On the Security Analysis of a Hopfield Chaotic Neural Network-Based Image Encryption Algorithm

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.

Section: Introductionmentioning

confidence: 99%

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

Xie

Zhang

2022

“…Since then, chaos has been widely used in image encryption [ 2 , 3 , 4 , 5 ]. So far, many research results exist that are based on chaotic image encryption, such as the Hopfield chaotic neural network [ 6 , 7 , 8 ], which is a typical dynamic neural network with rich dynamic properties; however, the self-feedback Hopfield network used to generate chaotic phenomena is complex in its structure, computationally intensive with fixed parameters, DNA encryption [ 9 , 10 , 11 , 12 ], DNA computation with huge parallelism, and has huge storage and ultra-low power consumption. The compressed sensing (CS) [ 13 , 14 , 15 , 16 ] compression feature allows multimedia encryption schemes with a much reduced length of ciphertext, and simple linear measurements make the encryption process very efficient.…”

Section: Introductionmentioning

confidence: 99%

Plaintext-Related Dynamic Key Chaotic Image Encryption Algorithm

Ping

Sun

et al. 2021

To address the problems of the high complexity and low security of the existing image encryption algorithms, this paper proposes a dynamic key chaotic image encryption algorithm with low complexity and high security associated with plaintext. Firstly, the RGB components of the color image are read, and the RGB components are normalized to obtain the key that is closely related to the plaintext, and then the Arnold transform is used to stretch and fold the RGB components of the color image to change the position of the pixel points in space, so as to destroy the correlation between the adjacent pixel points of the image. Next, the generated sequences are independently encrypted with the Arnold-transformed RGB matrix. Finally, the three encrypted images are combined to obtain the final encrypted image. Since the key acquisition of this encryption algorithm is related to the plaintext, it is possible to achieve one key per image, so the key acquisition is dynamic. This encryption algorithm introduces chaotic mapping, so that the key space size is 10180. The key acquisition is closely related to the plaintext, which makes the ciphertext more random and resistant to differential attacks, and ensures that the ciphertext is more secure after encryption. The experiments show that the algorithm can encrypt the image effectively and can resist attack on the encrypted image.

“…It was also proved to have good chaotic dynamic behavior [ 16 ]. Therefore, the self-feedbacked Hopfield network has been widely used in optimization problems and image encryption [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. However, the self-feedbacked Hopfield network still has some interesting properties to be discovered.…”

Section: Introductionmentioning

confidence: 99%

“…Self-feedbacked Hopfield networks that were used to generate chaos phenomena have complex structures, a large computational amount and fixed parameters [ 16 , 18 , 19 , 20 , 21 , 22 ]. Due to these properties, self-feedbacked Hopfield networks need to be combined with other chaotic maps [ 18 , 19 , 20 , 21 ], which have consequently limited the application. On the contrary, the structure and calculation of single neuron are simplified, and the single neuron can present chaos phenomenon as its parameters vary in continuous range.…”

Section: Introductionmentioning

confidence: 99%

Single Neuronal Dynamical System in Self-Feedbacked Hopfield Networks and Its Application in Image Encryption

Chen

2021

Image encryption is a confidential strategy to keep the information in digital images from being leaked. Due to excellent chaotic dynamic behavior, self-feedbacked Hopfield networks have been used to design image ciphers. However, Self-feedbacked Hopfield networks have complex structures, large computational amount and fixed parameters; these properties limit the application of them. In this paper, a single neuronal dynamical system in self-feedbacked Hopfield network is unveiled. The discrete form of single neuronal dynamical system is derived from a self-feedbacked Hopfield network. Chaotic performance evaluation indicates that the system has good complexity, high sensitivity, and a large chaotic parameter range. The system is also incorporated into a framework to improve its chaotic performance. The result shows the system is well adapted to this type of framework, which means that there is a lot of room for improvement in the system. To investigate its applications in image encryption, an image encryption scheme is then designed. Simulation results and security analysis indicate that the proposed scheme is highly resistant to various attacks and competitive with some exiting schemes.