Color image chaos encryption algorithm combining CRC and nine palace map

Xiong, Zenggang; Wu, Yuan; Ye, Conghuan; Zhang, Xuemin; Xu, Fang

doi:10.1007/s11042-018-7081-3

Cited by 117 publications

(52 citation statements)

References 28 publications

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“…In this experiment, in order to compare the classification accuracy and results of different methods more truly and comprehensively, the overall classification accuracy and kappa coefficient are used as evaluation indexes to evaluate the classification accuracy of six different feature extraction and classification methods. In order to compare and analyze the influence of different models and methods on the classification results more comprehensively, the proposed method is also combined with convolution neural network (CNN), Gaussian kernel support vector machine (RBF-SVM), convolution neural network (PCA-CNN), convolution neural network and support vector machine (CNN-SVM) after principal component analysis and dimensionality reduction, and the experimental results of SVM algorithm Ratio [41], [42].…”

Section: B Results and Analysismentioning

confidence: 99%

Research on Medical Image Classification Based on Machine Learning

Tang

2020

IEEE Access

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In this paper, we propose a new method for CT pathological image analysis of brain and chest to extract image features and classify images. Because the deep neural network needs a large number of labeled samples to complete the training, and the cost of medical image labeling is very high, the training samples needed to train the deep neural network are insufficient. In this paper, a semi supervised learning based image classification method is proposed, which uses a small amount of labeled pathological image data to train the network model, and then integrates the features extracted by the network to classify the image. The results show that the classification effect of the neural network is better than convolution neural network and other traditional image classification models. To some extent, it can reduce the dependence of neural network on a large number of training samples, and effectively reduce the over fitting phenomenon of the network. Through the analysis of the overall classification accuracy and kappa coefficient of different classification methods under different sample numbers, it is found that the overall classification accuracy and kappa coefficient are increasing with the increasing number of training samples. Especially in the case of a small number of training samples, compared with other deep neural networks and traditional classification methods, the classification accuracy of the counter neural network is about 10% higher than that of other neural networks and traditional classification methods, and the advantages are more obvious. INDEX TERMS Generative adversarial network, deep learning, feature extraction, image classification.

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Section: B Results and Analysismentioning

confidence: 99%

Research on Medical Image Classification Based on Machine Learning

Tang

2020

IEEE Access

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“…Table 15 gives the time taken for encryption by the different images. The time taken by the Lena image and the average for all the eight images is 5.9328 sec and 5.7754 sec respectively which is better than the [71] and [72]. As far as the theoretical analysis is concerned, a number of factors like input, compiler, software, hardware etc.…”

Section: J Speed and Complexity Analysismentioning

confidence: 96%

DNA Strands Level Scrambling Based Color Image Encryption Scheme

et al. 2020

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In the past, many image encryption schemes have been developed through the swapping operations at the different levels of granularity. These levels span bits, Deoxyribonucleic acid (DNA) molecules, pixels, blocks of pixels. In this study, a new scheme for the encryption of color images based on the DNA strands level scrambling (DNASLS) and chaotic system has been proposed. After a color image is input, it is decomposed into the red, green and blue components. After it, these components are merged to form a big single image. Intertwining logistic map (ILM) has been used for the random data which generates three streams of random numbers. These streams have been further manipulated in such a way that nine streams are spawned out of them. One stream out of these nine streams has been used for the generation of a key image. Two streams have been used to DNA-encode the big single image and the key image. Afterwards, the strands of DNA-encoded single image are swapped with each other. Four streams determine the addresses of two DNA encoded pixels for the selection. A yet another stream is being used to select a particular strand from the DNA strands. To create the diffusion effects, an XOR operation has been done between the DNA encoded image after the swapping of strands and the DNA encoded key image. Finally, the last and ninth stream has been used to decode the DNA-encoded pixels into the decimal form. Purely random numbers with no interdependence have been employed in the entire encryption process. The effects of plaintext sensitivity have been achieved through the incorporation of Secure Hash Algorithm-256 or SHA-256 hash codes. In the end, the experiment and the security analysis have been performed. The results of the validation metrics like information entropy(7.9973), average key sensitivity(99.61%) and mean absolute error(84.7158) demonstrate the security, defiance to the number of attacks and a potential for real world application of the proposed image cipher.

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“…A delay estimation based on Kalman filtering was proposed in [22], and congestion control was performed accordingly to improve the system throughput. Different bandwidth estimation methods based on Kalman filtering were proposed and applied to different scenarios in [23][24][25][26][27][28][29][30][31][32][33][34][35], however, these algorithms were all based on linear systems, while most of the actual networks were nonlinear systems.…”

Section: Research Background and Related Workmentioning

confidence: 99%

Research of Wireless Congestion Control Algorithm Based on EKF

Wang¹,

Tang²,

Hong³

2020

Symmetry

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The random variation of bandwidth in wireless networks causes some significant challenges to the congestion control protocols based on bandwidth estimation. In this paper, a wireless congestion control scheme based on extended Kalman filtering and bandwidth (CSEKB) is proposed. The CSEKB can effectively perceive the bandwidth oscillation of wireless networks and distinguish the type of packet loss by establishing a noise perception factor. According to the congestion factor, the congestion control parameters are adjusted to correspondingly improve the performance of the wireless network. Moreover, the variation trend of the size of the congestion window presents a law of similar normal distribution curve, which has a certain degree of local symmetry. The CSEKB was implemented in Network simulator 3 (NS3) and compared with TCP Westwood (TCPW), CUBIC, and extended Kalman filtering-based bandwidth estimation (EBE). Through extensive simulation studies, the proposed CSEKB demonstrated the significant performance in wireless networks. First, the CSEKB can achieve congestion control based on the accurate prediction of available bandwidth, and improve average throughput and link utilization. In addition, the CSEKB has good fairness and friendliness compared with several other well-known congestion control methods.

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Color image chaos encryption algorithm combining CRC and nine palace map

Cited by 117 publications

References 28 publications

Research on Medical Image Classification Based on Machine Learning

Research on Medical Image Classification Based on Machine Learning

DNA Strands Level Scrambling Based Color Image Encryption Scheme

Research of Wireless Congestion Control Algorithm Based on EKF

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