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19th International Workshop on Digital Forensics and Watermarking, IWDW 2020

作者： Zhao, Xianfeng Yang, Chunfang Liu, Fenlin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100195 China School of Cyber Security University of Chinese Academy of Sciences Beijing100093 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030694487

Steganography has long been considered as a way of hiding the fact of secret communication. However, the fact that a message sender and a receiver communicated with each other is seldom protected. It can be dangerous because they often have very secret relationship. Noticeably, in recent two years some researchers respectively proposed robust and secure steganographic schemes which realize dependable steganography in the lossy channel of social media without degrading the security and capacity, and more relevant technologies, such as robust steganographic coding and fast steganography, were also proposed. In this review, the sharing-based model of steganography implied by social media and promoted by both the emerging advanced robust steganography and fast steganography is formally presented and compared with the dominating prisoner-warden model. It is shown that designing steganography under the sharing-based model can protect the fact of communication and the sender-receiver relationship instead of merely the fact of secret communication, improving the overall security of steganographic systems. © 2021, Springer Nature Switzerland AG.

关键词： Steganography

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ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by Distributed Ledger Technology and Internet of Things 23

ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Haoyu Li, Leixiao Lin, Hao Chang, Xiangyang Wan, Jianxiong Li, Jie Zhu, Fangyuan College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Reg. Eng. and Technol. Research Center of Big Data Based Software Service China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

P2P Botnet is famous for the resilience against termination. However, its dependence on Neighbor List (NL) makes it susceptible to infiltration and poison, also leading to a dearth of adequate protection of Botmaster's identity. So as to overcome these weaknesses, the novel botnet infrastructure combined with blockchain emerges. But due to some flaws such like high latency and expensive transactions fees on blockchain, these new schemes can't satisfy the need of constructing hyper large-scale botnet in practice. We propose ZombieCoin3.0, a Botnet underlying overlay leveraging IoT devices to be maintainers and integrated with Distributed Ledger Technology (DLT), which is able to deploy bots fully on blockchain. And it is versatile for file transfer, data feedback and other command and control scenarios that many contemporaries are impotent to cope with. Our proposal eliminates the P2P botnets' dependence on NL and the bottlenecks that other blockchain combined botnets have. Experiments' results authenticated that the effectiveness of our proposal as well as low latency of communication and nearly zero overhead of cost other comparatives unable to attain are achieved. © 2021 IEEE.

关键词： Costs Distributed ledger Botnet Blockchains Proposals Internet of Things Low latency communication

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Exploration and research of blended learning in the "principles of computer organization" 2

Exploration and research of blended learning in the "princip...

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2nd International Conference on Computer Science and Educational Informatization, CSEI 2020

作者： Hou, Yifan Wang, Wei Liu, Fudong Shan, Zheng Wang, Junchao Wang, Lixin State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China

ISBN: (纸本)9781728170077

The'Principles of Computer Organization' is the core basic course for the construction of computer hardware courses. In this paper, it analyzes the characteristics of the 'Principles of Computer Organization 'Aiming at the challenges faced by the traditional teaching. This paper makes a beneficial exploration on the teaching reform of the Principle of Computer Composition based on the blended learning mode from the aspects of teaching content, teaching method transformation, interactive communication, evaluation and assessment, etc. This paper proposes a framework for self-learning ability training based on a blended learning model, which is applied and formed in the course of principles. Teaching practice shows that the reform of teaching mode and teaching practice not only stimulates learning interest and motivation of students, but also improves their self-learning ability and practical ability. © 2020 IEEE.

