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检索条件"机构=Data Science and System Security"
269 条 记 录,以下是21-30 订阅
排序:
DarkSAM: Fooling Segment Anything Model to Segment Nothing  38
DarkSAM: Fooling Segment Anything Model to Segment Nothing
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38th Conference on Neural Information Processing systems, NeurIPS 2024
作者: Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia
Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversar...
来源: 评论
Tracking Objects and Activities with Attention for Temporal Sentence Grounding  48
Tracking Objects and Activities with Attention for Temporal ...
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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
作者: Xiong, Zeyu Liu, Daizong Zhou, Pan Zhu, Jiahao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Peking University Wangxuan Institute of Computer Technology China
Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object... 详细信息
来源: 评论
Confident Information Coverage Reliability Evaluation for Sensor Networks of Openly Deployed ICP systems
IEEE Transactions on Industrial Cyber-Physical Systems
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IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 565-574页
作者: Chen, Suning Yi, Yuanyuan Yi, Lingzhi Liu, Shenghao Deng, Xianjun Xia, Yunzhi Fan, Xiaoxuan Yang, Laurence T. Park, Jong Hyuk Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security The School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan430074 China Zhongnan University of Economics and Law School of Information and Safety Engineering Wuhan430073 China Seoul National University of Technology Department of Computer Science and Engineering Seoul01811 Korea Republic of
Industrial Cyber-Physical (ICP) systems are integration of computation and physical processes to help achieve operational excellence. As sensors and actuators compose the openly deployed ICP systems and are often susc... 详细信息
来源: 评论
Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection  31
Towards Effective and Efficient Error Handling Code Fuzzing ...
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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024
作者: Chen, Kang Wen, Ming Jia, Haoxiang Wu, Rongxin Jin, Hai Wuhan430074 China Jin YinHu Laboratory Wuhan430074 China School of Informatics Xiamen University Xiamen361005 China School of Computer Science and Technology HUST Wuhan430074 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China
Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bu... 详细信息
来源: 评论
An SGX and Blockchain-Based system for Federated Learning in Classifying COVID-19 Images  21
An SGX and Blockchain-Based System for Federated Learning in...
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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023
作者: Qin, Sihang Gu, Xianjun Jun, Xing Li, Haitao Huang, Mengqi Dai, Weiqi Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China State Grid Wuhan Power Supply Company China
Federated learning, an emerging distributed learning framework, has gained prominence recently. However, two challenges arise when applying federated learning to medical data. The first challenge stems from the inabil... 详细信息
来源: 评论
Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee
IEEE Transactions on Artificial Intelligence
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IEEE Transactions on Artificial Intelligence 2024年 第12期5卷 6369-6384页
作者: Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada
Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private... 详细信息
来源: 评论
Towards Demystifying Android Adware: dataset and Payload Location  39
Towards Demystifying Android Adware: Dataset and Payload Loc...
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39th ACM/IEEE International Conference on Automated Software Engineering Workshops, ASEW 2024
作者: Wang, Chao Liu, Tianming Zhao, Yanjie Zhang, Lin Du, Xiaoning Li, Li Wang, Haoyu Monash University Clayton Australia Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China The National Computer Emergency Response Beijing China Beihang University Beijing China
Adware represents a pervasive threat in the mobile ecosystem, yet its inherent characteristics have been largely overlooked by previous research. This work takes a crucial step towards demystifying Android adware. We ... 详细信息
来源: 评论
On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities  24
On the Effectiveness of Function-Level Vulnerability Detecto...
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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024
作者: Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China
Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as ... 详细信息
来源: 评论
Real-time stealth transmission via dither-based bias control
Real-time stealth transmission via dither-based bias control
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2023 Conference on Lasers and Electro-Optics, CLEO 2023
作者: Wang, Yuanxiang Shao, Weidong Zhong, Linsheng Dai, Xiaoxiao Yang, Qi Deng, Lei Liu, Deming Cheng, Mengfan Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China
We report a real-time 1 kbps stealthy transmission in the 10 Gbps QPSK public communication. The stealth data is embedded in dither signals of bias control. The scheme is compatible with existing optical transmission ... 详细信息
来源: 评论
Backdoor Attacks on Bimodal Salient Object Detection with RGB-Thermal data  24
Backdoor Attacks on Bimodal Salient Object Detection with RG...
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32nd ACM International Conference on Multimedia, MM 2024
作者: Yin, Wen Zhu, Bin Benjamin Xie, Yulai Zhou, Pan Feng, Dan School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Microsoft Corporation Beijing China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China
RGB-Thermal Salient Object Detection (RGBT-SOD) plays a critical role in complex scene recognition applications, such as autonomous driving. However, security research in this domain is still in its infancy. This pape... 详细信息
来源: 评论