Synchronization for arrays of coupled jumping delayed neural networks and its application to image encryption

Zhang, Xiaomei; Sheng, Suying; Li, Guoping; Zheng, Yufan

doi:10.1109/cdc.2017.8263821

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…Recently, the study of coupled neural networks (CNNs) has attracted increasing attention from researchers in various fields because of their applications in science and engineering, such as brain science, optimisation, image encryption, secure communication etc. Up to now, some interesting works have been reported regarding the dynamical behaviours of CNNs [1–5]. Cao et al .…”

Section: Introductionmentioning

confidence: 99%

Dissipativity of Markovian multiple‐weighted coupled neural networks with dynamic event‐triggered pinning control

Chen

Zheng

2020

IET Control Theory & Appl

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Section: Introductionmentioning

confidence: 99%

Dissipativity of Markovian multiple‐weighted coupled neural networks with dynamic event‐triggered pinning control

Chen

Zheng

2020

IET Control Theory & Appl

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“…The types of neural networks involved in synchronization research are also various. These include arrays of coupled jumping delayed neural networks [247], reaction-diffusion neural networks with mixed delays [248], and chaotic memristive multidirectional associative memory neural networks [249].…”

Section: Synchronizationmentioning

confidence: 99%

Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap

Zolfaghari

Koshiba

2022

ASI

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Recently, many researchers have been interested in the application of chaos in cryptography. Specifically, numerous research works have been focusing on chaotic image encryption. A comprehensive survey can highlight existing trends and shed light on less-studied topics in the area of chaotic image encryption. In addition to such a survey, this paper studies the main challenges in this field, establishes an ecosystem for chaotic image encryption, and develops a future roadmap for further research in this area.

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“…Papers have also tested different reaction-diffusion technique of Lyapunov time-dependent impulses within NNs to see its applicability to encrypting images [154]. Synchronization for arrays in a network system can also be achieved by using master-slave synchronization of a delayed NN [155]. The use of memristor-based models and its chaotic properties have also been studied in regards to its image encryption capabilities [156].…”

Section: -Neural Physically Unclonable Function (Puf)mentioning

confidence: 99%

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.

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Synchronization for arrays of coupled jumping delayed neural networks and its application to image encryption

Cited by 11 publications

References 19 publications

Dissipativity of Markovian multiple‐weighted coupled neural networks with dynamic event‐triggered pinning control

Dissipativity of Markovian multiple‐weighted coupled neural networks with dynamic event‐triggered pinning control

Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap

The Dichotomy of Neural Networks and Cryptography: War and Peace

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