关键词： Teaching

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The Propagation Strategy Model of Taint Analysis 4

The Propagation Strategy Model of Taint Analysis

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2019 4th International Seminar on Computer Technology, Mechanical and Electrical engineering, ISCME 2019

作者： Ren, Yuzhu Zhao, Jiangtao Zhang, Cao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450000 China

The taint propagation strategy is the core of the taint analysis technology. When the taint analysis toolsanalyses the target program, it needs to mark the target data according to the formulatedtaint propagation strategy. Formulating a reasonable strategy for taint propagation can effectively improve the accuracy of taint analysis. There are two difficulties in developing the taint propagation strategy, namely, alias analysis in static taint analysis and indirect jump in dynamic taint analysis. In our paper, we propose a taint propagation strategy at the intermediate representation language level, which can effectively improve the accuracy of taint propagation. © Published under licence by IOP Publishing Ltd.

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LinNet: Linear Network for Efficient Point Cloud Representation Learning 38

LinNet: Linear Network for Efficient Point Cloud Representat...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Deng, Hao Jing, Kunlei Cheng, Shengmei Liu, Cheng Ru, Jiawei Bo, Jiang Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University China Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality China School of Software Engineering Xi'an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet. © 2024 Neural information processing systems foundation. All rights reserved.

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MC-RGCN: A Multi-Channel Recurrent Graph Convolutional Network to Learn High-Order Social Relations for Diffusion Prediction

MC-RGCN: A Multi-Channel Recurrent Graph Convolutional Netwo...

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IEEE International Conference on Data Mining (ICDM)

作者： Ningbo Huang Gang Zhou Mengli Zhang Meng Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665423991

Information diffusion prediction aims to predict the tendency of information spreading in the network. Previous methods focus on extracting chronological features from diffusion paths and leverage relations in social graph as side information to facilitate diffusion prediction. However, abundant high-order social relations in information diffusion have not been sufficiently utilized, such as co-repose and co-following which can further mine potential user common preferences. In this paper, we construct a heterogeneous diffusion network (HDN) from the social graph and information cascades to model the high-order social relations in information diffusion. Then, we design a novel model named Multi-Channel Recurrent Graph Convolutional Network (MC-RGCN), which can extract high-order social relation semantics from the channels of HDN to promote prediction performance. In each channel, we depict a specific social relations from the views of global topology, pairwise strength, and local structure. Finally, we conduct extensive experiments on three real-world datasets, and the results show that our proposed method outperforms the state-of-the-art models on diffusion prediction.

关键词： Network topology Conferences Semantics Predictive models Feature extraction Data models Topology

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GLCF: A Global-Local Multimodal Coherence Analysis Framework for Talking Face Generation Detection

arXiv

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arXiv 2024年

作者： Chen, Xiaocan Yin, Qilin Liu, Jiarui Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China Alibaba Group Hangzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

Talking face generation (TFG) allows for producing lifelike talking videos of any character using only facial images and accompanying text. Abuse of this technology could pose significant risks to society, creating the urgent need for research into corresponding detection methods. However, research in this field has been hindered by the lack of public datasets. In this paper, we construct the first large-scale multi-scenario talking face dataset (MSTF), which contains 22 audio and video forgery techniques, filling the gap of datasets in this field. The dataset covers 11 generation scenarios and more than 20 semantic scenarios, closer to the practical application scenario of TFG. Besides, we also propose a TFG detection framework, which leverages the analysis of both global and local coherence in the multimodal content of TFG videos. Therefore, a region-focused smoothness detection module (RSFDM) and a discrepancy capture-time frame aggregation module (DCTAM) are introduced to evaluate the global temporal coherence of TFG videos, aggregating multi-grained spatial information. Additionally, a visual-audio fusion module (V-AFM) is designed to evaluate audiovisual coherence within a localized temporal perspective. Comprehensive experiments demonstrate the reasonableness and challenges of our datasets, while also indicating the superiority of our proposed method compared to the state-of-the-art deepfake detection approaches. © 2024, CC BY-NC-ND.

关键词： Semantics

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ZeroDVS: Trace-Ability and Security Detection of Container Image Based on Inheritance Graph

ZeroDVS: Trace-Ability and Security Detection of Container I...

