permutation, bit permutation, and pixel modification are three key functions in this cryptosystem. The encryption time depends on the run time of each function and the number of rounds. Table 2 presents the average run time for one round of each function in this cryptosystem on a 512 512 image. Additional tests show a linear relation between running time and number of pixels. The results for 256 256 were almost a quarter of the given intervals in Table 2. The total encryption time is calculated by (25) and the decryption time is calculated by (26). Table 3 is the encryption time of proposed cryptosystem in comparison with recent similar works for a gray image. Consider

Table 2: Evaluated running time for main functions in proposed cryptosystem.

Variable
Function
Running time for one round (ms)

Bit permutation running time
62

Pixel permutation running time
13

Pixel modification running time
4

Inverse bit permutation running time
75

Inverse pixel permutation running time
23

Inverse pixel modification running time
4

Table 3: Comparison of encryption time for proposed algorithm with recent similar works.

Proposed scheme
[12]
[10]
[25]
[26]
[27]

Encryption time (ms)
19.75
23
22
20.79
32
52

4.3. Encryption and Decryption Illustrations and Histogram

The image histogram is the graphical illustration of the pixel distribution at different gray levels. A great deal of statistical information regarding the image is extractable from its histogram [45]. The histogram of an encrypted image should have a uniform distribution and be completely different from that of the plain image. This prevents the leakage of any meaningful information from the plain image. In addition to the histogram, the encrypted image must be absolutely unique in appearance without any similar pattern to the original image. Basically, the histogram analysis results are demonstrated for a bit-permuted plain image based on the proposed technique to compare with the proposed scheme by Zhu et al. [9]. Despite the pixel value alteration by Zhu’s model, the histogram of the permuted image is not uniform. This is due to the permutation of a group of bits in the same position. According to Shannon’s theory, the 8th bit (MSB) pixel values carry almost 50% of the total information of the image. Permuting all of the bits at the same location will not vastly change the image pixels’ value. Hence, the plain image, which is shown in Figure 5, is bit permuted for four rounds, and the result and its histogram are illustrated in Figure 6. It seems that there are patterns that still appear in the image. To overcome this vulnerability, a bit permutation scheme is proposed and shuffles the bits entirely and independently of its position. Figure 7 shows the subimages of the plain image that would be permuted independently. After four rounds of permuting each subimage with altered parameters, the result and its histogram are shown in Figure 7. Visual comparison of images and histograms in Figures 6 and 7 demonstrates the efficacy of the proposed bit-permutation model. Figures 8 and 9 are the images and the histograms after the encryption and decryption process, respectively. The decrypted image is the same as the original one and this technique is found to be lossless.

(a)

(b)

Figure 5: (a) Plain image and (b) its histogram.

(a)

(b)

Figure 6: (a) Plain image after four rounds of bit permutation based on Zhu’s algorithm and (b) its histogram.

(a)

(b)

(c)

Figure 7: (a) Plain subimages, (b) plain image after four rounds of proposed bit permutation and (c) its histogram.

(a)

(b)

Figure 8: (a) Encrypted image and (b) its histogram.

(a)

(b)

Figure 9: (a) Decrypted image and (b) its histogram.

4.4. Key Analysis

4.4.1. Key Space Analysis

The total number of possible keys that an attacker must try to break a cryptosystem is called key space and it should be large enough to prevent brute-force attack. In the proposed cryptosystem, the initial point of the Henon map was used as one of the secret keys. Other control parameters are , and . These parameters should be kept secret and be used as secret keys. Table 4 is the upper bound for each variable. A combination of these parameters will provide a large key space of approximately 2³⁰⁰ that is sufficient to make brute-force attack infeasible and very large rather than similar works that are compared in Table 5.

Table 4: Secret parameters length in bit.

Parameter
Length (bit)

10

10

10

48

48

24

24

64

64

Table 5: Comparison of encryption time for proposed algorithm with recent similar works.

