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arXiv 2022年

作者： Zhou, Li Li, Yujun Li, Jin Huang, Minhuan Nie, Yuanping Liu, Yiwei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Science and Technology on Information System Security Chengdu China National Key Laboratory of Science and Technology on Information System Security Beijing China

With the continuous extension of the Industrial Internet, cyber incidents caused by software vulnerabilities have been increasing in recent years. However, software vulnerabilities detection is still heavily relying on code review done by experts, and how to automatedly detect software vulnerabilities is an open problem so far. In this paper, we propose a novel solution named GraphEye to identify whether a function of C/C++ code has vulnerabilities, which can greatly alleviate the burden of code auditors. GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality. Hence, detecting vulnerable functions is attributed to the graph classification *** is comprised of VecCPG and GcGAT. VecCPG is a vectorization for the code property graph, which is proposed to characterize the key syntax and semantic features of the corresponding source code. GcGAT is a deep learning model based on the graph attention graph, which is proposed to solve the graph classification problem according to VecCPG. Finally, GraphEye is verified by the SARD Stack-based Buffer Overflow, Divide-Zero, Null Pointer Deference, Buffer Error, and Resource Error datasets, the corresponding F1 scores are 95.6%, 95.6%,96.1%,92.6%, and 96.1% respectively, which validate the effectiveness of the proposed solution. © 2022, CC BY.

关键词： Deep learning

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Spiderweb Protector: Biomimetic Defense Against Targeted Attacks on Graph Neural Networks

Spiderweb Protector: Biomimetic Defense Against Targeted Att...

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International Conference on Wavelet Active Media technology and information Processing (ICWAMTIP)

作者： Zeyu Zhou Yuanyuan Huang Yongli Wang Siyu Li Jiazhong Lu Peilin He Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China Dazhou key Laboratory of Government data security Dazhou China Department of Informatics and Networked Systems University of Pittsburgh Pittsburgh United States

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

Graph Neural Networks (GNNs) have emerged as powerful tools across diverse applications, including social networks, bioinformatics, and recommendation systems. However, their susceptibility to adversarial attacks, particularly targeted attacks, poses a significant challenge. Targeted attacks exploit the message-passing mechanism of GNNs to induce severe performance degradation, even with minimal perturbations. Inspired by the self-repair and adaptive behaviors of spider webs, this paper introduces SpiderWeb Protector, a biomimetic defense strategy designed to enhance GNN robustness. The proposed method integrates three core components: Community-Centric Graph Update, which selectively reinforces key graph structures; Topology Feature Merging, which improves feature representation by incorporating structural insights; and Edge Pruning, which dynamically removes adversarial connections. Comprehensive experiments on three datasets (Cora, Citeseer and Cora_ml) and two GNN models (GCN and GIN) demonstrate the superior performance of SpiderWeb Protector against both direct and influence-targeted attacks, achieving state-of-the-art results.

关键词： Technological innovation Social networking (online) Biomimetics Merging Refining Graph neural networks Robustness Topology Recommender systems Resilience

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AI-Enabled Multimedia Semantic Communications

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IEEE Communications Magazine 2025年第6期63卷 66-73页

作者： Li, Chenxing Duan, Yiping Wang, Liejun Tao, Xiaoming Tsinghua University Department of Electronic Engineering China State Key Laboratory of Space Network China Beijing National Research Center for Information Science and Technology China School of Computer Science and Technology Xinjiang University China

With increasing demand for mobile communication and the development of artificial intelligence, the development of the sixth-generation mobile communication network (6G) will be oriented toward more intelligent and multimodal services. It is necessary to consider the deep integration of information, communication, and data technologies and build a full life cycle resource management framework for computing, data, artificial intelligence models, and communication. To address the requirements of 6G intelligent applications and multimodal services, this article refers to the visual cognitive mechanism of the human brain to improve multimedia representation and communication efficiency and studies the architecture of semantic communication systems, privacy protection mechanisms, and quality evaluation and guarantee mechanisms. The "semantic-network-semantic" architecture was established to integrate semantic computing, intelligent communication, and endogenous security to improve transmission efficiency, service quality, and security credibility. Through prior semantic knowledge, semantic representation, and encoding transmission methods, the quality of service of semantic communication is improved. To build an efficient 6G semantic communication paradigm and system, the communication mode should be changed, and new ideas for future mobile communication should be provided. © 1979-2012 IEEE.

