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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xiaogeng Liu Minghui Li Haoyu Wang Shengshan Hu Dengpan Ye Hai Jin Libing Wu Chaowei Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Software Engineering Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Arizona State University

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo) 1 1 https://***/CGCL-codes/TeCo, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability.

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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Fingerprint Presentation Attack Detector Using Global-Local Model

arXiv

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arXiv 2024年

作者： Liu, Haozhe Zhang, Wentian Liu, Feng Wu, Haoqian Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society The National Engineering Laboratory for Big Data System Computing Technology The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module;2) the local module;and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ∼10% in terms of TDR (91.19% versus 80.74%). Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by Distributed Ledger technology and Internet of Things 23

ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Haoyu Li, Leixiao Lin, Hao Chang, Xiangyang Wan, Jianxiong Li, Jie Zhu, Fangyuan College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Reg. Eng. and Technol. Research Center of Big Data Based Software Service China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

P2P Botnet is famous for the resilience against termination. However, its dependence on Neighbor List (NL) makes it susceptible to infiltration and poison, also leading to a dearth of adequate protection of Botmaster's identity. So as to overcome these weaknesses, the novel botnet infrastructure combined with blockchain emerges. But due to some flaws such like high latency and expensive transactions fees on blockchain, these new schemes can't satisfy the need of constructing hyper large-scale botnet in practice. We propose ZombieCoin3.0, a Botnet underlying overlay leveraging IoT devices to be maintainers and integrated with Distributed Ledger technology (DLT), which is able to deploy bots fully on blockchain. And it is versatile for file transfer, data feedback and other command and control scenarios that many contemporaries are impotent to cope with. Our proposal eliminates the P2P botnets' dependence on NL and the bottlenecks that other blockchain combined botnets have. Experiments' results authenticated that the effectiveness of our proposal as well as low latency of communication and nearly zero overhead of cost other comparatives unable to attain are achieved. © 2021 IEEE.

关键词： Costs Distributed ledger Botnet Blockchains Proposals Internet of Things Low latency communication

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Automated Crash Analysis and Exploit Generation with Extendable Exploit Model 7

Automated Crash Analysis and Exploit Generation with Extenda...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Liu, Zhehui Wang, Zhongru Zhang, Yuntao Liu, Tong Fang, Binxing Pang, Zhengyuan Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Chinese Academy of Cyberspace Studies Beijing China North China University of Technology School of Information Science and Technology Beijing100144 China Beijing DigApis Technology Co. Ltd Beijing100871 China JiangSu DigApis Technology Co. Ltd Jiangsu Nantong226000 China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

Recently, more and more software vulnerabilities are disclosed and researchers tend to study on automatically discover and exploit the vulnerabilities. However, the main challenges of automated exploit generation are: 1) it is hard to analyze the program failure and extract useful information, 2) the scenario of the vulnerability too complex to successfully exploit. Therefore, This paper proposes a vulnerability exploit generation framework AEG-E. AEG-E can extract the control flow graph from the target program and employ the crash reproduce algorithm in symbolic execution to reduce the problem of path explosion. To adapt to complex vulnerability scenarios, we design the extendable and user-configurable exploit model to generate different exploit. Finally, we used the binaries from Robo Hacking Games and real world program to demonstrate the validity and efficiency of AEG-E. The experiment results shows that AEG-E is 2.913 times more efficient than previous exploit generation tool, REX. © 2022 IEEE.

关键词： Flow graphs

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PACE: Reputation-Driven Hierarchical Incentive Mechanism for Trusted Healthcare Transactions in Model Marketplace

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IEEE Transactions on Consumer Electronics 2024年

作者： Lu, Jianfeng Dou, Xiaochi Rathore, Bharati Jhaveri, Rutvij H. Seid, Abegaz Mohammed Erbad, Aiman Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China United Kingdom Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology India Hamad Bin Khalifa University College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Federated learning-based model marketplaces have the potential to securely leverage healthcare data for efficient healthcare transactions. However, the willingness to participate in this marketplace is severely hindered by self-interested model sellers, who maximize their personal gains via distorting the quality of their models. To this end, we propose a rePutation-driven hierArchical inCentive mEchanism, called PACE, which combines contract cooperation and game pricing to motivate model sellers to be honest, thereby improving the sustainability of the model marketplace via two-layer cooperation. Specifically, at the training layer, we design a reward payment rule based on multidimensional contracts, while at the trading layer, we use a competitive market pricing game to develop the accuracy pricing rule. In particular, the above rules cleverly combine the fine-grained model sellers' reputations with the willingness of healthcare devices to indirectly affect the model sellers' utilities. Theoretically, we prove that PACE can effectively incentivize model sellers to truthfully report model quality while satisfying both individual rationality and incentive compatibility with a fairness guarantee. Experimental results conducted on four real-world datasets show that our PACE improves the global performance by up to 12.85% and reduces the global loss by at least 19.23% compared with the state-of-the-art baselines. © 1975-2011 IEEE.

