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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Shanshan Chen Jun Liu Yuying Li Yamin Li Yang Yang Gaoyang Liu Chen Wang Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In Federated Learning (FL), the regulatory need for the "right to be forgotten" requires efficient Federated Unlearning (FU) methods, which enable FL models to unlearn appointed training data. Associating with the emergence FU, verifying the performance of FU plays a critical role in evaluating the consistency FU methods, in case of the unexpected degradation of the FL model. Though well developed, none of the existing verification methods in FU stands for the clients who opt out of the FL process, which is a universal demand in FL. More specifically, after the clients quit the FL cooperation, they can no longer verify whether the FL model unlearns their data after the FL keeps training for several rounds. To this end, we introduce a continuous verification mechanism for FL clients, called Backdoor Attack-based Forgetting Verification (BAFV). Inspired by backdoor attack, BAFV embedded a persistent mark for the client that proposes to leave, with the intention that the client still has the right to verify of FU after leaving the FL cooperation for a relatively long period. Extensive experiments across diverse FU environments and datasets demonstrated that our method maintains the accuracy of model and provides clients with a continuous verification mechanism to defend their rights. Our code of BAFV is publicly available at: https://***/paper-liu/***.

关键词： Training Privacy Distributed processing Accuracy Federated learning Training data Feature extraction Data models Servers Protection

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A Blockchain-Assisted key Generation Electric Health Records Sharing Scheme 8th

A Blockchain-Assisted Key Generation Electric Health Records...

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8th International Conference on Artificial Intelligence and security , ICAIS 2022

作者： Zhang, Qiao Chen, Xiubo Ahmad, Haseeb Xu, Gang Yang, Yixian Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University Guiyang Guizhou550025 China State Key Laboratory of Networking and Switching Technology Information Security Center Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad37610 Pakistan School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9783031067631

Electronic health records (EHRs) contain a large amount of private data of patients. Once these data are compromised during the process of sharing, it may threaten the patients’ privacy. In this paper, a novel blockchain-assisted electronic health record sharing scheme is proposed, which utilizes attribute-based encryption (ABE) based on the consortium blockchain to realize the privacy protection in the sharing process of EHRs. Firstly, the master key is negotiated by all consensus nodes of consortium blockchain, no one knows the specific value of the master key. Consensus nodes are acted by medical institutions, they also responsible for generating and managing users’ private keys. Secondly, with the help of powerful cloud services, pre-decryption and ciphertext retrieval assignments are outsourced to cloud services for reducing the burden of blockchain. Thirdly, this scheme is also based on searchable encryption, which allows for quick ciphertext lookup from cloud services. Data owners can generate ciphertext indexes for their private data, and data users with retrieval trapdoors can quickly retrieve ciphertexts. Finally, the security and performance of blockchain-assisted electronic health record sharing scheme are analyzed in detail, the results show that our scheme is safe and feasible. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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A Blockchain-Based IoT Data Secure Vickery Auction system 8th

A Blockchain-Based IoT Data Secure Vickery Auction System

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8th International Conference on Artificial Intelligence and security , ICAIS 2022

作者： Wang, Haohui Chen, Xiubo Ahmad, Haseeb Xu, Gang Yang, Yixian Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang550025 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad37610 Pakistan School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9783031067631

Traditional centralized architectures for IoT data suffer from single points of failure in terms of data storage and access control, and IoT data does not fully realize its value as a digital asset. In response to the above challenges, we propose a decentralized storage and lightweight access control method for IoT data based on blockchain technology, and put forward an auction system to commercialize the IoT data. Firstly, a blockchain-based data storage method proposed using Inter Planetary File system (IPFS), which ensures the data is immutable and traceable. Secondly, in this paper, we propose a distributed lightweight Capability-Based Access Control (CapBAC) based on blockchain, in which a time dimension is added so that no additional token revocation operation is required. Thus, lightweight access control is achieved under IoT devices with limited computing power. Finally, we come up with a blockchain-based Vickery auction system to sell the data to the highest bidder in demand. This auction effectively protects the privacy of bidders and does not require third-party intermediary fees. And it can ensure fairness and non-repudiation of the auction. By analyzing the entire system process, we guarantee secure storage of IoT data. Besides, we can achieve lightweight access control and realize the value of the data based on the blockchain technology. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Access control