Proposed scheme
[12]
[10]
[25]
[26]
[28]
[27]

Key space

4.4.2. Key Sensitivity Analysis

In addition to a sufficiently large key space to protect an encrypted image from brute-force attacks, a strength algorithm should also be absolutely sensitive to both encryption and decryption keys. Changing even one bit in a secret key will cause a completely different result in either the encrypted image or the decrypted image. Key sensitivity is analyzed in both the encryption and the decryption phase. In the encryption phase, the cipher image that results from changing even one bit in any one of the initial values is compared with the encrypted image that resulted before changing the key. The results are given in Table 6. Several experiments were performed and, in each experiment, only one parameter was manipulated while others were unchanged. The changed values and the difference rates for the produced images are listed in the table.

Table 6: Difference rates of two encrypted images with slight change in a parameter.

Parameter
Initial value
Changed value
Encrypted images difference rate

6
7
99.59%

2
1
99.60%

2
1
99.62%

12345678
12345679
99.58%

87654321
87654320
99.62%

12345
12346
99.63%

67890
67891
99.60%

1.21000001
1.21
99.59%

0.36000001
0.36
99.60%

In the decryption phase, key sensitivity means that the encrypted image cannot be decrypted by slight variations in the secret key. Based on the results in Table 7, changing even one bit in the decryption key will result in a wholly different decrypted image.

Table 7: Difference rate of two decrypted images with slight change in a parameter.

Parameter
Encryption parameters
Decryption parameters
Decrypted images difference rate

6
5
99.59%

2
1
99.62%

2
1
99.59%

12345678
12345677
99.62%

87654321
87654322
99.58%

12345
12344
99.59%

67890
67889
99.60%

1.21000001
1.21000002
99.60%

0.36000001
0.36000002
99.62%

4.5. Statistical Analysis

Statistical analysis can extract the relationships between the original and the encrypted image. Shannon in his theory of information and communication [45] proved that it is possible to break many types of cryptograms by statistical analysis. This can be thwarted by dissipating the redundancy in the structure of the message by diffusion or by increasing the complexity of the relationship between the encrypted message and the secret key by confusion. Either confusion or diffusion is presented in the proposed cryptosystem to frustrate statistical attacks.

4.5.1. Correlation Analysis

Two adjoining pixels in a regular image are strongly correlated in horizontal, vertical, and diagonal positions. Scatter plots in Figures 10 and 11 reveal the correlation of two adjacent pixels in horizontal, vertical, and diagonal distributions in the plain and the cipher image, respectively. Correlation coefficients are calculated for test images by (27) and the results for plain images and cipher images are listed in Table 14. For an ordinary image, the correlation coefficients are very close to 1, which is the highest possible value. The produced encrypted image is ideal and resists statistical attack if the correlation coefficients are very low and close to 0. Consider

(a)

(b)

(c)

Figure 10: Correlation of plain image’s pixels in (a) horizontal, (b) vertical, and (c) diagonal position.

(a)

(b)

(c)

Figure 11: Correlation of cipher image’s pixels in (a) horizontal, (b) vertical, and (c) diagonal position.

4.5.2. Entropy Analysis

Entropy is a statistical parameter that is defined to measure the uncertainly and randomness of a bundle of data. According to Shannon theory, image entropy is the number of bits that is necessary to encode every pixel of the image. The optimal value for entropy of an encrypted image is ~8. This quantity describes the random pattern and texture of pixels in an encrypted image and is calculated by

where is the total number of gray levels (i.e., 256) and is the probability of incidence of intensity in the current image. is the number of pixels with intensity divided by the total number of pixels. The base-2 logarithm will present the calculated entropy in bits. The entropy values for plain images and encrypted images are given in Table 14.

4.6. Differential Analysis

For the purpose of differential attack, an attacker changes a specific pixel in the plain image and traces the differences in the analogous encrypted image to find a meaningful relation. This is also known as a chosen-plaintext attack. A robust encrypted image must be sensitive to minor changes and even changing one bit in the plain image should result in a wide range of changes in the cipher image.

The NPCR measures the number of pixels change rate in an encrypted image when 1 bit is changed in the plain image. This parameter is calculated by (29) and for an ideal encryption algorithm it is 1. Consider

where and are obtained by encrypting two plain images and one random bit dissimilarity.

