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2021 International Conference on Advanced Electrical Equipment and Reliable Operation, AEERO 2021

作者： Zhang, Zhixian Chen, Weigen Liu, Fan Wu, Kejie Song, Yuxuan Lei, Jiali Yang, Tianhe Chongqing University State Key Laboratory of Power Transmission Equipment System Security and New Technology Chongqing China State Grid Hubei Electric Power Research Institute Hubei Wuhan China Chongqing University Chongqing China

ISBN: (纸本)9781665402644

To simplify the installation and wiring of sensors inside transformers and the structure of light source and demodulation system of fiber optic sensing system, a Fabry-Perot (F-P) interference-based fiber optic sensing method for partial discharge and fast temperature change in power transformers is proposed. The joint sensing of partial discharge and oil temperature change is realized based on narrowband laser and photodetector. A fiber optic F-P ultrasonic sensor with resonant frequency at 153 kHz was designed using a vibration model of a single-sided contact diaphragm with liquid to achieve sensing of 191.23 pC in oil. Based on the same sensor, a temperature change detection method based on the DC component of the F-P cavity output light intensity was investigated. The experimental results show that the DC component of the sensor output light intensity will periodically appear as a Gaussian pulse reversed along the X-axis which will pass through a minimum point when the temperature changes. The faster the temperature changes, the more times the DC component passes the minimum point in a period of time. Therefore, the number of the DC component of the sensor's output light intensity passing the minimum point in a period of time can be counted to reflect the rapidity of the internal oil temperature change of the transformer. © 2021 IEEE.

关键词： Partial discharges

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Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 label responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are available at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

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You Are Catching My Attention: Are Vision Transformers Bad Learners under Backdoor Attacks?

You Are Catching My Attention: Are Vision Transformers Bad L...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zenghui Yuan Pan Zhou Kai Zou Yu Cheng 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 Protagolabs Inc Microsoft Research

Vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the process of industrializing ViTs, backdoor attacks have brought severe challenges to security. The success of ViTs benefits from the self-attention mechanism. However, compared with CNNs, we find that this mechanism of capturing global information within patches makes ViTs more sensitive to patch-wise triggers. Under such observations, we delicately design a novel backdoor attack framework for ViTs, dubbed BadViT, which utilizes a universal patch-wise trigger to catch the model's attention from patches beneficial for classification to those with triggers, thereby manipulating the mechanism on which ViTs survive to confuse itself. Furthermore, we propose invisible variants of BadViT to increase the stealth of the attack by limiting the strength of the trigger perturbation. Through a large number of experiments, it is proved that BadViT is an efficient backdoor attack method against ViTs, which is less dependent on the number of poisons, with satisfactory convergence, and is transferable for downstream tasks. Furthermore, the risks inside of ViTs to backdoor attacks are also explored from the perspective of existing advanced defense schemes.

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Flexible and self-powered blinking and eyeball movements sensing based on electrospun lead-free piezoelectric nanofibers

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Science China Materials 2024年第12期67卷 3948-3955页

作者： Lei Jiang Xu Mei Tingting Shao Xinran Yang Wuyang Long Meilin Wan Yijing Fan Juan Xiong Xianghui Zhang Yongming Hu Haoshuang Gu Zhao Wang Hubei Key Laboratory of Micro&Nano Electric Materials and Devices School of MicroelectronicsHubei UniversityWuhan 430062China Key Laboratory of Intelligent Sensing System and Security Ministry of EducationHubei UniversityWuhan 430062China School of Chemistry and Environmental Engineering Wuhan University of BioengineeringWuhan 430415China

Potassium sodium niobate(KNN)-based nanofibers could combine piezoelectric properties with exceptional flexibility and biocompatibility,making them highly promising for flexible sensors in electronic skin and wearable ***,the suboptimal piezoelectric performance limits their sensitivity in detecting minute human body motions,such as blinking and eye ***,Li and Ta-doped KNN nanofibers were fabricated via an electro-spinning ***-doping with 6%Li at the A-site and 30%Ta at the B-site induces lattice distortion and an orthorhombic(O)to tetragonal(T)phase transition in the electrospun nanofibers,resulting in a significantly enhanced piezoelectric response,with an average d_(33)*value reaching 110.7 pm/*** outstanding piezoelectric response gives rise to a remarkable sensitivity(0.3365 V/(N cm^(2)))in a self-powered flexible pressure sensor based on the doped KNN nanofibers,encapsulated in a polydimethylsiloxane *** sensors,when attached to the temple regions,can detect tiny facial motions induced by blinking,enabling the distinction of abnormal blinking patterns associated with mental fatigue and excessive eye ***,they support real-time,continuous,and unobtrusive eyeball tracking,highlighting their potential as critical components in human-computer interaction and artificial intelligence applications.

关键词： flexible sensors eye movement sensing piezoelectric nanofibers potassium sodium niobate electrospinning

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Four-dimensional Floquet topological insulator with an emergent second Chern number

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Physical Review B 2024年第12期109卷 125303-125303页

作者： Zheng-Rong Liu Rui Chen Bin Zhou Department of Physics Hubei University Wuhan 430062 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China

Floquet topological insulators have been widely investigated in lower-dimensional systems. However, Floquet topological insulators induced by time-periodic driving in higher-dimensional systems remain unexplored. In this work, we study the effects of time-periodic driving in a four-dimensional (4D) normal insulator, focusing on topological phase transitions at the resonant quasienergy gap. We consider two types of time-periodic driving, including a time-periodic onsite potential and a time-periodic vector potential. We reveal that both types of time-periodic driving can transform the 4D normal insulator into a 4D Floquet topological insulator characterized by an emergent second Chern number. Moreover, it is found that the topological phase of the 4D system can be modulated by tuning the strength and frequency of the time-periodic driving. Our work will be helpful for the future investigation of Floquet topological insulators in higher dimensions.

