Memristor-Based Chaotic Circuit for Text/Image Encryption and Decryption

Yang, Chenyu; Hu, Qingqing; Yu, Yongbin; Zhang, Rongquan; Yao, Yuanzhe; Cai, Jingye

doi:10.1109/iscid.2015.156

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…Electrical simplicity of the ITO|PbI 2 |Cu memristors and the input–output compatibility create the possibility of facile concatenation of elements and thus construction of devices with much higher complexity. , These assemblies can be used either as more complex neuromimetic nodes or a platform for synthesis of logic functions . Future development of these devices may encompass both large integration scale leading toward computing systems with distributed memory , and discrete devices with increased functionality, for example, in the field of data security. − Our system offers learning phenomena at several levels. First, the PbI 2 -based memristor has a nonvolatile memory embodied as LRS/HRS.…”

Section: Discussionmentioning

confidence: 99%

Memristor in a Reservoir System—Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI₂

Wlaźlak

Marzec

Zawal

et al. 2019

ACS Appl. Mater. Interfaces

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Lead halides in an asymmetric layered structure form memristive devices which are controlled by the electronic structure of the PbX2|metal interface. In this paper, we explain the mechanism that stands behind the I–V pinched hysteresis loop of the device and shortly present its synaptic-like plasticity (spike-timing-dependent plasticity and spike-rate-dependent plasticity) and nonvolatile memory effects. This memristive element was incorporated into a reservoir system, in particular, the echo-state network with delayed feedback, which exhibits brain-like recurrent behavior and demonstrates metaplasticity as one of the available learning mechanisms. It can serve as a classification system that classifies input signals according to their amplitude.

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Section: Discussionmentioning

confidence: 99%

Memristor in a Reservoir System—Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI₂

Wlaźlak

Marzec

Zawal

et al. 2019

ACS Appl. Mater. Interfaces

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“…For example, in [21,22] new functions are implemented to generate chaotic systems. In 2016 and 2017 new circuits were proposed for encryption and decryption of images and texts [23].…”

Section: Related Workmentioning

confidence: 99%

Random Number Generator with Long-Range Dependence and Multifractal Behavior Based on Memristor

et al. 2020

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Random number generators are used in areas such as encryption and system modeling, where some of these exhibit fractal behaviors. For this reason, it is interesting to make use of the memristor characteristics for the random number generation. Accordingly, the objective of this article is to evaluate the performance of a chaotic memristive system as a random number generator with fractal behavior and long-range dependence. To achieve the above, modeling memristor and its corresponding chaotic systems is performed, from which a random number generator is constructed. Subsequently, the Hurst parameter for the detection of long-range dependence is estimated and a fractal analysis of the synthesized data is performed. Finally, a comparison between the model proposed in the research and the β-MWM algorithm is made. The results obtained show that the data synthesized from the proposed generator have a variable Hurst parameter and both monofractal and multifractal behavior. The main contribution of this research is the proposal of a new model for the synthesis of traces with long-range dependence and fractal behavior based on the non-linearity of the memristor.

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“…Image encryption in a PWL memristor-based Chua system was proposed by Lin et al [29]. Further, plain text encryption [30] is another important aspect in securing documents, and the user's data in terms of PDF and Doc files is another type of information we wish to secure.…”

Section: Introductionmentioning

confidence: 99%

From chaos to encryption using fractional order Lorenz-Stenflo model with flux-controlled feedback memristor

Khan

Qureshi²,

Akbar³

et al. 2022

Phys. Scr.

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To add complexity to a chaotic system, a new five-dimensional fractional-order chaotic system is proposed based on the Lorenz-Stenflo model with a feedback memristor. By analyzing the phase portraits, equilibrium points, bifurcation analysis, and Poincaré maps, the system generates a two-wing attractor with symmetrical coexistence, which implies that the newly developed chaotic system has abundant dynamical characteristics. The Routh–Hurwitz stability criterion, eigenvalues, and Lyapunov exponents were calculated for a memristive-based system, suggesting that the developed system is unstable and hyperchaotic. The chaotic system is executed with analogue circuits for both the open-loop and closed-loop feedback memristive systems. The transfer function technique was used for the fractional operator. The simulation results showed excellent agreement between the circuit and numerical simulations. Finally, random data information from a chaotic system is utilized to process multimedia encryption. A new cryptographic scheme is presented with the idea of an image as a key, which is introduced and tested with security analysis in support of the provision that images and chaotic systems together can form a viable key.

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Memristor-Based Chaotic Circuit for Text/Image Encryption and Decryption

Cited by 14 publications

References 15 publications

Memristor in a Reservoir System—Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI₂

Memristor in a Reservoir System—Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI₂

Random Number Generator with Long-Range Dependence and Multifractal Behavior Based on Memristor

From chaos to encryption using fractional order Lorenz-Stenflo model with flux-controlled feedback memristor

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Memristor-Based Chaotic Circuit for Text/Image Encryption and Decryption

Cited by 14 publications

References 15 publications

Memristor in a Reservoir System—Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI2

Memristor in a Reservoir System—Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI2

Random Number Generator with Long-Range Dependence and Multifractal Behavior Based on Memristor

From chaos to encryption using fractional order Lorenz-Stenflo model with flux-controlled feedback memristor

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Product

Resources

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Memristor in a Reservoir System—Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI₂

Memristor in a Reservoir System—Experimental Evidence for High-Level Computing and Neuromorphic Behavior of PbI₂