Hopfield attractor-trusted neural network: an attack-resistant image encryption

Lakshmi, C.; Thenmozhi, K.; Rayappan, John Bosco Balaguru; Amirtharajan, Rengarajan

doi:10.1007/s00521-019-04637-4

Cited by 61 publications

(27 citation statements)

References 27 publications

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“…And compared with Ref. [34], [35], [36] in Table V, our algorithm has better information entropy, which means that the encrypted image is very messy and it is difficult for an attacker to obtain useful information from it. Therefore, encrypted image has good security.…”

Section: F Information Entropymentioning

confidence: 98%

A New Image Encryption Scheme Based on a Novel One-Dimensional Chaotic System

Wang

Liu

2020

IEEE Access

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This paper introduces a novel one-dimensional sine chaotic system (1DSCS) with large parameter interval. The evaluation of 1DSCS indicates that the system has good chaotic characteristics and large parameters space. Based on 1DSCS, a new image encryption scheme is proposed. First, the image is scrambled by the row and column indexes, and then scrambled by the dynamic parameters Arnold map. This scheme avoids the inadequacy of row-column index scrambling and the periodicity of Arnold map. Second, four highly sensitive dynamic diffusion formulas related to plaintext are designed, and the dynamic formulas are selected through the chaotic sequence generated by 1DSCS. Third, the scheme proposed in this paper can also be applied to color image encryption. The experimental results demonstrate that the 1DSCS system is suitable for image encryption and the encryption scheme has good security to resist common attacks.

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Section: F Information Entropymentioning

confidence: 98%

A New Image Encryption Scheme Based on a Novel One-Dimensional Chaotic System

Wang

Liu

2020

IEEE Access

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“…New researches are developed to propose modern encryption algorithms. A chaotic system is a major tool in this prominent research domain due to ergodicity, deterministic dynamics, unpredictable behaviors, nonlinear transformation, and sensitivity dependence of the system [ 49 – 52 ]. For instance, Gao et al [ 49 ] designed an encryption algorithm based on Chen hyperchaotic system.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results show a high security algorithm. Lakshmi et al [ 52 ] design an encryption algorithm based on HNN. The technics uses a simple diffusion–confusion algorithm, and security against some existing attacks is achieved.…”

Section: Introductionmentioning

confidence: 99%

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Window of multistability and its control in a simple 3D Hopfield neural network: application to biomedical image encryption

Njitacke

Doubla

Tsafack

et al. 2020

Neural Comput & Applic

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In this contribution, the problem of multistability control in a simple model of 3D HNNs as well as its application to biomedical image encryption is addressed. The space magnetization is justified by the coexistence of up to six disconnected attractors including both chaotic and periodic. The linear augmentation method is successfully applied to control the multistable HNNs into a monostable network. The control of the coexisting four attractors including a pair of chaotic attractors and a pair of periodic attractors is made through three crises that enable the chaotic attractors to be metamorphosed in a monostable periodic attractor. Also, the control of six coexisting attractors (with two pairs of chaotic attractors and a pair of periodic one) is made through five crises enabling all the chaotic attractors to be metamorphosed in a monostable periodic attractor. Note that this controlled HNN is obtained for higher values of the coupling strength. These interesting results are obtained using nonlinear analysis tools such as the phase portraits, bifurcations diagrams, graph of maximum Lyapunov exponent, and basins of attraction. The obtained results have been perfectly supported using the PSPICE simulation environment. Finally, a simple encryption scheme is designed jointly using the sequences of the proposed HNNs and the sequences of real/imaginary values of the Julia fractals set. The obtained cryptosystem is validated using some well-known metrics. The proposed method achieved entropy of 7.9992, NPCR of 99.6299, and encryption time of 0.21 for the 256*256 sample 1 image.

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“…A lot of researches have been done on one-dimensional chaotic map, like Logistic chaotic map, Tent chaotic map, two-dimensional chaotic map, like Cat chaotic map, three-dimensional chaotic map, like Lorenz chaotic map, and spatiotemporal chaotic system, like Logistic-Dynamics Coupled Map Lattices (LDCML) spatiotemporal chaotic system [10], all indicate that cryptography based on chaos theory has become a popular research direction for researchers [11][12][13][14]. With the deepening of scholars' research, many chaotic systems and image encryption algorithms based on chaos theory have been proposed and achieved good results [15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Chaotic Image Encryption Algorithm Based on Dynamic Spiral Scrambling Transform and Deoxyribonucleic Acid Encoding Operation

Wang

Chen

2020

IEEE Access

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This paper proposes a dynamic spiral scrambling algorithm, which is combined with the random pixel value filling operation, Deoxyribonucleic Acid (DNA) operation and used in image encryption. The random pixel value filling operation is to generate some random values and fill them around the plainimage. These values can influence all the pixels after the confusion and diffusion operations. The proposed dynamic spiral scrambling algorithm dynamically combines the chaotic sequences with the plaintext image, and scrambles the pixel values of the plaintext image, this operation makes the scrambling algorithm not fixed, and position changes are highly sensitive to the chaotic sequences, any slight change will get a completely different result. Then, this paper combines Deoxyribonucleic Acid (DNA) coding and manipulation to further confuse pixel values. Finally, the average entropy is 7.9974, the average NPCR is 99.6067 and the average UACI is 33.4544, experimental results and various security analyses show that the algorithm has good encryption effects and can resist common attacks such as select plaintext attacks, cropping, and noise attacks. INDEX TERMS chaotic image encryption, chaotic system, Deoxyribonucleic acid coding, dynamic spiral scrambling transform

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Hopfield attractor-trusted neural network: an attack-resistant image encryption

Cited by 61 publications

References 27 publications

A New Image Encryption Scheme Based on a Novel One-Dimensional Chaotic System

A New Image Encryption Scheme Based on a Novel One-Dimensional Chaotic System

Window of multistability and its control in a simple 3D Hopfield neural network: application to biomedical image encryption

Chaotic Image Encryption Algorithm Based on Dynamic Spiral Scrambling Transform and Deoxyribonucleic Acid Encoding Operation

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