关键词： Multimedia services

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GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network 6

GraphEye: A Novel Solution for Detecting Vulnerable Function...

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6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Zhou, Li Huang, Minhuan Li, Yujun Nie, Yuanping Li, Jin Liu, Yiwei School Of Information And Communication Engineering University Of Electronic Science And Technology Of China Chengdu China National Key Laboratory Of Science And Technology On Information System Security Beijing China School Of Computer Science And Engineering University Of Electronic Science And Technology Of China Chengdu China

ISBN: (纸本)9781665418157

With the continuous extension of the Industrial Internet, cyber incidents caused by software vulnerabilities have been increasing in recent years. However, software vulnerabilities detection is still heavily relying on code review done by experts, and how to automatically detect software vulnerabilities is an open problem so far. In this paper, we propose a novel solution named GraphEye to identify whether a function of C/C++ code has vulnerabilities, which can greatly alleviate the burden of code auditors. GraphEye is originated from the observation that the code property graph of a non-vulnerable function naturally differs from the code property graph of a vulnerable function with the same functionality. Hence, detecting vulnerable functions is attributed to the graph classification problem. GraphEye is comprised of VecCPG and GcGAT. VecCPG is a vectorization for the code property graph, which is proposed to characterize the key syntax and semantic features of the corresponding source code. GcGAT is a deep learning model based on the graph attention networks, which is proposed to solve the graph classification problem according to VecCPG. Finally, GraphEye is verified by the SARD Stack-based Buffer Overflow, Divide-Zero, Null Pointer Deference, Buffer Error, and Resource Error datasets, the corresponding F1 scores are 95.6%, 95.6%, 96.1%, 92.6%, and 96.1% respectively, which validate the effectiveness of the proposed solution. © 2021 IEEE.

关键词： Semantics

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Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second

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Light(science & Applications) 2025年第4期14卷 931-947页

作者： Jiehua Zhou Liye Mei Mingjie Yu Xiao Ma Dan Hou Zhuo Yin Xun Liu Yan Ding Kaining Yang Ruidong Xiao Xiandan Yuan Yueyun Weng Mengping Long Taobo Hu Jinxuan Hou Yu Xu Liang Tao Sisi Mei Hui Shen Yaxiaer Yalikun Fuling Zhou Liang Wang Du Wang Sheng Liu Cheng Lei The Institute of Technological Sciences Wuhan UniversityWuhan 430072China School of Computer Science Hubei University of TechnologyWuhan 430068China Division of Materials Science Nara Institute of Science and TechnologyTakayama-cho 8916-5Japan School of Science Hubei University of TechnologyWuhan 430068China Department of Pathology Peking University Cancer HospitalBeijing 100142China Department of Breast Surgery Peking University People’s HospitalBeijing 100044China Department of Thyroid and Breast Surgery Zhongnan HospitalWuhan UniversityWuhan 430071China Department of Radiation and Medical Oncology Zhongnan HospitalWuhan UniversityWuhan 430071China People’s Hospital of Anshun City Guizhou Province Anshun 561000China Department of Hematology Zhongnan HospitalWuhan UniversityWuhan 430071China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074China Suzhou Institute of Wuhan University Suzhou 215000China Shenzhen Institute of Wuhan University Shenzhen 518057China

