Cartoon Image Encryption Algorithm by a Fractional-Order Memristive

Based on the Adomian decomposition method and Lyapunov stability theory, this paper constructs a fractional-order memristive hyperchaos. ,en, the 0–1 test analysis is applied to detect random nature of chaotic sequences exhibited by the fractional-order systems. Comparing with the corresponding integer-order hyperchaotic system, the fractional-order hyperchaos possesses higher complexity. Finally, an image encryption algorithm is proposed based on the fractional-order memristive hyperchaos. Security and performance analysis indicates that the proposed chaos-based image encryption algorithm is highly resistant to statistical attacks.


Introduction
With the rapid development of Internet and information technology, multimedia become an important mode of interaction.e widespread use of digital image, making the security problem of image data become more and more prominent, the research on digital image security is of great theoretical value and practical significance.Chaotic systems are extremely sensitive to initial conditions, the orbital unpredictability, and the internal randomness [1][2][3].
erefore, it becomes one of the key research objects of modern cryptography to introduce chaos theory into the field of image security [4][5][6].
Memristor is a kind of resistance and in possession of the function of memory.Since 2008, memristor has obtained people's close concern, which played an active promoting role in the development of circuit and system theory [7,8].Chaotic systems designed by memristor have complex dynamic characters, and thus they have wide application prospect in secure communication and image encryption [9][10][11][12][13][14]. e rest of this paper is organized as follows: the fractional-order memristive hyperchaotic system is presented in Section 2. e memristive hyperchaotic system shows complicate nonlinear dynamics behavior, and therefore it is difficult to predict trajectories.e proposed image encryption algorithm is described in Section 3. Block permutation and diffusion strategy of plain text processing are used in order to enhance the security of the encryption algorithm.In Section 4, different kinds of images are encrypted as examples by using the designed algorithm, and numerical experiment results are investigated.It is robust against chosen/known plain image attack with just one permutation-diffusion round.Finally, the conclusions are drawn in Section 5.

Memristive Hyperchaos
Memristive chaos circuit can create a new signal generator, and therefore, it becomes a new research field.A memristive hyperchaos [15] is proposed as follows: where k is the coefficient related with memristor , and x 4 are the state variables, a, b, c, d, and f are the intrinsic parameters, and q i (i � 1, 2, 3, 4) are the Caputo derivatives of fractional order.It is interesting that system (1) has an infinite set of equilibria, including infinite stable equilibria and unstable equilibria, so it shows more plentiful dynamic behaviors.e Adomian decomposition algorithm needs less computation time as well as memory resources, but it yields more accurate results.When solving the integerorder differential system, it is even more accurate than the Runge-Kutta algorithm.
en, the "0-1 test" of fractional-order memristive hyperchaos is conducted by the translation components (p, s) shown in Figure 2, where p and s are determined by observation data, which means that they are independent of original data and system dimension.Considering sequence x 1 (t) obtained from fractional-order memristive hyperchaos, numerical simulation displays unbounded trajectory similar to Brownian motion, which shows that the fractional-order memristive hyperchaos has rich dynamic characteristics.e 0-1 test provides a theoretical and experimental basis for application of fractional-order hyperchaos in information security and secure communication.

Proposed Image Encryption
is section presents the image encryption scheme in the framework of symmetric key cipher architecture.For simplicity, we employ gray-scale images with the size of L � M × N. Based on fractional-order memristive hyperchaos, the proposed encryption algorithm is used for gray image, color image, and binary image.Our algorithm is based on fully layered encryption technique to provide better security.We perform color transformation to separate the RGB color in its component matrix.
e fractional-order memristive chaotic system is converted into four one-dimensional chaotic sequences.To eliminate some harmful effect of chaos transient process, we make one thousand times interaction in advance.It is necessary to make the following pretreatment for chaotic sequences: Suppose that the positive integer m � 12.As shown in Figure 3, a vast majority of sequence K 1 fluctuate around zero and locate in the interval [−0.003, 0.003], which indicates that the sequence has a high degree of randomness.
Divide plain image P into sections A j (j � 1, 2, 3, 4).Pixel value substitution is made according to the following encryption equation: where C j (0) � 0. en, the encrypted sequence C j (j � 1, 2, 3, 4) is translated into (M/2) × (N/2) two-dimensional matrix, and the encrypted image is . Decryption is the inverse process of encryption.

