An image encryption scheme based on finite-time cluster synchronization of two-layer complex dynamic networks

Li, Xiaohui; Zhou, Lili; Tan, Fei

doi:10.1007/s00500-021-06500-y

Cited by 50 publications

(13 citation statements)

References 42 publications

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“…By analyzing Table 6 , it can be obtained that, as a whole, the proposed algorithm has smaller correlation coefficients compared with Refs. [ 33 , 34 , 35 , 36 , 37 ].…”

Section: Security Analysesmentioning

confidence: 99%

A Color Image Encryption Algorithm Based on Double Fractional Order Chaotic Neural Network and Convolution Operation

Xie

Zhang

2022

Entropy

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A color image encryption algorithm based on double fractional order chaotic neural network (CNN), interlaced dynamic deoxyribonucleic acid (DNA) encoding and decoding, zigzag confusion, bidirectional bit-level diffusion and convolution operation is proposed. Firstly, two fractional order chaotic neural networks (CNNs) are proposed to explore the application of fractional order CNN in image encryption. Meanwhile, spectral entropy (SE) algorithm shows that the sequence generated by the proposed fractional order CNNs has better randomness. Secondly, a DNA encoding and decoding encryption scheme with evolutionary characteristics is adopted. In addition, convolution operation is utilized to improve the key sensitivity. Finally, simulation results and security analysis illustrate that the proposed algorithm has high security performance and can withstand classical cryptanalysis attacks.

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“…By analyzing Table 6 , it can be obtained that, as a whole, the proposed algorithm has smaller correlation coefficients compared with Refs. [ 33 , 34 , 35 , 36 , 37 ].…”

Section: Security Analysesmentioning

confidence: 99%

A Color Image Encryption Algorithm Based on Double Fractional Order Chaotic Neural Network and Convolution Operation

Xie

Zhang

2022

Entropy

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“…This makes the actual key space of IEALM can not reach that estimated by the designers. Strictly speaking, the key space of IEALM is only 2 2L • (256MN) 3 . As for a plain-image of size 2048 × 2048, the sizes of key space of IEALM implemented with precision 32-bit and 64-bit are 10 46 and 10 65 , respectively.…”

Section: Analysis Of the Key Spacementioning

confidence: 99%

“…Due to the special characteristics of image data, such as the large amount of data itself and strong correlation existing between neighbouring pixels, it is generally impractical to encrypt the protected object with the classic text encryption algorithms by converting the object into one-dimensional text data. Under this background, a large number of image encryption algorithms were proposed in the past three decades [2,3,4]. Unfortunately, some were found insecure of different extents from the perspective of modern cryptanalysis [5,6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Cryptanalyzing an Image Encryption Algorithm Underpinned by 2D Lag-Complex Logistic Map

Li¹,

Shen²,

Liu³

2022

Preprint

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This paper analyzes security performance of an image encryption algorithm using 2D lag-complex Logistic map (LCLM), which adopts it as a pseudo-random number generator, and uses the sum of all pixel values of the plain-image as its initial value to control the random combination of the basic encryption operations. However, multiple factors make the final pseudorandom sequences controlling the encryption process may be the same for different plain-images. Based on this point, we proposed a chosen-plaintext attack by attacking the six encryption steps with a strategy of divide and conquer. Using the pitfalls of 2D-LCLM, the number of required chosen plain-images is further reduced to 5 • log 2 (MN) + 95, where MN is the number of pixels of the plain-image.

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“…In 1990, the pioneer workers Pecora and Carroll first proposed the concept of chaos synchronization and solved the synchronization problem of two identical chaotic systems under different initial conditions [3]. Since then, chaos synchronization control has become a very active research topic [4][5][6][7][8][9][10][11][12][13][14][15][16], for example, projective synchronization control and hybrid function projective synchronization control. Many control methods have been used to synchronize different chaotic systems, such as feedback linearization method [17], optimal control [18], and neural network control [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A new adaptive iterative learning control of finite-time hybrid function projective synchronization for unknown time-varying chaotic systems

et al. 2023

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A new adaptive iterative learning control (AILC) scheme is proposed to solve the finite-time hybrid function projective synchronization (HFPS) problem of chaotic systems with unknown periodic time-varying parameters. Fourier series expansion (FSE) is introduced to deal with the problem of uncertain time-varying parameters. The bound of the expanded remaining items is unknown. A typical convergent series is used to deal with the unknown bound in the design process of the controller. The adaptive iterative learning synchronization controller and parameter update laws are designed. Two different chaotic systems are synchronized asymptotically according to different proportional functions on a finite time interval by Lyapunov stability analysis. The simulation example proves the feasibility and effectiveness of the proposed method.

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An image encryption scheme based on finite-time cluster synchronization of two-layer complex dynamic networks

Cited by 50 publications

References 42 publications

A Color Image Encryption Algorithm Based on Double Fractional Order Chaotic Neural Network and Convolution Operation

A Color Image Encryption Algorithm Based on Double Fractional Order Chaotic Neural Network and Convolution Operation

Cryptanalyzing an Image Encryption Algorithm Underpinned by 2D Lag-Complex Logistic Map

A new adaptive iterative learning control of finite-time hybrid function projective synchronization for unknown time-varying chaotic systems

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