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SONICUMOS: An Enhanced Active Face Liveness Detection System via Ultrasonic and Video Signals

IEEE Transactions on Mobile computing 2025年

作者： Wu, Yihao Jiang, Peipei Cheng, Jianhao Zhao, Lingchen Shen, Chao Wang, Cong Wang, Qian Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Hubei430072 China Laboratory for AI-Powered Financial Technologies Ltd. 999077 Hong Kong Xi'An Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Xi'an710049 China Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an710049 China City University of Hong Kong Department of Computer Science 999077 Hong Kong

SONICUMOS is an enhanced behavior-based face liveness detection system that combines ultrasonic and video signals to sense the 3D head gestures. As face authentication becomes increasingly prevalent, the need for a reliable liveness detection system is paramount. Traditional behavior-based liveness detection methods (e.g., eye-blinking, nodding, etc.), which are widely deployed in mission-critical scenarios like finance and banking applications today, are prone to advanced media-based facial forgery attacks. SONICUMOS aims to incorporate the traditional behaviorbased method for active liveness detection without introducing extra user burden. By employing ultrasonic signals, SONICUMOS capitalizes on the head gestures, significantly raising the security bar. Our approach utilizes the frequency-modulated continuouswave (FMCW) ultrasonic radar for robust 3D gesture recognition compatible with face authentication. We also propose a new dual-feature fusion network that integrates audio and video features at the feature level to increase detection accuracy and resilience against numerous attacks. Our prototype has been tested on seven off-the-shelf Android/iOS smartphones, achieving an overall detection accuracy of 95.83% at an equal error rate (EER) of 4.96% when dealing with 3D impersonation attacks © 2025 IEEE.

关键词： Frequency modulation

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Learning From Single-Expert Annotated Labels for Automatic Sleep Staging

Learning From Single-Expert Annotated Labels for Automatic S...

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

作者： Zhiheng Luan Yanzhen Ren Li Peng Xiong Chen Xiuping Yang Weiping Tu Yuhong Yang Ministry of Education Key Laboratory of Aerospace Information Security and Trusted Computing School of Cyber Science and Engineering Wuhan University Department of Otorhinolaryngology Head and Neck Surgery Zhongnan Hospital of Wuhan University Sleep Medicine Centre Zhongnan Hospital of Wuhan University School of Computer Science Wuhan University

Existing automatic sleep staging algorithms rely on accurately labeled data. However, due to the subjectivity of sleep experts, accurate labels must be obtained through joint labeling by multiple experts, which results in high time and labor costs. In this work, we treat labels mislabeled by a single expert as noisy labels and first propose SE-ASS, an automatic sleep staging learning framework based on single-expert annotated data. Since multiple models tend to produce inconsistent predictions for instances with incorrect labels during training, we use two networks with the same structure but different initializations and regularize them with a prediction consistency loss to prevent overfitting to noisy labels. Furthermore, we use a contrastive loss between models to enhance the exploration of feature representations without relying on potentially noisy labels. Our results on two publicly available datasets show that SE-ASS can effectively improve the performance of automatic sleep staging models trained on single-expert annotated datasets.

关键词： Training Costs Medical services Signal processing Predictive models Noise measurement Labeling

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Data protection and provenance in cloud of things environment: Research challenges

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International Journal of Information and computer security 2020年第4期12卷 416-435页

作者： Wang, Chundong Yang, Lei Guo, Hao Wan, Fujin Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin China Global Energy Internet Research Institute Beijing University of Technology Tianjin China Department of College of Computer and Control Engineering Nankai University Tianjin China

internet of things are increasingly being deployed over the cloud (also referred to as cloud of things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DPSPM) based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks. © 2020 Inderscience Enterprises Ltd.

关键词： Data privacy

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A Lightweight Delegated Private Set Intersection Cardinality Protocol

SSRN

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SSRN 2023年

作者： Yang, Xiaoyi Zhao, Yanqi Zhou, Sufang Wang, Lianhai Department of Computing The Hong Kong Polytechnic University Hong Kong School of Cyberspace Security Xi’an University of Posts and Telecommunications Xi’an710121 China School of Computer and Information Engineering Henan University Kaifeng475004 China Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology Shandong Academy of Sciences Jinan250014 China

Secure multiparty computation (MPC) is an important means to realize privacy computing (PC). Private set intersection cardinality (PSI-CA) is a variant of an important problem in MPC research, that is private set intersection. PSI-CA allows multiple participants jointly compute the size of the common intersection of their private sets through interaction without revealing any other information about their respective sets. Delegated PSI-CA delegates a large number of computing in the protocol to some untrusted cloud servers, which makes the protocol efficient. Delegated PSI-CA is considered to be of great significance for the construction of privacy-preserving contact tracing systems recently. However, the existing work is still not ideal for the resource-limited mobile terminal of clients. In this paper, we propose a lightweight delegated PSI-CA protocol based on multi-point oblivious pseudorandom function and collision-resistant hash function. Our protocol does not need to do additional pre-operations, so the computation complexity and the communication complexity on the client side are further reduced compare to the existing works. In addition, we build a privacy-preserving contact tracing system by utilizing our protocol, which can be publicly checked, named PC-CONTrace. The experimental results show that our system is more practical and advantageous for densely populated areas. © 2023, The Authors. All rights reserved.

