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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Fang, Xinyue Liu, Jianwei Chen, Yike Han, Jinsong Ren, Kui Chen, Gang Zhejiang University School of Cyber Science and Technology China ZJU-Hangzhou Global Scientific and Technological Innovation Center China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China Zhejiang University College of Computer Science and Technology China

ISBN: (纸本)9798350334142

For safety guard and crime prevention, video surveillance systems have been pervasively deployed in many security-critical scenarios, such as the residence, retail stores, and banks. However, these systems could be infiltrated by the adversary and the video streams would be modified or replaced, i.e., under the video forgery attack. The prevalence of Internet of Things (IoT) devices and the emergence of Deepfake-like techniques severely emphasize the vulnerability of video surveillance systems under such attacks. To secure existing surveillance systems, in this paper we propose a vision-WiFi cross-modal video forgery detection system, namely WiSil. Leveraging a theoretical model based on the principle of signal propagation, WiSil constructs wave front information of the object in the monitoring area from WiFi signals. With a well-designed deep learning network, WiSil further recovers silhouettes from the wave front information. Based on a Siamese network-based semantic feature extractor, WiSil can eventually determine whether a frame is manipulated by comparing the semantic feature vectors extracted from the video's silhouette with those extracted from the WiFi's silhouette. Extensive experiments show that WiSil can achieve 95% accuracy in detecting tampered frames. Moreover, WiSil is robust against environment and person changes. © 2023 IEEE.

关键词： Retail stores

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Introducing on-chain graph data to consortium blockchain for commercial transactions

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Frontiers of computer science 2024年第2期18卷 255-257页

作者： Pengyi ZHANG Yuchen YUAN Jie SONG Yu GU Qiang QU Yongjie BAI Software College Northeastern UniversityShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China Blockchain Lab Huawei Cloud Computing Technologies Co.Ltd.Shenzhen 518101China

1 Introduction A blockchain can be referred to as a distributed ledger[1].Nowadays,according to our previous work[2],the graphrelated blockchain grows efficiently in both academic studies and commercial *** data,algor... 详细信息

关键词： transactions ledge commercial

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Boosting Transferability of Adversarial Examples by Joint Training and Dual Feature Mixup

Boosting Transferability of Adversarial Examples by Joint Tr...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Mengmeng Tang Shuhong Chen Guojun Wang Hanjun Li Zhuyi Yao Sheng Wen School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Blockchain Innovation Lab Swinburne University of Technology Victoria Australia

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The transferability of adversarial examples is pivotal in black-box attacks on deep learning models. The existing transfer-based attacks typically rely on a single data augmentation technique, which hampers the diversity of generated adversarial examples. Additionally, applying a single adversarial noise generation path may impose limitations on the perturbation strength of the generated noise, thereby compromising the transferability of these examples. To address these issues, we propose a framework called Joint Training and Dual Feature Mixiup (JFM), which comprises the dual feature mixup module and joint training module. The dual feature mixup module performs feature mixing between benign and augmented images, enabling the comprehensive extraction of benign example features and enhancing the diversity of adversarial examples. Furthermore, the joint training module designs a dual-path prediction loss function that incorporates both the loss between mixed feature examples and benign examples, as well as the loss between augmented examples and benign examples, thereby enhancing the transferability of the generated examples. Empirical evaluation of the ImageNet-compatible dataset demonstrates that our JFM method exhibits superior attack capability and significantly outperforms state-of-the-art methods.

关键词： Training Deep learning Privacy Perturbation methods Computational modeling Noise Closed box Feature extraction Data augmentation Security

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FedDCT: A Dynamic Cross-Tier Federated Learning Framework in Wireless Networks 18th

FedDCT: A Dynamic Cross-Tier Federated Learning Framework in...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Xian, Youquan Gan, Xiaoyun Yao, Chuanjian Li, Dongcheng Wang, Peng Liu, Peng Zhao, Ying Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin54104 China School of Computer Science and Engineering Guangxi Normal University Guilin54104 China School of Business Guilin University of Electronic Technology Guilin54104 China