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A fully-customized dataflow engine for 3D earthquake simulation with a complex topography

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science China(information sciences) 2022年第5期65卷 65-80页

作者： Bingwei CHEN Haohuan FU Wayne LUK Guangwen YANG Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University National Supercomputer Center in Wuxi Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua University Department of Computing Imperial College London

With HPC(high performance computing) evolving into the exascale era, improvements in computing performance and power efficiency have become increasingly more important. Based on our previous work on enabling earthquake simulations on a large scale on Sunway Taihu Light, we further explore other possibilities to improve the application through a fully-customized hardware design on reconfigurable FPGA(field programmable gate array) devices. We investigate the feasibility and the potential benefits of a complete fixed-point design. We first perform a coarse-resolution-based simulation to analyze the representation range and precision needed to capture both the total energy and the energy distribution of variables over space and time. We then derive a complete fixed-point design that identifies the suitable bitwidth for major categories of variables and dynamically represents the range through a dynamic scaling scheme. Finally, we use the optimized fixed-point design to run a case of the Wenchuan earthquake to demonstrate the potential of supporting large-scale scientific simulations on FPGA devices. The results demonstrate that an 18-bit fixed-point design already provides an almost identical description of the seismic events in the Wenchuan scenario down to a single-precision floating-point version and provides sustainable performance equivalent to 13.1 Intel Xeon Gold 6154 18-core CPUs or 2.10 Sunway 260-core processors, with performance per watt(power efficiency) improved by 15.3 and 3.72 times compared with the Intel Xeon Gold 6154 18-core CPUs and the Sunway 260-core processors, respectively.

关键词： reconfigurable computing earthquake simulation FPGA hardware-software co-design architecture exploration

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Optimizing Peak Age of information in Mobile Edge Computing

Optimizing Peak Age of Information in Mobile Edge Computing

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International Teletraffic Congress, ITC

作者： Jianhang Zhu Jie Gong Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350360073

ISBN: (纸本)9798350360080

Recently, information freshness in real-time monitoring systems has received increasing attention. Age of information (AoI) has emerged as a metric for measuring the information freshness. In many applications, the information embedded in an update packet needs to be computed before delivering to a destination. Mobile edge computing (MEC) can efficiently accomplish the computing process. In the MEC system, transmission process and computation process are coupled together, jointly affecting freshness. In this paper, we consider minimizing the average peak AoI (PAoI) in an MEC system, where each update is received and computed by an edge server before delivering to the destination. We consider the generate-at-will source model and study when to generate a new update. We prove that the fixed threshold policy is optimal for arbitrary transmission time and computation time distributions. Our numerical simulation results not only validate the theoretical findings, but also demonstrate the behaviors of the average PAoI versus the mean transmission time and the mean computation time.

关键词： Measurement Multi-access edge computing Scheduling algorithms Exponential distribution information age Numerical simulation Real-time systems

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Few-Shot Camouflaged Object Segmentation

Few-Shot Camouflaged Object Segmentation

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International Joint Conference on Neural Networks (IJCNN)

作者： Ziqiu Wang Yuying Li Yang Yang Yamin Li Gaoyang Liu Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the domain of computer vision, Camouflaged Object Segmentation (COS) is a crucial task aimed at identifying objects that blend into their surroundings, with applications spanning diverse sectors such as military, medical, and beyond. Traditional COS techniques, which primarily depend on supervised learning, necessitate large-scale labeled datasets. However, the acquisition of sufficient camouflage images for such purposes is often constrained due to their scarcity and the high cost of manual annotation. Additionally, these conventional methods frequently struggle to generalize to novel, unseen classes. In response to these challenges, this paper proposes the Camouflage Few-Shot (CAMFS) framework, an innovative approach integrating few-shot learning into COS. The CAMFS framework comprises two main components: the Camouflaged-Meta module, which converts the semantic information of camouflaged objects into compact feature vectors to facilitate knowledge transfer from support to query images; and the Camouflaged-Base module, focused on refining edge detection and enhancing the contrast between foreground and background elements. To overcome the limitations of existing COS datasets, which are primarily designed for supervised learning, we have developed the COS-FSS dataset, the first public few-shot COS dataset. It is based on the COD10K dataset and supplemented with approximately 3200 additional camouflage images. We conducted extensive evaluations of our CAMFS framework on the COS-FSS dataset. Compared to existing COS models, CAMFS demonstrates an average improvement of 5.7% in S α and 14.02% in $F_\beta ^w$, while against few-shot segmentation models, it achieves a 5.97% increase in m-IoU. The dataset and additional resources are available at https://***/CAM-FSS/FSS-COD.

