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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Lang Chen Yuling Chen Chaoyue Tan Hui Dou Yun Luo Pengyu Chen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

Blockchain has penetrated multiple fields such as finance, healthcare, supply chain, and intelligent transportation. However, the value exchange between different independent blockchain systems limits the expansion of blockchain. Cross-chain technology can achieve the transfer of digital assets between blockchain networks, and the notary mechanism is a typical cross-chain technology that can achieve value exchange between different blockchains. However, it requires trust in specific notaries. To address these issues, this paper proposes a multi-indicator credit ranking scheme based on notaries to improve the reliability of notaries and ensure the security of value exchange between different blockchains. To tackle the problem of single and incomplete inherent value evaluation indicators in traditional notary mechanisms, a multi-indicator evaluation is adopted to ensure the fairness of notary node credit evaluation. To address the issue of inaccurate comprehensive credit evaluation of notaries, a comprehensive evaluation and ranking of notary nodes are conducted based on their historical transaction information to improve the accuracy of their credit evaluation. The experimental results show that using multiple indicators to evaluate the inherent value of notary nodes can comprehensively evaluate the scheme and accurately screen out high-credit nodes to act as notaries, improving the security of cross-chain asset transactions.

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Adversarial Attack Method Based on Dual- frequency Domain Transformation

Adversarial Attack Method Based on Dual- frequency Domain Tr...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Maolin Xiong Yuling Chen Hui Dou Yun Luo Chaoyue Tan Yangwen Zhang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

Deep neural network is vulnerable to adversarial samples, which means altering the image subtly to introduce errors into the neural network model. Adversarial samples can categorize into black-box and white-box attacks, white-box attacks have been able to achieve a high success rate, but the performance of black-box attacks is weak due to the large gap between the substitute model and the victim model. Previously, the way to enhance the substitute model is image transformations on the spatial domain, to simulate different victim models. However, these methods lead to weak transferability, because it can not generate diverse enhanced models. To solve this problem, we propose a dual-frequency-domain transform attack based on DCT(Discrete Cosine Transform) and DWT(Discrete Wavelet Transformation), proposed method optimizes the process of frequency-domain transform, which can greatly enhance the substitute models. Besides, our method can combine with others (gradient attack methods and spatial domain attack methods) to further enhance the attack effect. Experiments show that the attack success rate of our method is good in several mainstream no-defense models.

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Robust Object Detection Based on a Comparative Learning Perspective

Robust Object Detection Based on a Comparative Learning Pers...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Hao Yang Yuling Chen Hui Dou Yun Luo Chaoyue Tan Yangwen Zhang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

With the advent of adversarial samples, the safety of Artificial Intelligence is of particular concern, as only a small amount of perturbation can be added to mislead a model's judgment. Therefore there is an urgent need for research on models that can resist adversarial perturbations. To alleviate this problem, we first analyze the vulnerability of adversarial samples and propose a uniform perspective robust model for object detection that accurately identifies both clean and ad-versarial samples. We propose a robust object detection based on the contrastive learning perspective (RCP), which can learn features from both clean and adversarial samples from a fine-grained perspective, and can recognize adversarial samples more accurately. Extensive experiments on PASCAL VOC and MS COCO show that our proposed method only degrades the clean sample detection performance by a small amount in exchange for a large robustness improvement against adversarial attacks, achieving state of the art results.

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SeeMe: An intelligent edge server selection method for location-aware business task computing over IIoT

SeeMe: An intelligent edge server selection method for locat...

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作者： Dou, Wanchun Liu, Bowen Duan, Jirun Dai, Fei Qi, Lianyong Xu, Xiaolong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Computer Science Qufu Normal University Qufu China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

In the past few years, latency-sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self-driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across-domain business task computing on distributed edge servers. With this observation, a trade-off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low-latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low-latency edge server. The experiments and comparison analysis verify the availability of our method. © 2022 John Wiley & Sons Ltd.

关键词： Economic and social effects

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Research on active defense decision-making method for cloud boundary networks based on reinforcement learning of intelligent agent

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High-Confidence Computing 2024年第2期4卷 50-61页

作者： Huan Wang Yunlong Tang Yan Wang Ning Wei Junyi Deng Zhiyan Bin Weilong Li School of Computer Science and Technology Guangxi University of Science and TechnologyLiuzhou 545000China Liuzhou Key Laboratory of Big Data Intelligent Processing and Security Liuzhou 545000China Guangxi Education System Network Security Monitoring Center Liuzhou 545000China Center of Graphics and Information Guangxi ECO-Engineering Vocational&Technical CollegeLiuzhou 545004China

The cloud boundary network environment is characterized by a passive defense strategy,discrete defense actions,and delayed defense feedback in the face of network attacks,ignoring the influence of the external environment on defense decisions,thus resulting in poor defense ***,this paper proposes a cloud boundary network active defense model and decision method based on the reinforcement learning of intelligent agent,designs the network structure of the intelligent agent attack and defense game,and depicts the attack and defense game process of cloud boundary network;constructs the observation space and action space of reinforcement learning of intelligent agent in the non-complete information environment,and portrays the interaction process between intelligent agent and environment;establishes the reward mechanism based on the attack and defense gain,and encourage intelligent agents to learn more effective defense *** designed active defense decision intelligent agent based on deep reinforcement learning can solve the problems of border dynamics,interaction lag,and control dispersion in the defense decision process of cloud boundary networks,and improve the autonomy and continuity of defense decisions.

关键词： Active defense decision-making Cloud boundary network security Intelligent agent reinforcement learning Offensive and defensive game

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Uncertain graph generating approach based on differential privacy for preserving link relationship of social networks

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International Journal of Security and Networks 2022年第1期17卷 28-38页

作者： Yan, Jun Tian, YuPan Liu, Hai Wu, ZhenQiang School of Computer Science Shaanxi Normal University Xi’an710119 China School of Mathematics and Computer Applications Shangluo College Shangluo726000 China Guizhou Provincial Key Laboratory of Public Big Data Guiyang550025 China

With the widespread use of social networks in our daily life, the personal privacy in social networks has become a growing concern. To prevent the link relationship of social networks from disclosing users’ sensitive information when the social networks data is released, an uncertain graph approach based on differential privacy is introduced, which can resist attacks based on background knowledge and possesses better data utility. In this approach, we propose modification of edges based on random response (MERR) algorithm and injection of uncertainty based on k-edges-differential privacy (IUDP) algorithm. The MERR algorithm can modify the edge of original graph according to random response mechanism, while the IUDP algorithm injects uncertainty to generate an uncertain graph. For evaluating our approach, the expectation of editing distance between two graphs is adapted to measure the level of privacy preserving. In addition, our approach is conducted in different datasets and compared with other approaches. The experimental results indicate that this approach achieves differential privacy and has better data utility. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Privacy-preserving techniques

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Research on Instance Segmentation of High-Resolution Remote Sensing Images 7

Research on Instance Segmentation of High-Resolution Remote ...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zhang, Xiaoying Yang, Houqun Li, Xuanyu Yang, Dangui Shen, Jie Xu, Gaodi Hainan University College of Computer Science And Technology Hainan Haikou China Hainan University Haikou Key Laboratory of Deep Learning And Big Data Application Technology Hainan Haikou China

ISBN: (纸本)9798350359145

Increasingly, high-resolution remote sensing images are being used in real life as a result of the continuous advancement of high-definition remote sensing imaging technology in recent years. They play a crucial role in urban planning, agriculture, oceans, ecological protection, and other fields. A growing amount of remote sensing image data has led to a growing interest in using image instance segmentation to decode remote sensing images by automatically extracting valuable information. The present paper examines and analyzes a large number of instance segmentation methods on remote sensing images. It begins with an explanation of the classification and research progress of natural image instance segmentation techniques, which are categorized into two-stage instance segmentation, single-stage instance segmentation, multi-stage instance segmentation and weakly supervised instance segmentation, and also analyzed in terms of their advantages and shortcomings. And then we discussed it in more detail of remote sensing image instance segmentation using deep learning, and it is explained that each of these methods can be classified into three types based on its technical characteristics: methods that combine multiscale feature fusion, methods that combine dilated convolution and methods that combine attention mechanisms, as well as the benefits and limitations of each method. The results of classical experiments on different datasets are presented, along with an explanation of the commonly used datasets and evaluation metrics in instance segmentation. This paper presents an overview of the current problems and challenges associated with remote sensing image instance segmentation, as well as future trends and developments. © 2023 IEEE.

关键词： Convolution

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A Blockchain-Based Dynamic Incentive Model in Mobile Edge Computing

A Blockchain-Based Dynamic Incentive Model in Mobile Edge Co...

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International Conference on Networking and Network Applications (NaNA)

作者： Zhiyin Chen Feng Zhou Youliang Tian Shuai Wang State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

With the rapid development of the Internet of Things (IoT), mobile edge computing (MEC) is becoming increasingly important as a key technology to increase data processing speed and reduce delay. However, due to the imbalance of benefits, many mobile users (MUs) are not willing to participate, and even participating MUs take malicious actions to maximise benefits. To address these issues, we propose a dynamic incentive model based on smart contract. Firstly, we use cryptocurrency as a transaction medium and introduce a deposit mechanism based on smart contracts to enhance the transparency and enforcement of the incentive mechanism. Secondly, we formally construct the benefit functions and strategy behaviours of MUs, and analyse the stability of MUs’ strategies using evolutionary game. Finally, based on the analysis results, we propose an incentive contract that dynamically moderates the benefits of MUs so as to incentivise MUs to actively, honestly and stably participate in MEC. Simulation experiments prove the correctness and feasibility of the scheme, which promotes the active, honest and stable participation of MUs in MEC.

关键词： Analytical models Multi-access edge computing Computational modeling Smart contracts Games Quality of service data processing

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype Learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. The DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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A Novel Global-Local Representations Network for Speech Steganalysis 5

A Novel Global-Local Representations Network for Speech Steg...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Xu, Shulong Tian, Hui Quan, Hanyu Lu, Jing College of Computer Science and Technology Xiamen Key Laboratory of Data Security and Blockchain Technology Fujian Key Laboratory of Big Data Intelligence and Security National Huaqiao University China

ISBN: (纸本)9781450396899

With the popularity of the internet and the development of network services, Steganography based on speech stream has become a research hotspot in information hiding. To improve the detection performance of steganalysis of multiple steganography methods, in this paper, we proposed the Global-Local Representations Network (GLRN), which consists of a Global Correlation Extraction (GCE) module and a Local Correlation Enhancement (LCE) module. Firstly, considering the inter-class differences of different coding elements, the GCE module is used to capture the global correlation of different coding elements by using multi-channel modeling. Then, we realize that the process of global correlation extraction suffers from the loss of detailed information, so the LCE module is used to capture local correlations to complement the global features. The experiments show that the GLRN achieves the start-of-art detection performance. © 2022 ACM.

关键词： Steganography

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