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Cryptography, Security and Privacy (CSP), IEEE International Conference on

作者： Yan Zheng Weiyu Dong Jiangtao Zhao State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengZhou China

ISBN: (数字)9781728186214

ISBN: (纸本)9781728186221

Docker image is the foundation for container operation. Docker Hub is the largest online repository of public container images. Users can upload and download any random image file to the hub due to the absence of adequate security scanning and detection, potentially causing substantial security risks. This paper introduces ZeroDVS, a container image traceability and security detection system based on inheritance graphs. In ZeroDVS, a basic image inheritance graph is built with 160 official images published by Docker Hub. Then, the basic image source of the downloaded image can be identified, and the public vulnerability database is used to scan for public vulnerabilities in image files. ZeroDVS scans the public container images of Docker Hub and identifies the inheritance relationship of public container images to detect public vulnerabilities in the image layer above the parent image simultaneously. Docker image is the foundation for container operation. Docker Hub is the largest online repository of public container images. Users can upload and download any random image file to the hub due to the absence of adequate security scanning and detection, potentially causing substantial security risks. This paper introduces ZeroDVS, a container image traceability and security detection system based on inheritance graphs. In ZeroDVS, a basic image inheritance graph is built with 160 official images published by Docker Hub. Then, the basic image source of the downloaded image can be identified, and the public vulnerability database is used to scan for public vulnerabilities in image files. ZeroDVS scans the public container images of Docker Hub and identifies the inheritance relationship of public container images to detect public vulnerabilities in the image layer above the parent image simultaneously.

关键词： Privacy Databases Operating systems Containers Security Reliability Cryptography

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mathematical modeling of lunar soft landing —Take chang'e-3 as an example

Mathematical modeling of lunar soft landing —Take chang'e-3...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Benchao Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665428408

In this paper, the three-dimensional sphere dynamic model of lunar precise soft landing of the probe is discussed. Taking the National College Students' mathematical modeling competition as the background and Chang'e 3 lunar landing as an example, the lunar center inertial coordinate system and orbital coordinate system are established respectively to better describe the positional relationship between the moon and aircraft and the dynamic changes of aircraft. The coordinates of the near lunar point and the far lunar point are obtained. Through the changes of altitude, speed, pitch angle, yaw angle, longitude and latitude during landing, the landing preparation orbit of Chang'e-3 is determined. Then we analyze the six stages of chang'e-3 landing process one by one, and discuss the results.

关键词： Space vehicles Machine learning algorithms Moon Clustering algorithms mathematical models Data models Partitioning algorithms

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A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

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Plant Phenomics 2023年第2期5卷 209-225页

作者： Xiaojun Xie Fei Xia Yufeng Wu Shouyang Liu Ke Yan Huanliang Xu Zhiwei Ji College of Artificial Intelligence Nanjing Agricultural UniversityNanjingJiangsu 210095China Center for Data Science and Intelligent Computing Nanjing Agricultural UniversityNanjingJiangsu 210095China State Key Laboratory for Crop Genetics and Germplasm Enhancement Bioinformatics CenterAcademy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjingJiangsu 210095China Academy for Advanced Interdisciplinary Studies Nanjing Agricultural UniversityNanjingJiangsu 210095China Department of the Built Environment College of Design and EngineeringNational University of Singapore4 Architecture DriveSingapore 117566Singapore

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern ***,it has limited interpretability for deep *** the transfer of expert knowledge,handcrafted features provide a new way for personalized diagnosis of plant ***,irrelevant and redundant features lead to high *** this study,we proposed a swarm intelligence algorithm for feature selection[salp swarm algorithm for feature selection(SSAFS)]in image-based plant disease *** is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of *** verify the effectiveness of the developed SSAFS algorithm,we conducted experimental studies using SSAFS and 5 metaheuristic *** evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from *** results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms,confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image *** computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

关键词： Plant image redundant

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