关键词： Image segmentation Military computing Supervised learning Semantics Refining Neural networks Object segmentation

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Cancelable Speaker Identification system Based on Optical-Like Encryption Algorithms

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computer systems science & Engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai Ghada El-Banby Hesham F.A.Hamed Gerges M.Salama Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural information Processing systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

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 adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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Speed Master: Quick or Slow Play to Attack Speaker Recognition 39

Speed Master: Quick or Slow Play to Attack Speaker Recogniti...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Ye, Zhe Zhang, Wenjie Ren, Ying Kang, Xiangui Yan, Diqun Ma, Bin Wang, Shiqi Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Jinan China Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)157735897X

Backdoor attacks pose a significant threat during the model’s training phase. Attackers craft pre-defined triggers to break deep neural networks, ensuring the model accurately classifies clean samples during inference yet erroneously classifies samples added with these triggers. Recent studies have shown that speaker recognition systems trained on large-scale data are susceptible to backdoor attacks. Existing attackers employ unnoticed ambient sounds as triggers. However, these sounds are not inherently part of the training samples themselves. In essence, triggers can be designed to maintain an intrinsic connection with the original speech to enhance stealthiness. Our paper presents a novel attack methodology named Speed Master, which undermines deep neural networks by manipulating the speed of speech samples. Specifically, we execute poison-only backdoor attacks using speed or tempo adjustment. Changes in speech rate have become a common occurrence, as seen on platforms that allow users to adjust playback speed. In real-world scenarios, people naturally adjust their speaking rate depending on the context. As a result, changes in a speaker’s speech rate are typically perceived as normal and are unlikely to raise suspicion. Furthermore, detecting such subtle adjustments becomes challenging for users without reference speech. Our comprehensive experiments demonstrate that Speed Master can achieve an ASR over 99% in the digital domain, with only a 0.6% poisoning rate. Additionally, we validate the feasibility of Speed Master in the real world and its resistance to typical defensive measures. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Elastic Collision Based-Interactive Path Planning system for Vascular Intervention Robots

Elastic Collision Based-Interactive Path Planning System for...

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International Conference on Biomimetic Intelligence and Robotics & Medical Robotics Forum, ICBIR+MRF 2024

作者： Cao, Sheng Guo, Shuxiang Guo, Jian Wang, Jian Wang, Bin Ding, Mingchao Aerospace Center Hospital The Key Laboratory of Convergence Medical Engineering System and Healthcare Technology School of Life Science Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China Shenzhen Institute of Advanced Biomedical Robot Company Limited Guangdong Province Shenzhen518000 China Department of Electronic and Electrical Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

As population aging intensifies, the incidence of cardiovascular and cerebrovascular diseases has continued to rise. Vascular interventional surgery, known for its minimal trauma, rapid recovery, and clear effectiveness, requires doctors to perform procedures in radiation environments while wearing lead aprons. The advent of vascular intervention robots has provided a system-level solution to protect doctors from radiation and enhance outcomes. Currently, preoperative path planning for these robots predominantly relies on automatically generated paths, calculated by setting constraints and minimizing costs. However, this method lacks incorporation of physicians' expertise and consideration of elastic collision. Therefore, this study has designed an interactive path planning system for vascular intervention robot based on elastic collision. By establishing a simulation environment, doctors can use a manipulator to control virtual catheters and guidewires. While considering deformations, they maneuver the guidewire to the target position and record the current motion trajectory to guide the procedure in actual surgeries. Experimental tests have been conducted, confirming the system's effectiveness and feasibility. © 2024 The Authors.

关键词： Robot programming

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