Simulation Results and Security Analysis
An encryption algorithm should be robust against any statistical attacks.In this section, the results and security of encryption performance are evaluated by the histogram, the information entropy, and the correlation of two adjacent pixels in the encrypted.

Histogram Analysis.
Histogram analysis of plain image and encrypted image has been performed to validate the algorithm.When some data of plain image or histogram are captured, the statistical attack will be very effective and highly performed.

Correlation Coe cient Analysis.
In simulation, we randomly select four thousand pixel pairs of horizontal, vertical, diagonal, and counterdiagonal adjacent positions in each direction from plain images and their corresponding cipher images, respectively.To test correlation coe cients of adjacent pixels between the plain images and encrypted images, correlation coe cients are determined by the following formula: where For the gray Mickey image, distributions of four directions are shown with di erent markers in Figure 7.

4
Journal of Electrical and Computer Engineering e numeric representations of correlation coefficients are calculated and listed in Table 1.
e correlation coefficients in plain images are all greater than 0.9, but they are smaller than 0.05 in corresponding encrypted images.Table 1 indicates that the performance of the proposed algorithm is better.erefore, the proposed encryption algorithm dramatically randomized the pixels.e conception of "information entropy" is proposed by Shannon.Information entropy is expressed by H(x) � − n i�1 p(x i ) log 2 p(x i ), where p(x i ) represents probability of symbol x i .In addition, 0 ≤ p(x i ) ≤ 1 (i � 0, 1, 2, . . ., n) and  n i�1 p(x i ) � 1. Information entropy is the whole average uncertainty and statistical properties of overall information sources.Suppose that the source emits n � 2 8 symbols with equal probability, we obtain the largest entropy H max � log 2 n � 8. Information entropies of various encrypted images are listed in Table 1, which are close to the theoretical value 8. is means that information leakage in the encryption process is negligible, and the encryption system is secure against entropy attack.

Conclusion
In this study, the Adomian decomposition method is adopted to solve fractional-order memristive hyperchaos.Rich dynamic characteristics of the conformable fractionalorder hyperchaotic system are shown by using the 0-1 test.A novel scheme for image encryption of digital images is proposed based on the hyperchaotic system, and it has been validated for color image, gray image, binary image, and so on.Numerical simulations demonstrate that the designed scheme not only maintains the larger secret space but also has better cryptographic performances.e algorithm is analyzed by encrypted image, histogram, correlation coefficients, and so on, therefore effectively Figures 4(b), 5(b), and 6(b) illustrate the histograms of cartoon images "Snow White" (Figure 4(a)), "Mickey Mouse" (Figure 5(a)), and "Binary image" (Figure 6(a)).Histograms of their encrypted images are flat as shown in Figures 4(f )-4(h), 5(d), and 6(d), respectively.It is noticeable that the designed algorithm results in uniform distributions of cipher images, which can resist cipher-only attack.

Figure 4 :
Figure 4: Histogram of color Snow White image and its encrypted image: (a) plain image; (b) histogram of red component of plain image; (c) histogram of green component of plain image; (d) histogram of blue component of plain image; (e) encrypted image; (f) histogram of red component of encrypted image; (g) histogram of green component of encrypted image; (h) histogram of blue component of encrypted image.

Figure 5 :
Figure 5: Histogram of triangle gray Mickey Mouse image and its encrypted image: (a) Mickey Mouse image; (b) histogram of (a); (c) encrypted image; (d) histogram of (c).

Figure 7 :
Figure 7: Correlation coefficients of plain and encrypted rectangle image Snow White.

Table 1 :
Correlation coefficients of plain image and encrypted image.Journal of Electrical and Computer Engineering ensuring a secure image communication.Effectiveness of the proposed algorithm is fully evaluated by numerical experiments of histogram, correlation coefficients, and information entropy.