The UACI in differential analysis is the unified average changing intensity between two encrypted images with a difference in only one bit in corresponding plain images. The UACI can be calculated by (30):

To evaluate the sensitivity of the proposed algorithm to differential attacks, a random bit is changed in the plain image. Encrypting two plain images with a difference in only one bit produces two encrypted images. The rates of pixel and intensity differences in the two encrypted images are calculated. Tables 8, 9, and 10 present calculated UACI and NPCR values for different combinations of , and to trade off the encryption speed and the overall rounds to find a threshold that achieves the highest rate. The following tables are related to the Peppers image. From Tables 8 and 9, it was concluded that increasing the value of the parameter that is related to the pixel permutation rounds does not affect the NPCR and UACI values. These values depend only on and . However, to increase the level of confusion and increase the key space, pixel permutation is required. The results in Table 10 were used to find the minimum values for and that result in the ideal value for NPCR and UACI with the smallest run-time. It was concluded that at least three rounds of were required to obtain the highest values for UACI and NPCR. After the 3rd row, all of the combinations are ideal. Different combinations of , and are calculated with (25) and the combination of that encrypts the image with the smallest run-time is (3, 1, 1).

Table 8: Calculated UACI and NPCR for different combinations of and while in Peppers.

1
2
3
4
5
6
7
8
9
10

1
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

2
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

3
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

4
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

5
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

6
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

7
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

8
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

9
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

10
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

Table 9: Calculated UACI and NPCR for different combinations of and while in Peppers.

1
2
3
4
5
6
7
8
9
10

1
UACI
5.1163
16.2147
33.4573
33.4727
33.4075
33.4355
33.4484
33.5372
33.4775
33.4735

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

2
UACI
5.1163
16.2173
33.4317
33.4902
33.4462
33.4644
33.5382
33.4616
33.5023
33.4503

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

3
UACI
5.1163
16.2191
33.3994
33.5234
33.4093
33.4373
33.4497
33.4928
33.5363
33.5198

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

4
UACI
5.1163
16.2003
33.4263
33.4481
33.3943
33.4181
33.5062
33.4770
33.4784
33.5147

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

5
UACI
5.1163
16.1772
33.4520
33.4995
33.3855
33.4539
33.4610
33.5291
33.4940
33.4577

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

6
UACI
5.1163
16.1845
33.4262
33.5164
33.3721
33.4232
33.4606
33.5161
33.4626
33.4400

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

7
UACI
5.1163
16.1885
33.4141
33.4955
33.4241
33.4615
33.4464
33.5113
33.4486
33.4434

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

8
UACI
5.1163
16.1882
33.4694
33.4696
33.3941
33.4834
33.5019
33.4556
33.4589
33.5000

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

9
UACI
5.1163
16.1954
33.4277
33.4527
33.3872
33.5124
33.4510
33.4750
33.4249
33.5083

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

10
UACI
5.1163
16.2147
33.4573
33.4727
33.4075
33.4355
33.4484
33.5372
33.4775
33.4735

NPCR
81.5414
89.1617
99.6342
99.6208
99.6059
99.6231
99.5930
99.6277
99.5930
99.6174

Table 10: Calculated UACI and NPCR for different combinations of and while in Peppers.