关键词： Marketplaces

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Internet of Digital Twin: Framework, Applications and Enabling Technologies

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IEEE Communications Surveys and Tutorials 2025年

作者： Liu, Ruhan Luan, Tom H. Qu, Youyang Xiang, Yong Gao, Longxiang Zhao, Dongmei Deakin University School of Info Technology MelbourneVIC3125 Australia Xi'an Jiaotong University School of Cyber Science and Engineering Shaanxi Xi'an710049 China Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China McMaster University Department of Electrical and Computer Engineering HamiltonON Canada

Intelligent physical systems, such as smart vehicles and robotic arms, are increasingly integrated into both industrial and everyday applications. However, the systems typically face hardware limitations that constrain their computational capacities. Digital twin systems offer a solution by creating real-time digital replicas of physical systems that enhance computational efficiency, overcoming physical limitations. Moreover, multiple digital twins that hold complementary knowledge can conveniently collaborate to share information and computational resources, further improving the performance of physical systems by forming an Internet of Digital Twin (IoDT). This paper presents a comprehensive investigation of the digital twin network, tracing the evolution of digital twins and providing a classification of the key technologies, functional frameworks, and application domains of IoDT. This paper delves into the IoDT communication framework by studying the fundamental communication modes of IoDT, exploring its integration with advanced technologies such as edge computing, blockchain, 5G/6G networks, and machine learning to facilitate data transmission, interaction, and omni-directional sensing. By offering a broad perspective, the paper aims to deepen stakeholders' understanding of current research and potential future developments, encouraging further exploration of IoDT technologies and their evolution. © 1998-2012 IEEE.

关键词： 5G mobile communication systems

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Automated Vulnerability Discovery system Based on Hybrid Execution 7

Automated Vulnerability Discovery System Based on Hybrid Exe...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Liu, Tong Wang, Zhongru Zhang, Yuntao Liu, Zhehui Fang, Binxing Pang, Zhengyuan Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Chinese Academy of Cyberspace Studies Beijing China North China University of Technology School of Information Science and Technology Beijing100144 China Beijing DigApis Technology Co. Ltd Beijing100871 China JiangSu DigApis Technology Co. Ltd Jiangsu Nantong226000 China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

At present, program vulnerabilities occur frequently, which seriously threatens the security of cyberspace. Automated vulnerability discovery technology has attracted more and more attention because of its efficiency and universality. In the current field of vulnerability discovery, it is common to combine fuzzing with symbolic execution technology. Symbolic execution technology is often used to solve the input of complex paths and help fuzzing improve the program coverage, so as to detect vulnerabilities better. However, the symbolic execution has no goals in the process of hybrid execution exploration path, it is easy to cause the execution path to deviate from the expected target points. And generating the test case of the corresponding path requires frequently calling the solver for solution, while the current solver has low efficiency and poor solvability for nonlinear operation. In order to solve the above problems, we propose an automated vulnerability discovery system based on hybrid execution. In the system, we propose the symbolic execution guidance algorithm based on dynamic and static combination to guide the symbolic execution to solve the input reaching the target points, so as to avoid exploring in useless paths and avoid consuming a lot of time and computing resources. In addition we hook some nonlinear functions to optimize the nonlinear function constraint solving, so as to improve the hybrid execution efficiency. We have conducted extensive experiments on the RHG 2019 challenge dataset and the RHG 2021 challenge dataset. The experimental results show the effectiveness and scalability of the system. © 2022 IEEE.

关键词： Efficiency

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Localized Adaptive Channel and Power Selection With TinyML (LACPSA) in Dense IEEE 802.11 WLANs

Localized Adaptive Channel and Power Selection With TinyML (...

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Khalid Ibrahim Qureshi Cheng Lu Ruoheng Luo Muhammad Ali Lodhi Lei Wang Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China

ISBN: (数字)9798350366440

ISBN: (纸本)9798350366457

In dense IEEE 802.11 WLANs, optimized channel allocation and transmission power control are crucial for miti- gating interference and congestion. Traditional approaches face significant challenges due to limited channels and dynamic traffic patterns. To address these issues, we propose a novel scheme wherein TinyML models are directly integrated into access points (APs) for adaptive channel optimization and transmission power control. This distributed approach empowers APs to optimize settings autonomously based on their local information, reducing reliance on central controllers and enhancing scalability. Leveraging compact TinyML models enables rapid decision- making, allowing APs to adapt to network changes promptly. By dynamically adjusting channel optimization and transmission power based on signal strength, interference levels, and traffic load, interference can be minimized, thereby improving network performance. Through extensive simulations, we demonstrate a significant reduction in interference compared to traditional approaches. Our proposed distributed solution enhances network resilience and flexibility, making it more suitable for real-world deployment scenarios where conditions change rapidly. The integration of TinyML into the network presents a promising approach for future WLAN optimization efforts.

关键词： Adaptation models Wireless LAN Tiny machine learning Power control Interference Channel allocation Traffic control Optimization IEEE 802.11 Standard Load modeling

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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