scholarly journals Medical Image Encryption and Compression Scheme Using Compressive Sensing and Pixel Swapping Based Permutation Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Li-bo Zhang ◽  
Zhi-liang Zhu ◽  
Ben-qiang Yang ◽  
Wen-yuan Liu ◽  
Hong-feng Zhu ◽  
...  

This paper presents a solution to satisfy the increasing requirements for secure medical image transmission and storage over public networks. The proposed scheme can simultaneously encrypt and compress the medical image using compressive sensing (CS) and pixel swapping based permutation approach. In the CS phase, the plain image is compressed and encrypted by chaos-based Bernoulli measurement matrix, which is generated under the control of the introduced Chebyshev map. The quantized measurements are then encrypted by permutation-diffusion type chaotic cipher for the second level protection. Simulations and extensive security analyses have been performed. The results demonstrate that at a large scale of compression ratio the proposed cryptosystem can provide satisfactory security level and reconstruction quality.

2021 ◽  
Vol 29 ◽  
pp. 185-193
Author(s):  
Shuang Liu ◽  
Li Liu ◽  
Ming Pang

BACKGROUND: Medical image security has been paid more attention in the medical field. OBJECTIVE: In order to achieve a higher security level of medical image encryption, this paper proposes a stream cipher enhanced logic mapping encryption method. METHODS: According to the theory of stream cipher, this method uses Chebyshev map to form encryption key. A series of coding operations are used to set the initial value before image chaos processing. Combining with logical mapping, the original image information is encrypted by chaos from X and Y dimensions. RESULTS: The experimental results show that the NPCR value of the encrypted image is 0.9874 after the blood cells are encrypted. CONCLUSIONS: All four evaluation methods fully prove that this method has excellent encryption performance.


2022 ◽  
Vol 32 (2) ◽  
pp. 841-857
Author(s):  
Arwa Mashat ◽  
Surbhi Bhatia ◽  
Ankit Kumar ◽  
Pankaj Dadheech ◽  
Aliaa Alabdali

2020 ◽  
pp. 1-26
Author(s):  
Qinwen Hu ◽  
Muhammad Rizwan Asghar ◽  
Nevil Brownlee

HTTPS refers to an application-specific implementation that runs HyperText Transfer Protocol (HTTP) on top of Secure Socket Layer (SSL) or Transport Layer Security (TLS). HTTPS is used to provide encrypted communication and secure identification of web servers and clients, for different purposes such as online banking and e-commerce. However, many HTTPS vulnerabilities have been disclosed in recent years. Although many studies have pointed out that these vulnerabilities can lead to serious consequences, domain administrators seem to ignore them. In this study, we evaluate the HTTPS security level of Alexa’s top 1 million domains from two perspectives. First, we explore which popular sites are still affected by those well-known security issues. Our results show that less than 0.1% of HTTPS-enabled servers in the measured domains are still vulnerable to known attacks including Rivest Cipher 4 (RC4), Compression Ratio Info-Leak Mass Exploitation (CRIME), Padding Oracle On Downgraded Legacy Encryption (POODLE), Factoring RSA Export Keys (FREAK), Logjam, and Decrypting Rivest–Shamir–Adleman (RSA) using Obsolete and Weakened eNcryption (DROWN). Second, we assess the security level of the digital certificates used by each measured HTTPS domain. Our results highlight that less than 0.52% domains use the expired certificate, 0.42% HTTPS certificates contain different hostnames, and 2.59% HTTPS domains use a self-signed certificate. The domains we investigate in our study cover 5 regions (including ARIN, RIPE NCC, APNIC, LACNIC, and AFRINIC) and 61 different categories such as online shopping websites, banking websites, educational websites, and government websites. Although our results show that the problem still exists, we find that changes have been taking place when HTTPS vulnerabilities were discovered. Through this three-year study, we found that more attention has been paid to the use and configuration of HTTPS. For example, more and more domains begin to enable the HTTPS protocol to ensure a secure communication channel between users and websites. From the first measurement, we observed that many domains are still using TLS 1.0 and 1.1, SSL 2.0, and SSL 3.0 protocols to support user clients that use outdated systems. As the previous studies revealed security risks of using these protocols, in the subsequent studies, we found that the majority of domains updated their TLS protocol on time. Our 2020 results suggest that most HTTPS domains use the TLS 1.2 protocol and show that some HTTPS domains are still vulnerable to the existing known attacks. As academics and industry professionals continue to disclose attacks against HTTPS and recommend the secure configuration of HTTPS, we found that the number of vulnerable domain is gradually decreasing every year.


Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1384
Author(s):  
Yin Dai ◽  
Yifan Gao ◽  
Fayu Liu

Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it cannot deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependencies, and effective multi-modal fusion strategies can greatly improve the performance of deep models. This prompts us to study transformer-based structures and apply them to multi-modal medical images. Existing transformer-based network architectures require large-scale datasets to achieve better performance. However, medical imaging datasets are relatively small, which makes it difficult to apply pure transformers to medical image analysis. Therefore, we propose TransMed for multi-modal medical image classification. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images and establish long-range dependencies between modalities. We evaluated our model on two datasets, parotid gland tumors classification and knee injury classification. Combining our contributions, we achieve an improvement of 10.1% and 1.9% in average accuracy, respectively, outperforming other state-of-the-art CNN-based models. The results of the proposed method are promising and have tremendous potential to be applied to a large number of medical image analysis tasks. To our best knowledge, this is the first work to apply transformers to multi-modal medical image classification.


Author(s):  
M. KUZHALISAI ◽  
G. GAYATHRI

Cloud computing is a new type of service which provides large scale computing resource to each customer. Cloud Computing Systems can be easily threatened by various cyber attacks, because most of Cloud computing system needs to contain some Intrusion Detection Systems (IDS) for protecting each Virtual Machine (VM) against threats. In this case, there exists a tradeoff between the security level of the IDS and the system performance. If the IDS provide stronger security service using more rules or patterns, then it needs much more computing resources in proportion to the strength of security. So the amount of resources allocating for customers decreases. Another problem in Cloud Computing is that, huge amount of logs makes system administrators hard to analyse them. In this paper, we propose a method that enables cloud computing system to achieve both effectiveness of using the system resource and strength of the security service without trade-off between them.


Author(s):  
Yang-Sun Lee ◽  
Jae-Min Kwak ◽  
Sung-Eon Cho ◽  
Ji-Woong Kim ◽  
Heau-Jo Kang

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