1
2
3
4
5
6
7
8
9
10

1
UACI
5.1163
2.7810
2.0331
1.1860
1.5810
6.1305
1.4349
3.7728
3.3215
0.0564

NPCR
81.5414
44.3218
32.4032
18.9026
25.1965
97.7051
22.8695
60.1284
52.9366
0.8984

2
UACI
16.2119
2.0258
0.9736
0.4904
0.8663
32.6779
0.4922
3.8314
4.1271
0.1998

NPCR
89.1617
85.5148
81.7390
75.0000
76.3233
94.8944
75.3403
86.6261
89.8193
50.9499

3
UACI
33.3879
32.8273
34.7519
32.7804
33.5263
33.5459
33.3332
34.4429
33.5705
33.2778

NPCR
99.6342
99.5850
99.6552
99.5987
99.6357
99.6017
99.6098
99.6044
99.6033
99.5075

4
UACI
33.4882
33.3894
33.4144
33.4373
33.5037
33.5170
33.4163
33.4185
33.4494
33.5380

NPCR
99.6208
99.6124
99.5979
99.5918
99.6075
99.5892
99.6185
99.6075
99.5960
99.5995

5
UACI
33.3994
33.3844
33.5083
33.4886
33.4846
33.5354
33.4447
33.5059
33.3940
33.4656

NPCR
99.6059
99.6124
99.6258
99.6159
99.6033
99.6067
99.6101
99.6166
99.6120
99.5953

6
UACI
33.4314
33.4144
33.4623
33.3797
33.4872
33.5663
33.4599
33.4731
33.4311
33.4141

NPCR
99.6231
99.6296
99.6250
99.6006
99.6208
99.6265
99.6037
99.6040
99.5926
99.6120

7
UACI
33.4744
33.4282
33.4541
33.5446
33.4563
33.4700
33.4543
33.4240
33.4748
33.4333

NPCR
99.5930
99.5968
99.6128
99.6017
99.6082
99.6021
99.6311
99.6132
99.5964
99.6185

8
UACI
33.4535
33.3790
33.5941
33.5118
33.5437
33.4958
33.5057
33.5276
33.4622
33.4553

NPCR
99.6277
99.5972
99.5758
99.6044
99.6094
99.5983
99.6170
99.6136
99.5888
99.5983

9
UACI
33.4703
33.4465
33.4612
33.5285
33.5804
33.4783
33.4213
33.4463
33.4063
33.4409

NPCR
99.5930
99.5975
99.6006
99.6094
99.6185
99.6262
99.5903
99.6052
99.6082
99.6071

10
UACI
33.4631
33.4266
33.3962
33.4113
33.4824
33.5212
33.5256
33.4979
33.3891
33.4482

NPCR
99.6174
99.6109
99.6178
99.6105
99.6212
99.6071
99.5857
99.6014
99.6071
99.5960

In addition to Peppers, the same experiments were performed on Baboon, Figure 12, Fingerprint, Figure 13, Cameraman, Figure 14, and Chess-plate, Figure 15. The results of the experiments on these three images are investigated to determine the strength of the proposed cryptosystem. Tables 11, 12, 13, and 14 list the calculated values of UACI and NPCR for different combinations of and in Baboon, Fingerprint, Cameraman, and Chess-plate images, respectively. Because the results were similar to other combinations of the Pepper image, the other similar tables were discarded and only the set of and was surveyed. The results for entropy, correlation, and brief of the UACI and NPCR are listed in Table 15.

Table 11: Calculated UACI and NPCR for different combinations of and while in Baboon.

