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A Novel Video Encryption Method Based on Faster R-CNN

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A Novel Video Encryption Method Based on Faster R-CNN

2018 International Conference on Computer Science, Electronics and Communication Engineering（CSECE2018）

作者： Lijuan Duan Dongkui Zhang Fan Xu Guoqin Cui Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection State Key Laboratory of Digital Multi-media Chip Technology Vimicro Corporation

ISBN: (纸本)9781510862593

In order to improve the generalization performance of video encryption and reduce the amount of data in video en-cryption, this paper proposes a video encryption on regions of interest(ROI) method based on Faster R-CNN by combining machine learning with information security. The method trains a Faster R-CNN model using the ROI dataset firstly, and then uses the model to extract ROI in the video. Different encryption algorithms are used to encrypt ROI and non-ROI in the video respectively. To overcome the shortcomings of encryption algorithms that can only be used for a specific coded video, a special video encryption method is proposed to encrypt the video with different video coding structure and has better generalization performance. Compared with the encryption method in the video coding process, this method considers the content information of the video fully and has better performance. It can be concluded through experiments that the encryption method in this paper has the characteristics of higher security and less calculation.

关键词： video encryption faster R-CNN the ROI of video

Robust ordinal embedding from contaminated relative comparisons

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arXiv 2018年

作者： Ma, Ke Xu, Qianqian Cao, Xiaochun State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

Existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons;then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. The merits of our method are three-fold: (1) By virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated;(2) The proposed method is aware of global inconsistency by minimizing a corresponding cost, while traditional methods only involve local inconsistency;(3) Instead of considering the nuclear norm heuristics, we adopt an exact solution for rank equality constraint. Our studies are supported by experiments with both simulated examples and real-world data. The proposed framework provides us a promising tool for robust ordinal embedding from the contaminated comparisons. Copyright © 2018, The Authors. All rights reserved.

关键词： Embeddings

Spatial-Temporal Person Re-identification

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arXiv 2018年

作者： Wang, Guangcong Lai, Jianhuang Huang, Peigen Xie, Xiaohua School of Data and Computer Science Sun Yat-sen University China Guangdong Key Laboratory of Information Security Technology Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education

Most of current person re-identification (ReID) methods neglect a spatial-temporal constraint. Given a query image, conventional methods compute the feature distances between the query image and all the gallery images and return a similarity ranked table. When the gallery database is very large in practice, these approaches fail to obtain a good performance due to appearance ambiguity across different camera views. In this paper, we propose a novel two-stream spatial-temporal person ReID (st-ReID) framework that mines both visual semantic information and spatial-temporal information. To this end, a joint similarity metric with Logistic Smoothing (LS) is introduced to integrate two kinds of heterogeneous information into a unified framework. To approximate a complex spatial-temporal probability distribution, we develop a fast Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint, the st-ReID model eliminates lots of irrelevant images and thus narrows the gallery database. Without bells and whistles, our st-ReID method achieves rank-1 accuracy of 98.1% on Market-1501 and 94.4% on DukeMTMC-reID, improving from the baselines 91.2% and 83.8%, respectively, outperforming all previous state-of-the-art methods by a large margin. Code is available at https://***/Wanggcong/ Spatial-Temporal-Re-identification. Copyright © 2018, The Authors. All rights reserved.

关键词： Probability distributions

Less but Better: Generalization enhancement of ordinal embedding via distributional margin

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arXiv 2018年

作者： Ma, Ke Xu, Qianqian Yang, Zhiyong Cao, Xiaochun State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences

In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. However, collecting a large number of labeled data is known as a hard task, and most of the existing work pay little attention to the generalization ability with insufficient samples. Meanwhile, recent progress in large margin theory discloses that rather than just maximizing the minimum margin, both the margin mean and variance, which characterize the margin distribution, are more crucial to the overall generalization performance. To address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled Distributional Margin based Ordinal Embedding (DMOE). Precisely, we first define the margin for ordinal embedding problem. Secondly, we formulate a concise objective function which avoids maximizing margin mean and minimizing margin variance directly but exhibits the similar effect. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to seek the optimal solution of DMOE effectively. Experimental studies on both simulated and real-world datasets are provided to show the effectiveness of the proposed algorithm. Copyright © 2018, The Authors. All rights reserved.