ISBN: (纸本)9783031714634

Federated Learning (FL), as a privacy-preserving machine learning paradigm, trains a global model across devices without exposing local data. However, resource heterogeneity and inevitable stragglers in wireless networks severely impact the efficiency and accuracy of FL training. In this paper, we propose a novel Dynamic Cross-Tier Federated Learning framework (FedDCT). Firstly, we design a dynamic tiering strategy that dynamically partitions devices into different tiers based on their response times and assigns specific timeout thresholds to each tier to reduce single-round training time. Then, we propose a cross-tier device selection algorithm that selects devices that respond quickly and are conducive to model convergence to improve convergence efficiency and accuracy. Experimental results demonstrate that the proposed approach under wireless networks outperforms the baseline approach, with an average reduction of 54.7% in convergence time and an average improvement of 1.83% in convergence accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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A multiple sensitive attributes data publishing method with guaranteed information utility

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CAAI Transactions on Intelligence technology 2023年第2期8卷 288-296页

作者： Haibin Zhu Tong Yi Songtao Shang Minyong Shi Zhucheng Li Wenqian Shang Department of Computer Science and Mathematics Nipissing UniversityNorth BayOntarioCanada Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilinChina Guangxi Key Lab of Multi‐Source Information Mining and Security Guangxi Normal UniversityGuilinChina School of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhouChina School of Computer Science Communication University of ChinaBeijingChina Business College Beijing Union UniversityBeijingChina

Data publishing methods can provide available information for analysis while preserving *** multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression *** category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for ***,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information *** acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information *** experimental results verify the practice of the proposed data publishing method with multiple sensitive *** proposed method can guarantee information utility when compared with previous ones.

关键词： data analysis data privacy security of data

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Two-Stage, Global-Local Approach for Cell Nuclei Segmentation in Histopathology Images 8

Two-Stage, Global-Local Approach for Cell Nuclei Segmentatio...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Shakya, Amit Saharan, Revat Gupta, Chetan Kumar, Rupesh Sharma, Lalit Naidu, Srivatsava Murala, Subrahmanyam Arora, Chetan Yamaha Motor Solutions Emerging Technology and Innovation Lab India Indian Institute of Technology Ropar Department of Biomedical Engineering India School of Computer Science and Statistics Trinity College Dublin Ireland Indian Institute of Technology Delhi Department of Computer Science and Engineering India

ISBN: (纸本)9798331531539

Effective management of high-resolution, and spatially wide contextual cues is fundamental to the accurate semantic segmentation. Traditional approaches like multi-resolution feature maps, and skip-connection are effective but require changes in the backbone architecture, restricting utilization of newer models and architectures for the problem. In this work we propose an architecture-agnostic, two-stage, global-local frame-work, called GoLo, for the semantic segmentation, which can use arbitrary semantic segmentation models within its two stages. We focus on segmenting cell nuclei in histopathology image analysis, where accurate segmentation of cell nuclei boundaries is one of the key issues. The proposed framework consists of first stage with Global and second stage with Local learning approach. The first stage is proposed to process the image globally and provide the coarse nuclei segmentation map. In the second stage, to process the image locally, coarse segmentation map and input image is first converted into patches. These patches are then fed as input to the second stage to get the fine- grained segmentation map. Both stages are trained with a combination of dice and binary cross entropy loss. To show the effectiveness of our approach, we test 4 state-of-the-art segmentation architectures (ACC-UNet, UCTransnet, Swin-UNet, and Vanilla U-Net), on 4 different benchmark datasets (MoNuSeg, CPM-17, CoNSep, and TNBC). We evaluate performance of each technique before and after using our framework. We report an average improvement of 4.82 % in mIoU, and 4.52% mDSC score, across techniques, and datasets. © 2024 IEEE.