1
2
3
4
5
6
7
8
9
10

1
UACI
34.5764
18.5622
1.9700
39.7193
43.0302
12.3125
4.4068
40.1488
48.6447
20.7776

NPCR
68.8828
36.9793
3.9246
79.1283
85.7243
24.5289
8.7791
79.9839
96.9093
41.3929

2
UACI
7.9196
0.9566
0.1960
15.6506
16.3404
0.5035
0.1959
14.2109
32.8500
1.7190

NPCR
87.5687
81.3938
49.9817
93.7302
93.2835
76.1761
49.9420
86.6169
93.3601
67.2352

3
UACI
33.4245
32.9577
32.9665
33.6173
33.4770
33.5504
33.4176
32.8382
33.4229
32.5148

NPCR
99.6040
99.6105
99.6094
99.6319
99.6090
99.6128
99.6162
99.6017
99.5922
99.5224

4
UACI
33.5433
33.4850
33.4328
33.4770
33.4972
33.4388
33.4096
33.4726
33.4217
33.4147

NPCR
99.6140
99.6307
99.6166
99.6136
99.6037
99.6071
99.6059
99.5964
99.6056
99.6235

5
UACI
33.4336
33.4809
33.4824
33.4264
33.4568
33.5143
33.4470
33.3774
33.4297
33.3378

NPCR
99.6151
99.5892
99.6128
99.6189
99.6273
99.5922
99.6407
99.6044
99.5819
99.6120

6
UACI
33.4377
33.4569
33.4803
33.4672
33.5039
33.4647
33.4143
33.5158
33.4548
33.4117

NPCR
99.6021
99.5987
99.6178
99.5975
99.6174
99.6155
99.6090
99.6067
99.6140
99.6143

7
UACI
33.4281
33.4751
33.4826
33.4711
33.4626
33.4911
33.4976
33.4263
33.4891
33.5239

NPCR
99.6052
99.6120
99.5922
99.5892
99.5998
99.6334
99.6040
99.6052
99.6258
99.6120

8
UACI
33.4534
33.5000
33.4770
33.4986
33.4616
33.4514
33.4376
33.4205
33.4672
33.5179

NPCR
99.6265
99.5892
99.5953
99.6082
99.6101
99.6059
99.6014
99.6334
99.6258
99.6128

9
UACI
33.4524
33.5607
33.4740
33.5257
33.3783
33.5049
33.4948
33.4504
33.4197
33.4726

NPCR
99.6223
99.6204
99.6002
99.5934
99.6124
99.5991
99.6025
99.5911
99.5953
99.6094

10
UACI
33.3259
33.4977
33.3791
33.5318
33.5857
33.4664
33.4249
33.4084
33.5136
33.5256

NPCR
99.6155
99.6353
99.6536
99.6338
99.5850
99.5987
99.6536
99.6063
99.6155
99.6353

Table 12: Calculated UACI and NPCR for different combinations of and while in Fingerprint.

1
2
3
4
5
6
7
8
9
10

1
UACI
0.1102
0.2833
0.2429
0.3697
0.3291
0.4672
0.6168
0.1744
0.0714
0.1102

NPCR
14.0533
36.1149
30.9715
47.1390
41.9651
59.5711
78.6449
22.2366
9.0992
14.0533

2
UACI
0.4539
1.0997
1.0502
2.0179
2.0101
4.0957
16.3335
0.4905
0.1923
0.4539

NPCR
71.9505
85.4733
86.7638
85.9703
84.3555
88.2622
89.8018
75.0031
49.0311
71.9505

3
UACI
32.7711
33.2829
23.0805
33.4200
33.3053
33.4104
33.9360
33.4891
34.1633
32.7711

NPCR
99.6140
99.6277
99.3843
99.6178
99.5754
99.6014
99.6368
99.6098
99.7284
99.6140

4
UACI
33.5087
33.4496
33.4820
33.5291
33.3559
33.4629
33.5104
33.4655
33.4892
33.5087

NPCR
99.6105
99.6220
99.6216
99.6178
99.6067
99.6181
99.6120
99.5991
99.6105
99.6105

5
UACI
33.5129
33.4573
33.4886
33.4295
33.5528
33.5534
33.4449
33.4749
33.5176
33.5129

NPCR
99.6117
99.6181
99.5953
99.6269
99.5880
99.6021
99.6094
99.6166
99.5991
99.6117

6
UACI
33.4461
33.4562
33.5025
33.5266
33.4495
33.4262
33.4276
33.4117
33.4122
33.4461

NPCR
99.5983
99.6094
99.6044
99.5956
99.5930
99.6132
99.6391
99.6204
99.5926
99.5983

7
UACI
33.3924
33.5282
33.4733
33.4923
33.5048
33.4279
33.4765
33.5338
33.4224
33.3924

NPCR
99.6265
99.6075
99.6014
99.6273
99.6212
99.6075
99.5922
99.6101
99.6155
99.6265

8
UACI
33.4546
33.4874
33.5073
33.4015
33.4098
33.4831
33.3897
33.4127
33.5300
33.4546

NPCR
99.6063
99.6025
99.6132
99.6159
99.6147
99.6292
99.5827
99.6315
99.5949
99.6063

9
UACI
33.5202
33.4528
33.5220
33.4781
33.4966
33.4986
33.4642
33.4896
33.5301
33.5202

NPCR
99.6223
99.6201
99.5995
99.6304
99.5895
99.6002
99.6078
99.5880
99.5781
99.6223

10
UACI
33.3561
33.4246
33.4130
33.4110
33.3838
33.4363
33.5498
33.4906
33.4433
33.3561

NPCR
99.6170
99.5872
99.5777
99.5884
99.6170
99.6048
99.5926
99.6140
99.5987
99.6170

Table 13: Calculated UACI and NPCR for different combinations of and while for Cameraman.

