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A New Quantum gray-scale image Encoding Scheme

Communications in Theoretical Physics 2018年第2期69卷 215-226页

作者： mosayeb naseri mona abdolmaleky fariborz parandin negin fatahi ahmed farouk reza nazari department of physics kermanshah branchislamic azad universityKermanshahIran department of electrical engineering college of engineeringkermanshah branchislamic azad universityKermanshahIran computer sciences department faculty of computers and informationmansoura universityMansoura UniversityEgypt department of it engineering college of engineeringkermanshah branchislamic azad universityKermanshahIran

In this paper, a new quantum images encoding scheme is proposed. The proposed scheme mainly consists of four different encoding algorithms. The idea behind of the scheme is a binary key generated randomly for each pixel of the original image. Afterwards, the employed encoding algorithm is selected corresponding to the qubit pair of the generated randomized binary key. The security analysis of the proposed scheme proved its enhancement through both randomization of the generated binary image key and altering the gray-scale value of the image pixels using the qubits of randomized binary key. The simulation of the proposed scheme assures that the final encoded image could not be recognized visually. Moreover, the histogram diagram of encoded image is flatter than the originM one. The Shannon entropies of the final encoded images are significantly higher than the original one, which indicates that the attacker can not gain any information about the encoded images.

关键词： quantum image quantum encoding gray-scale image

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Curve length estimation based on cubic spline interpolation in gray-scale images

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Journal of Zhejiang University-Science C(Computers and Electronics) 2013年第10期14卷 777-784页

作者： Zhen-xin WANG Ji-hong OUYANG College of Computer Science and Technology Jilin University MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University

This paper deals with a novel local arc length estimator for curves in gray-scale *** method first estimates a cubic spline curve fit for the boundary points using the gray-level information of the nearby pixels,and then computes the sum of the spline segments’*** this model,the second derivatives and y coordinates at the knots are required in the computation;the spline polynomial coefficients need not be computed *** provide the algorithm pseudo code for estimation and preprocessing,both taking linear *** shows that the proposed model gains a smaller relative error than other state-of-the-art methods.

关键词： Arc length estimation Cubic spline interpolation gray-scale image Local algorithm

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Automatic malware classification and new malware detection using machine learning

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Frontiers of Information Technology & Electronic Engineering 2017年第9期18卷 1336-1347页

作者： Liu LIU Bao-sheng WANG Bo YU Qiu-xi ZHONG College of Computer National University of Defense Technology Changsha 410073 China

The explosive growth ofmalware variants poses a major threat to information security. Traditional anti-virus systems based on signatures fail to classify unknown malware into their corresponding families and to detect new kinds of malware pro- grams. Therefore, we propose a machine learning based malware analysis system, which is composed of three modules： data processing, decision making, and new malware detection. The data processing module deals with gray-scale images, Opcode n-gram, and import fimctions, which are employed to extract the features of the malware. The decision-making module uses the features to classify the malware and to identify suspicious malware. Finally, the detection module uses the shared nearest neighbor （SNN） clustering algorithm to discover new malware families. Our approach is evaluated on more than 20 000 malware instances, which were collected by Kingsoft, ESET NOD32, and Anubis. The results show that our system can effectively classify the un- known malware with a best accuracy of 98.9%, and successfully detects 86.7% of the new malware.

关键词： Malware classification Machine learning n-gram gray-scale image Feature extraction Malware detection

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