关键词： Semantic Segmentation

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BuildEnVR: An Immersive Analysis System for Environmental Field 30

BuildEnVR: An Immersive Analysis System for Environmental Fi...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhou, Zhenghan Liu, Kebin Xie, Yantong Jin, Hangxu Liu, Shi Wang, Ruiqing Zhao, Haitian Lin, Borong Mu, Xiaofang Qi, Hui Tsinghua University Global Innovation Exchange China Fuzhou Fuyao Institute for Advanced Study China Carnegie Mellon University School of Architecture United States Tsinghua University School of Architecture China Taiyuan Normal University College of Computer Science and Technology China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology China

ISBN: (纸本)9798331515966

Amidst global warming and escalating extreme weather events, indoor environmental quality's impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR's intuitiveness and precision, and it is suitable for both experts and general users. © 2024 IEEE.

关键词： Indoor air pollution

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CNNRF-Ensemble: A Multifaced Approach For Predicting White Spot Syndrome Virus In Shrimp Farming 4

CNNRF-Ensemble: A Multifaced Approach For Predicting White S...

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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Tanvir, Kazi Al Huda, Sadi Opee, Shaiful Ajam Kabir, Sayem Ali, Asraf Bhuiyan, Touhid School of Advanced Sciences Vellore Institute of Technology Department of Mathematics Tamil Nadu Vellore India Department of Computer Science Dhaka Bangladesh Artificial Intelligence Research and Innovation Lab Dhaka Bangladesh School of Information Technology Washington University of Science and Technology AlexandriaVA22314 United States

ISBN: (纸本)9798331530938

Shrimp farming has traditionally served as a crucial source of seafood and revenue for coastal countries, filling the rapid growth demand, especially in Bangladesh. However, the White Spot Syndrome Virus(WSSV) creates and constantly threatens the global shrimp population, impacting the economic and ecological systems. Recently, researchers have tried to predict disease, but no studies have achieved significant accuracy using ML and DL algorithms. In this study, we have proposed an optimized CNNRF-Ensemble model for predicting WSSV using multifaceted data, including geographical features, environmental attributes, farm characteristics, operational practices, and socioeconomic indicators. Our proposed model achieved 95.714% accuracy in sequential model and afterwards, combining with RandomForestClassifier in Ensemble learning we obtained 99.99% accuracy. Our study suggested deep learning as an accurate, easy and low-cost method to predict shrimp WSSV, which helps to predict viruses and create awareness of shrimp firming. © 2025 IEEE.

关键词： Shellfish

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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BackLip: Passphrase-Independent Lip-reading User Authentication with Backscatter Signals 31

BackLip: Passphrase-Independent Lip-reading User Authenticat...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Tian, Ye Zhou, Hao Du, Haohua Xu, Chenren Hou, Jiahui Ren, Dong Li, Xiang-Yang School of Computer Science and Technology University of Science and Technology of China China University of Science and Technology of China Cas Key Lab of Wireless-Optical Communications China USTC-Deqing Alpha Innovation Research Institute Zhejiang China School of Cyber Science and Technology Beihang University China Peking University Department of Computer Science and Technology China

ISBN: (纸本)9798350399738

User authentication is essential for threat defense and data protection. Existed authentication systems have some known limitations, such as spoofing attacks, privacy leakage, and user-unfriendliness. In this paper, we propose BackLip, a novel anti-spoofing authentication system based on lip reading. We employ Wi-Fi backscatter-based technology to recognize users lip reading due to its various advantages, e.g. privacy protection, low power consumption, and low cost. Our system is touch-free and passphrase-independent, making it user-friendly, especially for the elderly and disabled. We first filter out irrelevant interference and enhance the signal-to-noise ratio of lip-reading signals by modulating the backscatter tags and constructing a series of suitable filters. Then, we study the energy distribution and steady-state characteristics of the backscattered signal caused by lip-reading movement at different frequencies to extract passphrase-independen lip-reading fingerprints. We build a theoretical model to analyze and prove the feasibility of our method and design an adaptive correction method to resist the interference caused by distance and angle changes. Additionally, we propose an adaptive segmentation algorithm to label lip-reading motions automatically. Extensive experiments demonstrate that BackLip has an average accuracy of 92.1% and is improved to 96.5% when users use the same passphrases. © 2023 IEEE.

关键词： Signal to noise ratio

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