关键词： Hash functions

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On the Rate Region of the Downlink NOMA System with Improper Signaling and Imperfect SIC

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IEEE Transactions on Communications 2025年

作者： Cheng, Hao Zhang, Min Hua, Meng Xia, Yili Ding, Fei Zhang, Shunwai Pei, Wenjiang Swindlehurst, A. Lee The School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China The National Mobile Communications Research Laboratory Southeast University China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The School of Information Science and Engineering Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing210003 China The Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China The College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing210003 China The Center for Pervasive Communications and Computing University of California at Irvine IrvineCA92697 United States

Non-orthogonal multiple access (NOMA) is a promising technology garnering significant attention among the internet of Things (IoT) community due to its superior spectral efficiency. This work addresses the rate region boundary enhancement of downlink NOMA systems under imperfect successive interference cancellation (SIC) with advanced improper Gaussian signaling (IGS), which provides additional degrees of freedom for system design. We investigate a universal scenario in which two users adopt improper signaling and their transmit powers are optimized. We first formulate the achievable rate of both users in terms of the impropriety degree of the IGS. First, the analytical expressions for the best improper transmission are characterized by jointly optimizing the users’ power and the impropriety degree for the perfect SIC case. Then, a deep Q network (DQN)-based approach is provided to find the rate region of the IGS-aided NOMA system under imperfect SIC. Simulations presented for the downlink NOMA system support the analysis, illustrating that IGS can efficiently enhance the rate region of the NOMA system compared to proper signaling. © 1972-2012 IEEE.

关键词： Gaussian distribution

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Blockchain-assisted Puncturable Signcryption for Cloud and Fog-Hosted Industrial Cyber-Physical Systems

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IEEE Network 2025年

作者： Ma, Qingru Shen, Jian Pandi, Vijayakumar Gupta, Brij B. Arya, Varsha Mahalinghum, Maheswari Zhejiang Sci-Tech University School of Computer Science and Technology School of Artificial Intelligence Hangzhou310018 China Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Hangzhou310018 China Department of Computer Science and Engineering Tamil Nadu Tiruchirappalli620009 India Asia University Department of Computer Science and Information Engineering Taichung413 Taiwan China Medical University Hospital China Medical University Department of Medical Research Taichung Taiwan Pune412115 India School of Cybersecurity Korea University Seoul Korea Republic of Kyung Hee University Seoul02447 Korea Republic of Lebanese American University Beirut1102 Lebanon Center for Interdisciplinary Research Dehradun India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

The widespread adoption of emerging technologies, such as interconnected sensors and advanced automation systems, has led to rapid advancements in smart industrial environments. While industrial cyber-physical systems (ICPSs) bring efficiency and innovation to industrial processes, the security risks are also of great concern. The existing work provides many methods for data security and authentication for ICPSs. However, since the authentication status of ICPSs is changeable, the up-to-date methods can hardly meet the requirements for rapid authentication and secure data transmission. What’s more, largescale message revocation incurs significant communication and computational costs. This paper proposes a blockchain-assisted identity-based puncturable signcryption (BC-IBPSC) scheme for cloud and fog-hosted ICPSs. First, based on the concepts of puncturable encryption and signature, a novel identity-based puncturable signcryption method is presented to achieve efficient verifiable data encryption. Second, the blockchain is utilized to record all valid and punctured messages, to achieve evidence storage and traceability. The results of the analysis indicate that the scheme ensures security and operates efficiently. In addition, experiments show that the size of the signcrypted files does not affect the value of Gas consumption on the blockchain when uploading proofs. © 2025 IEEE. All rights reserved.