1
2
3
4
5
6
7
8
9
10

1
UACI
7.4904
2.6620
5.1333
11.4982
3.7695
5.6794
5.3902
11.9543
8.4184
7.4904

NPCR
29.8447
10.6064
20.4529
45.8130
15.0192
22.6288
21.4767
47.6303
33.5419
29.8447

2
UACI
1.0214
0.1943
0.4910
1.9258
0.3592
0.5052
0.4911
1.8747
1.0055
1.0214

NPCR
83.5632
49.5514
75.0793
86.7004
63.8062
76.2024
75.0595
84.4025
81.1432
83.5632

3
UACI
33.7662
29.9298
32.1345
32.5679
33.5877
33.5277
33.6352
34.8690
33.6583
33.7662

NPCR
99.6262
99.5071
99.5102
99.5285
99.5224
99.5850
99.6384
99.6552
99.6521
99.6262

4
UACI
33.4563
33.3303
33.4678
33.5979
33.4952
33.5201
33.4910
33.2905
33.3876
33.4563

NPCR
99.6292
99.5453
99.5972
99.6582
99.5972
99.6170
99.5682
99.5743
99.6475
99.6292

5
UACI
33.2851
33.4706
33.3841
33.5766
33.3711
33.4932
33.3911
33.4998
33.4473
33.2851

NPCR
99.6155
99.6262
99.5834
99.5972
99.6109
99.5956
99.5956
99.6368
99.6201
99.6155

6
UACI
33.4854
33.3612
33.6393
33.4269
33.3583
33.6404
33.4790
33.3590
33.2436
33.4854

NPCR
99.6567
99.5911
99.5682
99.6078
99.6277
99.5880
99.6277
99.6124
99.6414
99.6567

7
UACI
33.4159
33.4013
33.5464
33.5039
33.4404
33.4768
33.4303
33.3951
33.2653
33.4159

NPCR
99.6674
99.5850
99.6094
99.6262
99.5880
99.6140
99.5941
99.5850
99.6002
99.6674

8
UACI
33.4321
33.4036
33.6195
33.4753
33.3993
33.5394
33.4112
33.5314
33.4387
33.4321

NPCR
99.5621
99.5926
99.6368
99.6277
99.5895
99.5941
99.5819
99.5987
99.6094
99.5621

9
UACI
33.4651
33.3438
33.4917
33.4837
33.4551
33.4780
33.4704
33.4806
33.4516
33.4651

NPCR
99.6140
99.6323
99.5636
99.6216
99.6429
99.6109
99.6338
99.5758
99.6002
99.6140

10
UACI
33.6242
33.5821
33.2618
33.4312
33.5802
33.6133
33.3934
33.6385
33.5515
33.6242

NPCR
99.5972
99.5895
99.6078
99.5773
99.6033
99.5911
99.6323
99.6109
99.5850
99.5972

Table 14: Calculated UACI and NPCR for different combinations of and while for Chess-plate.

