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Reversible data hiding scheme with edge-direction predictor and modulo operation

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Reversible data hiding scheme with edge-direction predictor ...

作者： Kim, Dae-Soo Yoon, Eun-Jun Kim, Cheonshik Yoo, Kee-Young School of Computer Science and Engineering College of IT Engineering Kyungpook National University 1370 Sankyuk-Dong Buk-Gu Daegu702-701 Korea Republic of Department of Cyber Security Kyungil University 50 Gamasil-Gil Hayang-Eup Gyenggsan-SiGyeongbuk712-701 Korea Republic of Department of Digital Media Engineering Anyang University 708-113 Anyang 5-dong Manan-gu Anyang-shiKyonggi-do430-714 Korea Republic of

Since the first histogram shifting technique was proposed by Ni et al., many histogram based data hiding methods were proposed to improve their scheme. One of the methods is using difference value between cover image and prediction image. Another method is using two point pairs and absolute value for improving Ni et al.’s scheme. In this paper, novel reversible data hiding scheme with edge-direction predictor and modulo operation was proposed for improving histogram shifting technique with 3 cases. We considered as much as possible around pixels by using edge-direction predictor with odd and even line embedding Also, we utilize two point pairs and absolute value at the same time by using modulo operation with wrap around. In the experimental results, the proposed scheme shown a good quality image result about 48dB as similar as other schemes and enhanced hiding capacity over 50 % than other schemes. © 2016, Springer-Verlag Berlin Heidelberg.

关键词： Steganography

A real-time secret image sharing with fairness

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A real-time secret image sharing with fairness

作者： Lee, Gil-Je Yoon, Eun-Jun Kim, Cheonshik Yoo, Kee-Young School of Computer Science and Engineering College of IT Engineering Kyungpook National University 1370 Sankyuk-Dong Buk-Gu Daegu702-701 Korea Republic of Department of Cyber Security Kyungil University 50 Gamasil-Gil Hayang-Eup Gyenggsan-SiGyeongbuk712-701 Korea Republic of Department of Digital Media Engineering Anyang University 708-113 Anyang 5-dong Manan-gu Anyang-shiKyonggi-do430-714 Korea Republic of

When traditional secret image sharing techniques reconstructed the secret, they input the shares over t. While less than t shares can know nothing about the secret, the problem arises when there are more than t shares. The cheater can use this to put their share in the last. Therefore, fairness is a important objective of the secret image sharing. Tian et al. proposed the fairness secret sharing scheme in 2012. However, they generated v polynomials for one secret data and performed v-times to reconstruct the polynomial using Lagrange interpolation. Therefore, their scheme is unsuitableness in the real-time processing. The proposed scheme generates one polynomial for the one secret data based on the fairness concept of Tian et al.’s scheme. For the providing fairness, the proposed scheme hides the verification value at the random coefficient of the polynomial. During the secret image reconstruction procedure, each shadow image brought by a participant is verified for its fairness using XOR operation. Our scheme not only satisfies the fairness, but also is suitable for the real-time process. This helps to detect the participant from intentional provision of a false or cheating. In addition, our scheme uses the steganography technique for increasing the security protection purpose. The proposed scheme as a whole offers a high secure and effective mechanism for the secret image sharing that is not found in existing secret image sharing methods. In the experimental result, PSNR of the proposed scheme is average 44.67 dB. It is higher 4 dB than the previous schemes. The embedding capacity is also similar to the other schemes. © 2016, Springer-Verlag Berlin Heidelberg.

关键词： Polynomials

The World of Malware: An Overview

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The World of Malware: An Overview

International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Anitta Patience Namanya Andrea Cullen Irfan U. Awan Jules Pagna Disso Faculty of Science and Technology Ankole Western University Sheema Uganda School of Electrical Engineering and Computer Science University of Bradford Bradford UK Research and Development Nettitude Intelligent Cyber Security and Risk Management Leamington Spa UK

ISBN: (纸本)9781538675045

Malware, short for malicious software is a program code that is hostile and often used to corrupt or misuse a system. Introducing malware into a computer network environment has different effects depending on the design intent of the malware and the network layout. Malware detection and prevention systems are bypassed by malicious files in computer systems as malware become more complex and large in numbers. This paper presents an overview of the world of malware with the intent of providing the underlying information for the intended study into developing malware detection approaches.

