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Image authentication and self-recovery scheme based on the rehashing model

Journal of information Hiding and Multimedia Signal processing 2016年第3期7卷 460-474页

作者： Lyu, Wan-Li Chang, Chin-Chen Wang, Feng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230039 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Rd. Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan Department of Mathematics and Physics Fujian University of Technology FuzhouFujian350108 China

An effective image tamper localization and self-recovery scheme that uses the rehashing model is proposed in this paper. The aim of the proposed scheme was to reduce the failure rate in detecting image tamper areas and achieve high rates of successful detection. To achieve this aim, the proposed scheme uses a set of hash functions which are called the rehashing model, gained an optimal decreasing collision method to detect tamper areas With an improved and accurate tamper detection rate, the tampered image can be recovered more accurately. The quality of the proposed scheme was tested with gray and color images. The experimental results indicated that the proposed scheme has good performance in locating tamper locations and allows image recovery with an acceptable visual quality up to 50% of the image content tampered. © 2016.

关键词： Hash functions

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Trusted Boolean Search on Cloud Using Searchable Symmetric Encryption

Trusted Boolean Search on Cloud Using Searchable Symmetric E...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Cong Zuo James Macindoe Siyin Yang Ron Steinfeld Joseph K. Liu School of Computer and Information Engineering Zhejiang Gongshang University Zhejiang P.R. China Faculty of Information Technology Clayton Campus Monash University Australia Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou Fujian China

ISBN: (纸本)9781509032068

A Searchable Symmetric Encryption (SSE) scheme allows a server to search a user's data without having to decrypt the data. This provides the user with a high degree of privacy and is particularly useful when data is stored on Cloud. Numerous SSE schemes have already been proposed and while most have excellent security properties, few meet high performance requirements and most only support searching for a single keyword at a time. The SSE scheme of Cash et al. (CRYPTO 2013) is notable for its high efficiency on restricted forms of Boolean queries, but has low efficiency for, or does not support, other common forms of boolean queries. In this paper, we propose a generalization and optimization of the Cash et al. SSE scheme, which extends to support a much larger class of Boolean queries, and performs no worse, and often with much higher efficiency than the Cash et al. scheme for the remaining queries.

关键词： Servers Indexes Encryption Complexity theory

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EXPRESS WAYBILL EXTRACTION ON PARCEL IMAGES BY LOGO MATCHING

EXPRESS WAYBILL EXTRACTION ON PARCEL IMAGES BY LOGO MATCHING

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IEEE International Conference on Image processing

作者： Shujing Lu Jie Zhao Yue Lu ECNU-SRI Joint Lab for Pattern Analysis and Intelligent System Shanghai Research Institute of China Post Group Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University

ISBN: (纸本)9781467399623

We propose an efficient method for automatically extracting express waybills from parcel images, which is challenging due to varied resolution of parcel images, arbitrary direction of waybills and different information filled by senders. To address these challenges, logo matching is employed to extract the waybills. We begin by extracting scale-invariant feature-transformation (SIFT) keypoints from both the reference logo image and a parcel image, and matching them subject to a consistent projective transformation (homography) by using random sample consensus (RANSAC). Once the homography matrix is computed, we extract the waybill of parcel image by mapping all pixels from a standard waybill image to the parcel image. Experimental results on test datasets demonstrate the effectiveness of the proposed method.

关键词： Logo matching Express waybill extraction SIFT images Sifting Fields LOGO Imagery (Psychotherapy) Image homography

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An online learning algorithm for bilinear models 32

An online learning algorithm for bilinear models

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32nd International Conference on Machine Learning, ICML 2015

作者： Wu, Yuanbin Sun, Shiliang Shanghai Key Laboratory of Multidimensional Information Processing China Department of Computer Science and Technology East China Normal University China

ISBN: (纸本)9781510810587

We investigate the bilinear model, which is a matrix form linear model with the rank 1 constraint. A new online learning algorithm is proposed to train the model parameters. Our algorithm runs in the manner of online mirror descent, and gradients are computed by the power iteration. To analyze it, we give a new second order approximation of the squared spectral norm, which helps us to get a regret bound. Experiments on two sequential labelling tasks give positive results. Copyright © 2015 by the author(s).

关键词： Iterative methods

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Regression-based execution time prediction in hadoop environment

Regression-based execution time prediction in hadoop environ...

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International Conference on information technology and computer Application Engineering, ITCAE 2014

作者： Wu, Xiao Dong Zeng, Yu Zhu Zhao, C.T. Faculty of Mathematics and Computer Science Quanzhou Normal University China Fujian Provincial Key Laboratory of Data Intensive Computing China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University China

ISBN: (纸本)9781138027176

Hadoop MapReduce has been proven an effective computing model to deal with big data for the last few years. However, one technical challenge facing this framework is how to predict the execution time of an individual job. In this paper, we propose a regression-based method to predict the execution times of Hadoop jobs. The proposed prediction method consists of three phases: learning data sampling, regression analysis and making prediction. Experimental results show that the predicted execution time obtained by our method is close to the actual execution time. © 2015 Taylor & Francis Group, London.