Imaging flow cytometry(IFC)combines the imaging capabilities of microscopy with the high throughput of flow cytometry,offering a promising solution for high-precision and high-throughput cell analysis in fields such as biomedicine,green energy,and environmental ***,due to limitations in imaging framerate and realtime data processing,the real-time throughput of existing IFC systems has been restricted to approximately 1000-10,000 events per second(eps),which is insufficient for large-scale cell *** this work,we demonstrate IFC with real-time throughput exceeding 1,000,000 eps by integrating optical time-stretch(OTS)imaging,microfluidic-based cell manipulation,and online image *** flowing at speeds up to 15 m/s are clearly imaged with a spatial resolution of 780 nm,and images of each individual cell are captured,stored,and *** capabilities and performance of our system are validated through the identification of malignancies in clinical colorectal *** work sets a new record for throughput in imaging flow cytometry,and we believe it has the potential to revolutionize cell analysis by enabling highly efficient,accurate,and intelligent measurement.

关键词： real time data processing flow cytometryoffering spatial resolution imaging flow cytometry ifc combines high throughput cell analysis cell analysis imaging flow cytometry optical time stretch imaging

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Privacy-Preserving IoT Image Transmission: Multi-Stage SVD Data Embedding and Heatmap Alignment

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IEEE Internet of Things Journal 2025年

作者： Du, Kaixin Ma, Bin Yang, Meihong Wang, Xiaoyu Li, Xiaolong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250101 China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

The images transmitted by IoT devices, particularly those used for surveillance or sensor data, are vulnerable to malicious screenshots and unauthorized access, leading to potential privacy breaches. To address this, we propose a multi-stage Singular Value Decomposition (SVD)-based robust data-hiding scheme for JPEG images aimed at mitigating screenshot attacks. The method exploits the decorrelation properties of the Discrete Cosine Transform (DCT) to preprocess the carrier image, facilitating the selection of specific frequency coefficients. These coefficients undergo a dual-stage SVD transformation, where dimensionality reduction reduces the impact of noise from non-critical image regions. Additionally, we optimize Grad-CAM heatmap generation to better align with human visual perception, enabling the identification of stable and reliable feature regions for embedding secret information. This approach ensures that the visual integrity of the carrier image is maintained while preserving the legibility of the embedded information, even under *** method enhances both the visual fidelity of the carrier image and the robustness of the embedded information. Experimental results demonstrate that the proposed scheme outperforms existing methods, achieving at least a 5% improvement in confidential information extraction accuracy and a data extraction rate exceeding 95% across screenshot angles ranging from -40∘ to 40∘. Extensive evaluations confirm the superior performance, efficiency, and security of our method in mitigating screenshot attacks, showcasing its broad applicability to various image formats and resilience to distortions. © 2014 IEEE.

关键词： Discrete cosine transforms

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Atomic Smart Contract Interoperability with High Efficiency via Cross-Chain Integrated Execution

arXiv

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arXiv 2025年

作者： Yin, Chaoyue Li, Mingzhe Zhang, Jin Lin, You Wei, Qingsong Goh, Siow Mong Rick Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Singapore

With the development of Ethereum, numerous blockchains compatible with Ethereum’s execution environment (i.e., Ethereum Virtual Machine, EVM) have emerged. Developers can leverage smart contracts to run various complex decentralized applications on top of blockchains. However, the increasing number of EVM-compatible blockchains has introduced significant challenges in cross-chain interoperability, particularly in ensuring efficiency and atomicity for the whole cross-chain application. Existing solutions are either limited in guaranteeing overall atomicity for the cross-chain application, or inefficient due to the need for multiple rounds of cross-chain smart contract execution. To address this gap, we propose IntegrateX, an efficient cross-chain interoperability system that ensures the overall atomicity of cross-chain smart contract invocations. The core idea is to deploy the logic required for cross-chain execution onto a single blockchain, where it can be executed in an integrated manner. This allows cross-chain applications to perform all cross-chain logic efficiently within the same blockchain. IntegrateX consists of a cross-chain smart contract deployment protocol and a cross-chain smart contract integrated execution protocol. The former achieves efficient and secure cross-chain deployment by decoupling smart contract logic from state, and employing an off-chain crosschain deployment mechanism combined with on-chain crosschain verification. The latter ensures atomicity of cross-chain invocations through a 2PC-based mechanism, and enhances performance through transaction aggregation and fine-grained state lock. We implement a prototype of IntegrateX. Extensive experiments demonstrate that it reduces up to 61.2% latency compared to the state-of-the-art baseline while maintaining low gas consumption. Copyright © 2025, The Authors. All rights reserved.