关键词： Authentication

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On the ADEP and DOR Analysis of RIS-aided URLLC Systems with Partial CSI in Smart Factory

On the ADEP and DOR Analysis of RIS-aided URLLC Systems with...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Bao, Tingnan Wang, Haiming Yang, Hong-Chuan Wang, Wen-Jing Hasna, Mazen O. Lenovo Research + Institute of Computing Technology Chinese Academy of Sciences Beijing China Lenovo Reseach Lenovo Group Limited Beijing China University of Victoria Department of Electrical and Computer Engineering Victoria Canada Shaanxi Key Laboratory of Information Communication Network and Security XUPT Xi'an China Qatar University Department of Electrical Engineering Doha Qatar

ISBN: (纸本)9781665423908

Factory automation applications impose stringent quality of service (QoS) over ultra-reliable and low-latency communication (URLLC) system. However, the existing wireless systems cannot meet the requirement for these mission-critical industrial internet-of-things (IIoT) applications. In this paper, we consider the reconfigurable intelligent surface (RIS)-aided URLLC systems in the smart factory. To facilitate the performance analysis of the practical situation, the presence of phase error at RIS is discussed. We analyze and derive approximate closed-form expression of average decoding error probability (ADEP) and the closed-form expression of performance limits de-lay outage rate (DOR) under our proposed scenario, respectively. Through analytical results and associated numerical examples, we demonstrate the effect of the number of reflecting elements at the RIS, channel blocklength and packet size in achieving high reliability and low latency. Therefore, the deployment of RIS can offer an attractive solution to enhance the reliability over URLLC systems in the smart factory. © 2021 IEEE.

关键词： Quality of service

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Federated Digital Twin Construction via Distributed Sensing: A Game-Theoretic Online Optimization with Overlapping Coalitions

arXiv

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arXiv 2025年

作者： Chen, Ruoyang Yi, Changyan Zhou, Fuhui Kang, Jiawen Wu, Yuan Niyato, Dusit College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China College of Electronic and Information Engineering Nanjing University of Astronautics and Aeronautics Nanjing210016 China School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

In this paper, we propose a novel federated framework for constructing the digital twin (DT) model, referring to a living and self-evolving visualization model empowered by artificial intelligence, enabled by distributed sensing under edge-cloud collaboration. In this framework, the DT model to be built at the cloud is regarded as a global one being split into and integrating from multiple functional components, i.e., partial-DTs, created at various edge servers (ESs) using feature data collected by associated sensors. Considering time-varying DT evolutions and heterogeneities among partial-DTs, we formulate an online problem that jointly and dynamically optimizes partial-DT assignments from the cloud to ESs, ES-sensor associations for partial-DT creation, and as well as computation and communication resource allocations for global-DT integration. The problem aims to maximize the constructed DT’s model quality while minimizing all induced costs, including energy consumption and configuration costs, in long runs. To this end, we first transform the original problem into an equivalent hierarchical game with an upper-layer two-sided matching game and a lower-layer overlapping coalition formation game. After analyzing these games in detail, we apply the Gale-Shapley algorithm and particularly develop a switch rules-based overlapping coalition formation algorithm to obtain short-term equilibria of upper-layer and lower-layer subgames, respectively. Then, we design a deep reinforcement learning-based solution, called DMO, to extend the result into a long-term equilibrium of the hierarchical game, thereby producing the solution to the original problem. Simulations show the effectiveness of the introduced framework, and demonstrate the superiority of the proposed solution over counterparts. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Federated Learning for Recognizing Private Handwritten Numbers

Federated Learning for Recognizing Private Handwritten Numbe...

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2021 IEEE International Conference on Data Science and computer Application, ICDSCA 2021

作者： Wang, Di Jiang, Yuchen Zhao, Yibo Chen, Panyang Li, Ang Pan, Kun Institution of Information Engineering Chinese Academy of Sciences State Key Laboratory of Information Security Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Computer Science and Technology College of Computer Science Chongqing University Chongqing China Zibo Shiyan High School International Division Zibo Shandong China Computer Science Department of Computer Science and Software Engineering Auburn University Auburn United States Shanghai Shangde Experimental School Shanghai China School of Computing University of Leeds Leeds United Kingdom

ISBN: (纸本)9781665440530

Federated learning (FL) is a new technology in the field of machine learning, which trains an algorithm across multiple decentralized edge devices or servers holding data samples, without exchanging their data samples. Horizontal Federated Learning (HFL) is a FL method focusing on using the same feature space across all users, which is suitable for situations where users' data feature dimensions overlap more. Because FL has the advantage of privacy protection in the field of manufacturing, we propose an HFL-based deep learning method named FedCNN, which realizes the digits handwritten recognition without violating the privacy or data sharing. Three different methods (local model, centralized model, and HFL-based FedCNN) are used on a public data set (MINIST) to compare the pros and cons of the solutions. The experimental results demonstrate that FedCNN scheme can simultaneously achieve practical privacy preservation and good model generalization performance with Homomorphic Encryption. © 2021 IEEE.

关键词： Data privacy

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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