关键词： Topological insulators Topological phase transition Topological phases of matter Floquet systems

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A First Look at LLM-powered Smartphones

A First Look at LLM-powered Smartphones

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IEEE/ACM International Conference on Automated Software Engineering - Workshops (ASE Workshops)

作者： Liangxuan Wu Yanjie Zhao Chao Wang Tianming Liu Haoyu Wang 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

ISBN: (数字)9798400712494

ISBN: (纸本)9798331523169

The integration of Large Language Models (LLMs) into edge devices such as smartphones represents a significant leap in mobile technology, promising enhanced user experiences and novel functionalities. This paper presents a first look at LLM-powered smartphones, addressing four key aspects: the current market landscape, core functions enabled by integrated LLMs, potential security risks, and user perceptions. The findings reveal a rapidly evolving market with major manufacturers competing to integrate LLMs, innovative features that improve user interaction, significant security challenges, and mixed user perceptions that balance enthusiasm for new capabilities with privacy concerns. This study contributes to understanding LLM integration in mobile devices and its implications for users, manufacturers, and the broader technological landscape.

关键词： Industries Privacy Technological innovation Ethics Large language models Conferences User experience security Smart phones Software engineering

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding

Tracking Objects and Activities with Attention for Temporal ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zeyu Xiong Daizong Liu Pan Zhou Jiahao Zhu 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 Wangxuan Institute of Computer Technology Peking University

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-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets’ behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed.

关键词： Solid modeling Target tracking Three-dimensional displays Grounding Benchmark testing Signal processing Feature extraction

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3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack

3DHacker: Spectrum-based Decision Boundary Generation for Ha...

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International Conference on Computer Vision (ICCV)

作者： Yunbo Tao Daizong Liu Pan Zhou Yulai Xie Wei Du Wei Hu 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 Wangxuan Institute of Computer Technology Peking University

With the maturity of depth sensors, the vulnerability of 3D point cloud models has received increasing attention in various applications such as autonomous driving and robot navigation. Previous 3D adversarial attackers either follow the white-box setting to iteratively update the coordinate perturbations based on gradients, or utilize the output model logits to estimate noisy gradients in the black-box setting. However, these attack methods are hard to be deployed in real-world scenarios since realistic 3D applications will not share any model details to users. Therefore, we explore a more challenging yet practical 3D attack setting, i.e., attacking point clouds with black-box hard labels, in which the attacker can only have access to the prediction label of the input. To tackle this setting, we propose a novel 3D attack method, termed 3D Hard-label attacker (3DHacker), based on the developed decision boundary algorithm to generate adversarial samples solely with the knowledge of class labels. Specifically, to construct the class-aware model decision boundary, 3DHacker first randomly fuses two point clouds of different classes in the spectral domain to craft their intermediate sample with high imperceptibility, then projects it onto the decision boundary via binary search. To restrict the final perturbation size, 3DHacker further introduces an iterative optimization strategy to move the intermediate sample along the decision boundary for generating adversarial point clouds with smallest trivial perturbations. Extensive evaluations show that, even in the challenging hard-label setting, 3DHacker still competitively outperforms existing 3D attacks regarding the attack performance as well as adversary quality.

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You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos

You Can Ground Earlier than See: An Effective and Efficient ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiang Fang Daizong Liu Pan Zhou Guoshun Nan 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 Peking University Beijing University of Posts and Telecommunications

Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target moment semantically according to a sentence query. Although previous respectable works have made decent success, they only focus on high-level visual features extracted from the consecutive decoded frames and fail to handle the compressed videos for query modelling, suffering from insufficient representation capability and significant computational complexity during training and testing. In this paper, we pose a new setting, compressed-domain TSG, which directly utilizes compressed videos rather than fully-decompressed frames as the visual input. To handle the raw video bit-stream input, we propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual features (I-frame, motion vector and residual features) for effective and efficient grounding. Particularly, instead of encoding the whole decoded frames like previous works, we capture the appearance representation by only learning the I-frame feature to reduce delay or latency. Besides, we explore the motion information not only by learning the motion vector feature, but also by exploring the relations of neighboring frames via the residual feature. In this way, a three-branch spatial-temporal attention layer with an adaptive motion-appearance fusion module is further designed to extract and aggregate both appearance and motion information for the final grounding. Experiments on three challenging datasets shows that our TCSF achieves better performance than other state-of-the-art methods with lower complexity.

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LLM App Store Analysis: A Vision and Roadmap

arXiv

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arXiv 2024年

作者： Zhao, Yanjie Hou, Xinyi Wang, Shenao Wang, Haoyu 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 China Huazhong University of Science and Technology Wuhan China

The rapid growth and popularity of large language model (LLM) app stores have created new opportunities and challenges for researchers, developers, users, and app store managers. As the LLM app ecosystem continues to evolve, it is crucial to understand the current landscape and identify potential areas for future research and development. This paper presents a forward-looking analysis of LLM app stores, focusing on key aspects such as data mining, security risk identification, development assistance, and market dynamics. Our comprehensive examination extends to the intricate relationships between various stakeholders and the technological advancements driving the ecosystem's growth. We explore the ethical considerations and potential societal impacts of widespread LLM app adoption, highlighting the need for responsible innovation and governance frameworks. By examining these aspects, we aim to provide a vision for future research directions and highlight the importance of collaboration among stakeholders to address the challenges and opportunities within the LLM app ecosystem. The insights and recommendations provided in this paper serve as a foundation for driving innovation, ensuring responsible development, and creating a thriving, user-centric LLM app landscape. Copyright © 2024, The Authors. All rights reserved.

关键词： Ecosystems

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