Two Discrete Memristive Chaotic Maps and Its DSP Implementation

Han, Xintong; Mou, Jun; Lu, Jinshi; Banerjee, Santo; Cao, Yinghong

doi:10.1142/s0218348x23401047

Cited by 19 publications

(6 citation statements)

References 31 publications

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“…Early fusion refers to fusion in the input layer, which involves fusing features firstly and then training the predictor on the fused features. Intermediate fusion first converts features on different data sources into intermediate high-dimensional feature representations, then performs the fusion, and finally trains the predictor [26]. Late fusion refers to fusion in the prediction layer, first making predictions on different features and then fusing the results of these predictions.…”

Section: Feature Fusionmentioning

confidence: 99%

CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

Lu,

Ren,

Zhang

et al. 2023

Electronics

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Malware is a significant threat to the field of cyber security. There is a wide variety of malware, which can be programmed to threaten computer security by exploiting various networks, operating systems, software and physical security vulnerabilities. So, detecting malware has become a significant part of maintaining network security. In this paper, data enhancement techniques are used in the data preprocessing stage, then a novel detection mode—CPL-Net employing malware texture image—is proposed. The model consists of a feature extraction component, a feature fusion component and a classification component, the core of which is based on the parallel fusion of spatio-temporal features by Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). Through experiments, it has been proven that CPL-Net can achieve an accuracy of 98.7% and an F1 score of 98.6% for malware. The model uses a novel feature fusion approach and achieves a comprehensive and precise malware detection.

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Section: Feature Fusionmentioning

confidence: 99%

CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

Lu,

Ren,

Zhang

et al. 2023

Electronics

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“…Feedback is defined as the output of the system as part of the input during causal iteration, which in turn influences the work of the system [33][34][35][36]. In DL, the feedback mechanism can support the network in using high-level features of the output to optimize the weights of the previous convolution kernel.…”

Section: Feedback Mechanismmentioning

confidence: 99%

Optimal Reconstruction of Single-Pixel Images through Feature Feedback Mechanism and Attention

Gao,

Su,

Zhang

et al. 2023

Electronics

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The single-pixel imaging technique can reconstruct high-quality images using only a bucket detector with no spatial resolution, and the image quality is degraded in order to meet the demands of real-time applications. According to some studies of algorithm performance, the network model performs differently in simulated and real-world experiments. We propose an end-to-end neural network capable of reconstructing 2D images from experimentally obtained 1D signals optimally. In order to improve the image quality of real-time single-pixel imaging, we built a feedback module in the hidden layer of the recurrent neural network to implement feature feedback. The feedback module fuses high-level features of undersampled images with low-level features through dense jump connections and multi-scale balanced attention modules to gradually optimize the feature extraction process and reconstruct high-quality images. In addition, we introduce a learning strategy that combines mean loss with frequency domain loss to improve the network’s ability to reconstruct complex undersampled images. In this paper, the factors that lead to the degradation of single-pixel imaging are analyzed, and a network degradation model suitable for physical imaging systems is designed. The experiment results indicate that the reconstructed images utilizing the proposed method have better quality metrics and visual effects than the excellent methods in the field of single-pixel imaging.

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“…First of all, chaotic keys are used to generate chaotic sequences to provide pseudo-randomness for the encryption scheme. [16][17][18][19] Most of the previously proposed encryption schemes utilize the generated chaotic sequences to re-alize the confusion and diffusion operations on the plaintext images, and after obtaining the cipher images, the user can get the decrypted images under the condition of the given chaotic key. [20][21][22][23][24] Chaotic systems are extremely keysensitive, i.e., two sets of chaotic keys with a slight difference produce chaotic sequences that are radically different from each other.…”

Section: Introductionmentioning

confidence: 99%

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row–column confusion and closed-loop bi-directional diffusion

Zhang 张,

Mou 牟,

Banerjee

et al. 2024

Chinese Phys. B

Self Cite

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Security during remote transmission has been an important concern for researchers in recent years. In this paper, a hierarchical encryption multi-image encryption scheme for people with different security levels is designed, and a multi-image encryption (MIE) algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper. While ensuring secure communication of medical image information, people with different security levels have different levels of decryption keys, and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys. The highest security level can obtain decrypted images without watermarks, and at the same time, patient information and copyright attribution can be verified by obtaining watermark images. The experimental results show that the scheme is sufficiently secure as a MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

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Two Discrete Memristive Chaotic Maps and Its DSP Implementation

Cited by 19 publications

References 31 publications

CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

CPL-Net: A Malware Detection Network Based on Parallel CNN and LSTM Feature Fusion

Optimal Reconstruction of Single-Pixel Images through Feature Feedback Mechanism and Attention

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row–column confusion and closed-loop bi-directional diffusion

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