关键词： Tools Ransomware computer viruses Botnet Trojan horses

Seeing isn’t believing: Towards more robust adversarial attack against real world object detectors

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arXiv 2018年

作者： Zhao, Yue Zhu, Hong Liang, Ruigang Shen, Qintao Zhang, Shengzhi Chen, Kai SKLOIS Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Department of Computer Science Metropolitan College Boston University United States

Recently Adversarial Examples (AEs) that deceive deep learning models have been a topic of intense research interest. Compared with the AEs in the digital space, the physical adversarial attack is considered as a more severe threat to the applications like face recognition in authentication, objection detection in autonomous driving cars, etc. In particular, deceiving the object detectors practically, is more challenging since the relative position between the object and the detector may keep changing. Existing works attacking object detectors are still very limited in various scenarios, e.g., varying distance and angles, etc. In this paper, we presented systematic solutions to build robust and practical AEs against real world object detectors. Particularly, for Hiding Attack (HA), we proposed the feature-interference reinforcement (FIR) method and the enhanced realistic constraints generation (ERG) to enhance robustness, and for Appearing Attack (AA), we proposed the nested-AE, which combines two AEs together to attack object detectors in both long and short distance. We also designed diverse styles of AEs to make AA more surreptitious. Evaluation results show that our AEs can attack the state-of-the-art real-time object detectors (i.e., YOLO V3 and faster-RCNN) at the success rate up to 92.4% with varying distance from 1m to 25m and angles from −60◦ to 60◦1. Our AEs are also demonstrated to be highly transferable, capable of attacking another three state-of-the-art black-box models with high success rate. Copyright © 2018, The Authors. All rights reserved.

关键词： Object detection

Cluster-Aware Kronecker Supported Data Collection for Sensory Data

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Cluster-Aware Kronecker Supported Data Collection for Sensor...

International Conference on computer Communications and Networks (ICCCN)

作者： Siguang Chen Zhihao Wang Kewei Sha Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer Science & Cyber Security Institute University of Houston-Clear Lake Houston TX

Although current proposed compression schemes achieve a better performance compared with traditional data compression schemes, they have not fully exploited the spatial and temporal correlations among the data. Well-designed clustering algorithms are needed to explore strong spatial correlation. In this paper, we propose a k-means based Kronecker supported two-dimensional (spatio-temporal) compression scheme to achieve better compression results. Our scheme first leverages a k-means based clustering algorithm that explores the spatial correlation among sensory data. Then it develops a novel two-dimensional data compression mechanism, which can recover the original data from the compressed data with a high precision. Simulation results show that our proposed scheme is energy-efficient and can achieve better clustering results and recovery performance compared with other schemes for sensory data.

关键词： Correlation Clustering algorithms Data compression Wireless sensor networks Sparse matrices Sensors Spatial databases

Contract-Based Content Delivery in 5G Converged Networks

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China Communications 2017年第12期14卷 120-133页

作者： Xiaoyuan Lu Yiqing Li Xiaoying Gan Yanwei Xu Jun Yao School of Computer Science Fudan University Shanghai China Dept. of Electronics Engineering Shanghai Jiao Tong University Shanghai 200240 China Engineering Research Center of Cyber Security Auditing and Monitoring Ministry of Education China Shanghai Engineering Research Center for Broadband Technologies & Applications China Philips Lighting Research China

Mobile broadcasting services provided by converged networks do aid in satisfying users' demands for popular multimedia content while unicasting services offer personalized experiences for *** analyze hybrid broadcasting unicasting framework from the perspective of network economics,where content provider(CP) figures out the cooperation of broadcasting and unicasting services providers,as long as their pricing *** this end,a contract-based content delivery scheme is *** profit of CP depends on users' preference and satisfaction for unicasting and broadcasting *** provides different users with distinctive data *** intent is to maximize its own *** classifying users into different types,the optimal contract in close form is *** results show that the proposed optimal contract is able to generate incentive for users to employ broadcasting transmission,which further benefits both the CP and users.