关键词： Regression analysis

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Neighborhood collaborative classifiers

Neighborhood collaborative classifiers

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Suping Xu Xibei Yang Eric C. C. Tsang Eric A. Mantey Intelligent Information Processing Key Laboratory of Shanxi Province Shanxi University Taiyuan China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Economics and Management Nanjing University of Science and Technology Nanjing China Faculty of Information Technology Macau University of Science and Technology Macau China

ISBN: (纸本)9781509003914

In neighborhood rough set model, the majority rule based neighborhood classifier (NC) is easy to be misjudged with the increasing of the size of information granules. To remedy this deficiency, we propose a neighborhood collaborative classifier (NCC) based on the idea of collaborative representation based classification (CRC). NCC first performs feature selection with neighborhood rough set, and then instead of solving the classification problem by the majority rule, NCC solves a similar problem with collaborative representation among the neighbors of each unseen sample. Experiments on UCI data sets demonstrate that: 1) Our NCC achieves satisfactory performance in larger neighborhood information granules when compared with NC; 2) NCC reduces the size of dictionary when compared with CRC.

关键词： Collaboration Rough sets Training Cybernetics Numerical models Error analysis Measurement

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Semantic automatic image annotation based on Non-Abstract Visual keywords

Semantic automatic image annotation based on Non-Abstract Vi...

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2014 International Conference on information technology and Application, ITA2014

作者： Ke, Xiao Chen, Guolong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9781138026773

Automatic image annotation is a significant and challenging problem in pattern recognition and computer vision. Existing models did not describe the visual representations of corresponding keywords, which would lead to appearing plenty of irrelevant annotations in final annotation results. These annotations did not relate to any part of images considering visual contents. We propose a new automatic image annotation model (NAVK) based on relevant visual keywords to overcome above problems. Our model focuses on non-abstract words. First, we establish visual keyword seeds of each non-abstract word, and then a new method is proposed to extract visual keyword collections by using corresponding seeds. Second, we propose adaptive parameter method and fast solution algorithm to determine similarity thresholds of each keyword. Finally, the combinations of above methods are used to improve annotation performance. Experimental results verify the effectiveness of proposed image annotation model. © 2015 Taylor & Francis Group, London.

关键词： Image annotation

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Study on subjective statement screening of product reviews based on the feature combination method

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ICIC Express Letters 2016年第7期10卷 1591-1597页

作者： Sun, Tieli Guan, Weiqiao Yang, Fengqin Sun, Hongguang Ji, Chuande Song, Meijia Li, Yanwen School of Computer Science and Information Technology Northeast Normal University No. 2555 Jingyue Ave Changchun130117 China Key Laboratory of Intelligent Information Processing of Jilin Universities No. 2555 Jingyue Ave Changchun130117 China Flight Theory Department Aviation University of Air Force Changchun130022 China

Subjective statement screening is a fundamental part of sentiment analysis of product reviews. Its aim is to extract the subjective texts from product reviews and filter objective texts that do not contain any sentimental information. The existing screening methods are mainly based on only one feature. However, single feature is insufficient to mark all subjective or objective statements. We propose a feature combination method based on the N-pos feature, the word feature and the syntactic dependency feature for subjective statement screening. The experiments on the real datasets show that the proposed feature combination method significantly improves the accuracy of subjective statement screening and is really feasible and effective. © 2016, ICIC Express Letters Office. All rights reserved.

关键词： Sentiment analysis

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A Method of QoS Deviation Monitoring for Web Services

A Method of QoS Deviation Monitoring for Web Services

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第27届中国控制与决策会议

作者： CHEN Bin-Bin FU Xiao-Dong LIU Li GAO Tian-Yu GE Jia Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Provincial Key Laboratory of Computer Application

ISBN: (纸本)9781479970162

Considering the fraudulent behavior of Quality of Service(QoS) for web services,a method to monitor the QoS deviation was proposed in this ***,we used random variable to describe the uncertainty of ***,the divergence of QoS in different time periods was tested with Kolmogorov Smirnov(K-S) ***,we utilized stochastic dominance to judge the better one of *** analysis and experimental data showed that the proposed method was reasonable and effective.

关键词： Quality of Service,Web Services,Kolmogorov Smirnov,Stochastic Dominance

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An improved reference point sampling method on Pareto optimal front

An improved reference point sampling method on Pareto optima...

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Congress on Evolutionary Computation

作者： Cheng He Linqiang Pan Hang Xu Ye Tian Xingyi Zhang Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China Huazhong University of Science and Technology Wuhan Hubei CN Department of Computer Science Xiamen University Xiamen Fujian China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui China

ISBN: (纸本)9781509006243

In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.

关键词： Sampling methods Shape Convergence Evolutionary computation Optimization Euclidean distance

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