1
2
3
4
5
6
7
8
9
10

1
UACI
49.7511
38.9683
0.6828
0.3194
17.8328
9.1234
1.8446
0.6682
24.8476
45.3508

NPCR
99.1135
77.6321
2.7206
81.4575
71.0526
72.7020
14.6988
85.1974
99.0021
90.3473

2
UACI
30.8111
26.3993
34.1747
33.5065
33.3622
33.4134
33.0488
33.5821
33.5865
33.5562

NPCR
72.5723
90.6128
99.3164
99.3210
99.5193
99.5987
99.4949
99.6674
99.5697
99.6399

3
UACI
33.6698
33.2530
33.4078
33.4257
33.5033
33.4428
33.4594
33.5224
33.3517
33.4282

NPCR
99.5483
99.6460
99.5850
99.5682
99.6063
99.6033
99.5819
99.6338
99.5972
99.6002

4
UACI
33.4568
33.4550
33.4542
33.4903
33.5600
33.3137
33.4928
33.5251
33.4225
33.3196

NPCR
99.6185
99.6201
99.6262
99.5819
99.5850
99.5972
99.5895
99.6216
99.5956
99.6063

5
UACI
33.4745
33.5885
33.5112
33.5035
33.4013
33.4363
33.4302
33.5542
33.4494
33.4572

NPCR
99.6155
99.6414
99.6292
99.6262
99.5850
99.5956
99.6063
99.6262
99.6109
99.6002

6
UACI
33.4729
33.4588
33.3505
33.5044
33.5310
33.5108
33.3838
33.6189
33.3733
33.4620

NPCR
99.6277
99.5728
99.6292
99.5987
99.6048
99.6689
99.5865
99.6017
99.5941
99.6109

7
UACI
33.4701
33.4639
33.3825
33.5426
33.4590
33.4672
33.3788
33.4522
33.4046
33.4853

NPCR
99.5926
99.6246
99.5758
99.6368
99.5941
99.6078
99.6201
99.6399
99.6048
99.5682

8
UACI
33.4681
33.4529
33.3931
33.3834
33.6822
33.4259
33.3729
33.3856
33.4834
33.4206

NPCR
99.5926
99.6811
99.6063
99.5941
99.5972
99.6002
99.6643
99.6368
99.5987
99.6155

9
UACI
33.3928
33.5164
33.5825
33.4455
33.5033
33.4134
33.4258
33.5132
33.5198
33.6166

NPCR
99.5956
99.6231
99.6078
99.6216
99.6262
99.6094
99.6033
99.6094
99.6216
99.6292

10
UACI
33.2732
33.5652
33.5405
33.6319
33.3404
33.4587
33.3828
33.4207
33.4494
33.5986

NPCR
99.5911
99.5895
99.6185
99.6002
99.6078
99.6063
99.6231
99.6216
99.5590
99.6536

Table 15: Results of security analysis.

Image name

Plain entropy
Cipher entropy
Plain image correlations
Cipher image correlations
UACI
NPCR

HC
VC
DC
HC
VC
DC

Cameraman

256 × 256
1
1
1
7.0097
7.9969
0.8390
0.7189
0.6973

3
1
1
7.9976

1
3
1
7.9971

1
1
3
7.9972

Chess-plate

256 × 256
1
1
1
1
7.9970
0.9775
0.9800
0.9637

3
1
1
7.9974

1
3
1
7.9972

1
1
3
7.9973

Baboon

512 × 512
1
1
1
7.3579
7.9993
0.8644
0.7587
0.7261

3
1
1
7.9993

1
3
1
7.9993

1
1
3
7.9993

Peppers

512 × 512
1
1
1
7.5714
7.9993
0.8642
0.7587
0.7261

3
1
1
7.9993

1
3
1
7.9993

1
1
3
7.9993

Fingerprint

512 × 512
1
1
1
6.7279
7.9993
0.8644
0.7587
0.7261

3
1
1
7.9992

1
3
1
7.9994

1
1
3
7.9993

(a)

(b)

(c)

(d)

Figure 12: (a) Baboon image and (b) its histograms and (c) encrypted image and (d) its histogram.

(a)

(b)

(c)

(d)

Figure 13: (a) Fingerprint image and (b) its histograms and (c) encrypted image and (d) its histogram.

(a)

(b)

(c)

(d)

Figure 14: (a) Cameraman image and (b) its histograms and (c) encrypted image and (d) its histogram.

(a)

(b)

(c)

(d)

Figure 15: (a) Chess-plate image and (b) its histograms and (c) encrypted image and (d) its histogram.

5. Conclusion and Future Works

In this paper, a new chaos-based cryptosystem has been proposed for encrypting images. The Arnold cat map and the Henon map are two discrete chaotic maps that are used in this scheme. Bit shuffling and pixel shuffling are reversible transformations that are performed using the Arnold cat map with various secret parameters. Improving the randomness of transformation and the efficiency of bit permutation are two advantages of this cryptosystem that increases the strength of the ciphered image in comparison with previous works. Iterating the Arnold cat map with different parameters at each round prevents undesirable reconstruction of the input image. These parameters are generated by the Henon map with secret initial values. The points generated by the Henon map are also applied to create secret images for more confusion and diffusion and to increase the key space. Sequential XOR of the bit-permuted plain image and the pixel-permuted secret image is another phase of modifying the pixels values. This creates a slight distortion in the plain image to prevent successful differential attacks. The results of security analysis of five images demonstrate the resistance of the encrypted image to statistical attacks and to the chosen-plaintext attack. In addition, a sufficiently large key space makes a brute force attack impractical. As the future work, the proposed cryptosystem in this paper will combine with a public key technique such as ECC or RSA to propose a hybrid encryption method. This technique is a chaotic asymmetric cryptosystem.

Conflict of Interests

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

Acknowledgments

The authors wish to thank Universiti Kebangsaan Malaysia (UKM) and Ministry of Higher Education Malaysia for supporting this work by research Grants FRGS/1/2012/SG05/UKM/02/1 and ERGS/1/2012/STG07/UKM/02/9.

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