关键词： content delivery 5g convergednetworks

Non-reciprocity compensation combined with turbo codes for secret key generation in vehicular ad hoc social IoT networks

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arXiv 2018年

作者： Epiphaniou, Gregory Karadimas, Petros Ismail, Dhouha Kbaier Ben Al-Khateeb, Haider Dehghantanha, Ali Choo, Kim-Kwang Raymond School of Computer Science and Technology University of Bedfordshire United Kingdom School of Engineering University of Glasgow Scotland United Kingdom School of Computer Science and Engineering University of Salford United Kingdom Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX United States

—The physical attributes of the dynamic vehicle-to-vehicle (V2V) propagation channel can be utilised for the generation of highly random and symmetric cryptographic keys. However, in physical-layer key agreement, non-reciprocity due to inherent channel noise and hardware impairments can propagate bit disagreements which have to be addressed prior to the symmetric key generation which is inherently important in social IoT networks. This work parametrically models temporal variability attributes such as 3D scattering and scatterers’ mobility and for the first time incorporates such features into the key generation process by combining non-reciprocity compensation with turbo codes. Preliminary results indicate a significant improvement in bit mismatch rate (BMR) and key generation rate (KGR) when compared with sample indexing techniques. Copyright © 2018, The Authors. All rights reserved.

关键词： Internet of things

Gated fusion network for single image dehazing

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arXiv 2018年

作者： Ren, Wenqi Ma, Lin Zhang, Jiawei Pan, Jinshan Cao, Xiaochun Liu, Wei Yang, Ming-Hsuan IIE Cas Tencent Ai Lab City University of Hong Kong Nanjing University of Science and Technology School of Cyber Security University of Chinese Academy of Sciences University of California Electrical Engineering and Computer Science Merced United States

In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy input. The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder. The encoder is exploited to capture the context of the derived input images, while the decoder is employed to estimate the contribution of each input to the final dehazed result using the learned representations attributed to the encoder. The constructed network adopts a novel fusion-based strategy which derives three inputs from an original hazy image by applying White Balance (WB), Contrast Enhancing (CE), and Gamma Correction (GC). We compute pixel-wise confidence maps based on the appearance differences between these different inputs to blend the information of the derived inputs and preserve the regions with pleasant visibility. The final dehazed image is yielded by gating the important features of the derived inputs. To train the network, we introduce a multi-scale approach such that the halo artifacts can be avoided. Extensive experimental results on both synthetic and real-world images demonstrate that the proposed algorithm performs favorably against the state-of-the-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

MCP: A multi-component learning machine to predict protein secondary structure

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arXiv 2018年

作者： Khalatbari, Leila Kangavari, M.R. Hosseini, Saeid Yin, Hongzhi Cheung, Ngai-Man School of Computer Engineering Iran University of Science and Technology Tehran Iran ST Electronics SUTD Cyber Security Laboratory Singapore University of Technology and Design Singapore School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia

The Gene or DNA sequence in every cell does not control genetic prop-erties on its own Rather, this is done through translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightly connected to its specific 3D structure. Prediction of the protein secondary struc-ture is a crucial intermediate step towards elucidating its 3D structure and function. Traditional experimental methods for prediction of protein structure are expensive and time-consuming. Therefore, various machine learning approaches have been proposed to predict the protein secondary structure. Nevertheless, the average accuracy of the suggested solutions has hardly reached beyond 80%. The possible underlying reasons are the ambiguous sequence-structure relation, noise in input protein data, class imbal- A nce, and the high dimensionality of the encoding schemes that represent the protein sequence. In this paper, we propose an accurate multi-component prediction machine to overcome the challenges of protein structure prediction. We devise a multi-component designation to address the high complexity challenge in sequence-structure relation. Furthermore, we utilize a compound string dissimilarity measure to directly interpret protein sequence content and avoid information loss. In order to improve the accuracy, we employ two different classifiers including support vector machine and fuzzy nearest neighbor and collectively aggregate the classification outcomes to infer the final protein secondary structures. We conduct comprehensive experiments to compare our model with the current state-of-the-art approaches. The experimental results demonstrate that given a set of input sequences, our multi-component framework can accurately predict the protein structure. Nevertheless, the effectiveness of our unified model can be further enhanced through framework configuration. Copyright © 2018, The Authors. All rights reserved.

关键词： Support vector machines