关键词： Lagrange multipliers

Cryptanalysis of a lattice based key exchange protocol

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Science China(Information Sciences) 2017年第2期60卷 196-198页

作者： Shaowu MAO Pei ZHANG Houzhen WANG Huanguo ZHANG Wanqing WU Computer School of Wuhan University The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Huawei Technoligies Co. Ltd. School of Computer Science and Technology Hebei University

A lattice is a set of points in n-dimensional space with a periodic ***-based cryptosystem holds a great promise for post-quantum cryptography[1],as they enjoy very strong security proofs based on the worst-case hardness,relatively efficient implementations,as well as great *** first lattice-based cryptosystem was proposed by Ajtai and Dwork[2],whose security is based on the lattice problems in the *** their seminal work,several latticebased cryptosystems have been proposed till now,

关键词： ISIS Bi Cryptanalysis of a lattice based key exchange protocol

JDAS: a software development framework for multidatabases

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JDAS: a software development framework for multidatabases

作者： Xie, Guoqi Chen, Yuekun Liu, Yan Fan, Chunnian Li, Renfa Li, Keqin College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China Nanjing University of Information Science and Technology China Department of Computer Science State University of New York New PaltzNY United States

Modern software development for services computing and cloud computing software systems is no longer based on a single database but on existing multidatabases and this convergence needs new software architecture and framework design. Most current popular frameworks are not designed for multidatabases, and many practical problems in development arise. This study designs and implements a software development framework called Java data access service (JDAS) for multidatabases using the object-oriented programming language Java. The JDAS framework solves related problems that arise when other frameworks are employed in practical software development with multidatabases by presenting and introducing design methods. JDAS consists of the modules of the database abstract, object relational mapping, connection pools management, configuration management, data access service, and inversion of control. Results and case study reveal that the JDAS framework effectively reduces development complexity and improves development efficiency of the software systems with multidatabases. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Object oriented programming

Simple Convolutional Neural network on Image Classification 2

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Simple Convolutional Neural Network on Image Classification

2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Tianmei Guo Jiwen Dong Henjian Li Yunxing Gao Department of Computer Science and Technology Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan

ISBN: (纸本)9781509036202

In recent years,deep learning has been used inimage classification,object tracking,pose estimation,textdetection and recognition,visual saliency detection,actionrecognition and scene *** Encoder,sparse coding,Restricted Boltzmann Machine,Deep Belief networks andConvolutional neural networks is commonly used models indeep *** different type of models,Convolutionalneural networks has been demonstrated high performance onimage *** this paper we bulided a simpleConvolutional neural network on image *** Convolutional neural network completed the *** experiments are based on benchmarkingdatasets minist [1] and *** the basis of theConvolutional neural network,we also analyzed differentmethods of learning rate set and different optimizationalgorithm of solving the optimal parameters of the influence onimage classification.

关键词： Convolutional neural network Deep learning Image classification learning rate parametric solution

Power Attack and Protected Implementation on Lightweight Block Cipher SKINNY

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Power Attack and Protected Implementation on Lightweight Blo...

Asia Joint Conference on Information security (Asia JCIS)

作者： Jing Ge Yifan Xu Ruiqian Liu Enze Si Ning Shang An Wang School of Computer Science State Key Laboratory of Cryptology Beijing China School of Computer Science Beijing Institute of Technology Beijing China Network Security Squadron of Public Security Department of Henan Province Zhengzhou China Chinese Academy of Sciences China Key laboratory of network assessment technology & Institute of Information Engineering Beijing China

SKINNY is a new lightweight tweakable block cipher family, which can compete to other lightweight cipher in terms of hardware or software implementations. While its theoretical security has been widely studied, little effort has been made to analyze its implementation security such as side-channel attacks protection. In this paper, we first give correlation power attack and its experiment on SKINNY. Then, a masking scheme for software SKINNY is proposed against side-channel attacks. Its implementation shows that the protected SKINNY only costs 8.42%, 183.27%, and 90.85% of extra code, time, and RAM, respectively.

关键词： Encryption Correlation Power demand Ciphers Hamming weight

Fast stochastic ordinal embedding with variance reduction and adaptive step size

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arXiv 2019年

作者： Ma, Ke Zeng, Jinshan Xiong, Jiechao Xu, Qianqian Cao, Xiaochun Liu, Wei Yao, Yuan School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Information Engineering Jiangxi Normal University NanchangJiangxi330022 China Tencent AI Lab Shenzhen Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics and by courtesy Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

—Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(T1 ) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems