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2023 IEEE International Conference on Unmanned systems, ICUS 2023

作者： Li, Zhe Liu, Jin Dong, Yibo Ren, Jiaqi Li, Weili National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350316308

In order to solve the problem that it is difficult to effectively express and quantify various uncertain factors in infrastructure network attacker-defender game modelling, an infrastructure network attacker-defender game model based on intuitionistic fuzzy sets is established by combining game theory with intuitionistic fuzzy sets. After converting the exact payoff matrix into an intuitionistic fuzzy matrix according to certain rules, the nonlinear programming solver is used to solve the model to obtain the equilibrium strategy of both sides. Finally, the experimental results are analysed to show the effectiveness and feasibility of the model. © 2023 IEEE.

关键词： Nonlinear programming

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DADN: Data Augmentation-Driven Dual Network Framework for Node Classification 2

DADN: Data Augmentation-Driven Dual Network Framework for No...

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2nd International Conference on Automation, Robotics and Computer engineering, ICARCE 2023

作者： Liu, Dengfeng Pan, Zhiqiang Wang, Siyuan Li, Peihong Liu, Shixian Mao, Yanying National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350308341

Node classification is an important task in graph neural networks (GNNs), aiming to predict the category of nodes in the graph according to their neighbours and their own characteristics. Current methods mainly focus on one level of data augmentation, ignoring huge potential in other underlying levels. Furthermore, existing models cannot fully exploit the information contained in the existing data, which undermines the performance of node classification. In order to solve the above problems, we propose a novel Data Augmentation-driven Dual Network (DADN) framework. Specifically, we first design a composite data augmentation module that comprehensively considers the local neighborhood level, the node level, and the edge level to augment the original features. Then, we copy the original features and the augmented features into the dual network framework with dropout for model training to capture the innermost features. Finally, we experimentally evaluate our method on three real-world public datasets, and the results show that our method outperforms the baseline models in the node classification task, except for individual metrics. © 2023 IEEE.

关键词： Classification (of information)

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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Recent Progress in All-Solution-Processed Organic Solar Cells

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Chinese Journal of Chemistry 2024年第2期42卷 190-198页

作者： Yixuan Xu Qian Wang Wentao Zou Xu Zhang Yanna Sun Yuanyuan Kan Ping Cai Ke Gao School of Materials Science and Engineering&Guangxi Key Laboratory of Information Materials Guilin University of Electronic TechnologyGuilinGuangxi 541004China Shandong Provincial Key Laboratory for Science of Material Creation and Energy Conversion Science Center for Material Creation and Energy ConversionInstitute of Frontier and Interdisciplinary ScienceShandong UniversityQingdaoShandong 266237China

All-solution-processed organic solar cells(OSCs)(from the bottom electrode to the top electrode)are highly attractive thanks to their low cost,lightweight and high-throughput ***,achieving highly efficient all-solution-processed OSCs remains a significant *** of the key issues is the lack of high-quality solution-processed electrode systems that can replace indium tin oxide(ITO)and vacuum-deposited metal *** this paper,we comprehensively review recent advances in all-solution-processed osCs,and classified the devices as the top electrode materials,including silver nanowires(AgNWs),conducting polymers and composite conducting *** correlation between electrode materials,properties of electrodes,and device performance in all-solution-processed OSCs is *** addition,the critical roles of the active layer and interface layer are also ***,the prospects and challenges of all-solution-processed OSCs are presented.

关键词： Organic solar cells All-solution-processed organic solar cells Solution-processed electrodes High performance Silver nanowires Conductive polymers Composite conducting materials

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Collaborative Learning for Cross-Organizational Data Sharing Using Hyperledger Fabric 8

Collaborative Learning for Cross-Organizational Data Sharing...

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8th International Conference on Data science in Cyberspace, DSC 2023

作者： Zheng, Yaling Cheng, Ziwen Liu, Yi Wang, Bowen Zhu, Cheng National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350331035

The rise of edge networking presents both opportunities and challenges. The ever-expanding array of IoT devices generates vast amounts of data, enabling collaboration and smart applications. However, managing decentralized data, resolving heterogeneity, and ensuring privacy poses challenges. The combination of blockchain and federated learning offers a solution. But trust management, inefficiencies, and ambiguous assessment of data contribution have become new obstacles. In this paper, we propose a data sharing model that supports cross-organizational collaborative learning based on Hyperledger Fabric, in order to enhance the high-quality data sharing of collaborative learning and improve the effect of learning. The experimental results show that the proposed model can realize decentralized and efficient data sharing and improve the accuracy and stability of collaborative learning under acceptable communication load. © 2023 IEEE.

关键词： blockchain collaborative learning data sharing edge network Hyperledger Fabric

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Unveiling the Impact of Network Structure in Enhancing information Dissemination Efficiency for Military Command and Control : A SIR Modeling Study 9

Unveiling the Impact of Network Structure in Enhancing Infor...

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9th International Conference on Big Data and information Analytics, BigDIA 2023

作者： Liu, Shixian Jiang, Miao Chen, Honghui Li, Peihong Mao, Yanying Wang, Siyuan Liu, Dengfeng National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350330076

At the intersection of network science, communication models, and military command and control, this study explores the critical impact of network structure on the efficiency of information dissemination. We discuss how these findings can be applied to real-world military situations. Our research employs the SIR model and network simulation to replicate the information propagation process within the network. We discovered that various aspects of the network structure, such as node relationships, topology, and information propagation paths, exert a significant influence on the speed and scope of information dissemination. Furthermore, we explore the practical applications of these theories and findings in military command and control strategies. By considering network structure, we can design follow-up experiments tailored to specific network classifications within complex and realistic environments, aiming to optimize communication methods. In summary, this study introduces an innovative perspective in the military domain, with far-reaching implications for future research and the practical implementation of military command decision-making. © 2023 IEEE.

关键词： Network topology

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A Survey on Security of UAV Authentication Based on Blockchain under New C2 paradigm 9

A Survey on Security of UAV Authentication Based on Blockcha...

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9th International Conference on Big Data and information Analytics, BigDIA 2023

作者： Zheng, Yaling Liu, Yi Cheng, Ziwen Pan, Yongqi Zhu, Cheng National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350330076

Advancements in information technology and artificial intelligence have given rise to a new military command and control (C2) paradigm where unmanned aerial vehicles (UAVs) play a critical role. However, due to open wireless communication channels and evolving security challenges for UAVs in this paradigm, traditional centralized authentication methods are no longer sufficient. In this context, this paper conducts a systematic literature review (SLR) approach to explore the application of blockchain technology in UAV authentication. It categorizes and reviews relevant solutions published in recent years, and analyzes the advantages of blockchain in addressing centralized dependencies, cross-domain heterogeneity and other issues. The review finds dynamic management and efficiency improvement are key points needing attention. This paper provides valuable references on integrating UAV identity authentication with blockchain technology under new C2 paradigms. © 2023 IEEE.

关键词： Authentication

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Federated Learning in Heterogeneous Cloud Computing Environment 9

Federated Learning in Heterogeneous Cloud Computing Environm...

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9th International Conference on Big Data and information Analytics, BigDIA 2023

作者： Li, Yaoyu Jin, Shuai Zhu, Jianghan National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (纸本)9798350330076

This study examines key characteristics of cloud computing technology within the domain of federated learning, with the primary objective of exploring the principles concerning distributed computing acceleration ratio and storage input/output efficiency across various network segments. Combining the heterogeneous characteristics of cloud segments, we present a novel approach for distributed model training strategies by using the federated learning technique. This approach can keep the accuracy of the model, enhance training efficiency, and protect user privacy. The experiment demonstrates that the proposed strategy outperforms the baseline method, which does not account for variations in different segments. © 2023 IEEE.

关键词： Cloud computing

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CRP2-VCS: Contrast-Oriented Region-Based Progressive Probabilistic Visual Cryptography Schemes

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5501-5517页

作者： Wu, Xiaotian Song, Bofan Fang, Jia Yan, WeiQi Peng, Qing-Yu Jinan University College of Cyber Security The College of Information Science and Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou510632 China Auckland University of Technology Department of Computer and Information Systems Auckland1010 New Zealand Jinan University Dynamics and Space Science Department of Computer Science The Sino-French Joint Laboratory for Astrometry Guangzhou510632 China

Most visual cryptography schemes (VCSs) are condition-oriented which implies their designs focus on satisfying the contrast and security conditions in VCS. In this paper, we explore a new architecture of VCS: contrast-oriented region-based progressive probabilistic VCS (CRP2-VCS). The term contrast-oriented indicates the optimality of multi-contrast is taken into consideration when producing shadows. First of all, new requirements for CRP2-VCS, described by probabilities, are introduced. As a non-interference requirement is proposed, the secret interference problem in existing region-based progressive VCS can be avoided. Then, a construction of CRP2-VCS based on a multi-contrast-maximizing model is provided. The multi-contrast-maximizing problem is essentially a probabilistic VCS model that fuses region-based sharing, multi-contrast optimization, and general access structure (GAS) together. Finally, a Max-Min based technique is adopted to solve the multi-objective optimization problem. Moreover, to further boost the visual quality, the proposed method is extended to allow employing XOR operation for image recovery. Experimental results and comparisons are demonstrated to show the effectiveness and advantages, such as optimal visual quality, non-expansible shadow and GAS sharing policy, are provided by the proposed technique. © 1991-2012 IEEE.

关键词： Visualization Cryptography Probabilistic logic Interference Encoding Circuits and systems Polynomials Optimization Minimax techniques Computational modeling

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Modality Unified Attack for Omni-Modality Person Re-Identification

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IEEE Transactions on information Forensics and Security 2025年 20卷 5577-5587页

作者： Bian, Yuan Liu, Min Yi, Yunqi Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality re-id models unexplored. Due to the lack of knowledge about the specific type of model deployed in the target black-box surveillance system, we aim to generate modality unified AEs for omni-modality (single-, cross- and multi-modality) re-id models. Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models. A multi-modality model is adopted as the surrogate model, wherein the features of each modality are perturbed by metric disruption loss before fusion. To collapse the common features of omnimodality models, Cross Modality Simulated Disruption approach is introduced to mimic the cross-modality feature embeddings by intentionally feeding images to non-corresponding modality-specific subnetworks of the surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is devised to facilitate the attacker to comprehensively corrupt the informative content of person images by leveraging a multi modality feature collaborative metric disruption loss. Extensive experiments show that our MUA method can effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%, 49.0% and 62.7% mean mAP Drop Rate, respectively. © 2005-2012 IEEE.

关键词： Feature extraction Perturbation methods Measurement Generators Collaboration Robustness Identification of persons Data mining Closed box Training

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