关键词： Ethereum

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DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance Prior

arXiv

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arXiv 2025年

作者： Li, Mingrui Liu, Shuhong Deng, Tianchen Wang, Hongyu the Department of Computer Science Dalian University of Technology Dalian China the Department of Mechano-informatics Information Science and Technology The University of Tokyo Tokyo Japan Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education China

Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. To address these challenges, we introduce DenseSplat, the first SLAM system that effectively combines the advantages of NeRF and 3DGS. DenseSplat utilizes sparse keyframes and NeRF priors for initializing primitives that densely populate maps and seamlessly fill gaps. It also implements geometry-aware primitive sampling and pruning strategies to manage granularity and enhance rendering efficiency. Moreover, DenseSplat integrates loop closure and bundle adjustment, significantly enhancing frame-to-frame tracking accuracy. Extensive experiments on multiple large-scale datasets demonstrate that DenseSplat achieves superior performance in tracking and mapping compared to current state-of-the-art methods. © 2025, CC BY.

关键词： Real time systems

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Highly Secure Self-homodyne Coherent system Empowered by Quantum Noise Stream Cipher

Highly Secure Self-homodyne Coherent System Empowered by Qua...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Du, Haoze Lei, Chao He, Zonglong Zhang, Mingming Lin, Rui Yang, Chen Zhao, Zhiyong Tang, Ming Department of Electrical Engineering Chalmers University of Technology Göteborg41380 Sweden School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Department of Microtechnology and Nanoscience Chalmers University of Technology Göteborg41380 Sweden National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China State Key Laboratory of Optical Fiber and Cable Manufacture Technology Yangtze Optical Fibre and Cable Joint Stock Limited Company Wuhan430080 China

200Gb/s per spatial channel quantum noise stream cipher (QNSC) is experimentally demonstrated in 3km self-homodyne coherent (SHC) transmission system. A security level of (32761)^20 is obtained with 3dB penalty. ©... 详细信息

ISBN: (纸本)9798350379266

关键词： Quantum noise

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Virtual-Source and Virtual-Swarm-Based Particle Swarm Optimizer for Large-Scale Multi-Source Location via Robot Swarm

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Zhang, JunQi Lin, YuXuan Zhou, MengChu Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Multi-source location is a significant application in the field of robot swarm and is required to find all sources whose number and distribution are unknown in advance. With few parameters and fast search, Particle Swarm Optimizer (PSO) variants that have certain grouping capability have been applied to address multi-source location problems by dividing a swarm such that every source has robots to locate. However, they are difficult to predetermine the exact number of groups, require a big number of robots, and are easily trapped in the no-signal areas when the proportion of no-signal areas is high. This work proposes a Virtual-source and Virtual-swarm-based PSO (VVPSO) to divide a search area into multiple cells equally, each of which has a virtual source in its center. Then, instead of robots grouping, only one group of robots is employed to traverse all virtual sources, and search their corresponding cells to locate real sources by a new PSO called Real-virtual mapping PSO (RMPSO). RMPSO asymmetrically maps a robot into a particle swarm with multiple virtual particles to perform PSO, which greatly reduces the requirements for the number of robots. Experimental results show that VVPSO has great search scalability and can solve large-scale multi-source location problems than two state-of-the-art grouping methods and three representative multimodal PSO variants, even with only one robot. Hence, this work greatly advances the field of multi-source location by using mobile robot swarm. IEEE

关键词： Robots

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