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AdvMap: Crafting Adversarial Maps to Counter AI Aimbot in First-Person Shooter Games

IEEE Transactions on Games 2025年

作者： Yan, Kai Chen, Xianyi Cui, Qi Yuan, Haoqin Tan, Zhenshan Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education the School of Computer Science Nanjing210044 China Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing100044 China

AI-based automatic aiming cheats (a.k.a., AI aimbots) have proliferated in first-person shooter (FPS) games, which grant malicious users an unfair gameplay advantage. Since AI aimbots operate independently of game data and are developed using object detection algorithms, they are difficult to detect with traditional anti-cheating methods. To actively counter AI aimbots, we propose AdvMap, which introduces invisible adversarial perturbations into game scene elements. In optimizing these adversarial perturbations, we design a mixture-of-misleading loss function that increases the total target confidence score within each misleading bounding box. It mitigates the risk of segment missing even when AdvMap is obscured, thereby enhancing the robustness. Besides, an L1-norm constraint with a small scale is employed during each update of the adversarial perturbations, which preserves the fidelity of the game scene. Additionally, to enable effective adaptation to interact with various elements within game environments, we introduce an Image-Subspace-Based Multi-Directional (ISMD) optimization strategy. It enables the adversarial perturbations to adaptively fit into each element by leveraging the mapping relationship between the game's 3D scenes and its corresponding 2D images. Furthermore, we construct a comprehensive benchmark, which includes various FPS games with different graphics styles and perspectives. Extensive experimental results demonstrate the efficacy of our method in countering various AI aimbot tools on different state-of-the-art object detection methods. © 2021 IEEE.

关键词： Adversarial machine learning

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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A CPK-Based Identity Authentication Scheme for IoT

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Computer Systems science & Engineering 2022年第3期40卷 1217-1231页

作者： Mingming Zhang Jiaming Mao Yuanyuan Ma Liangjie Xu Chuanjun Wang Ran Zhao Zhihao Li Lu Chen Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information&Telecommunication BranchNanjing210008China State Grid Key Laboratory of Information&Network Security Institute of Information and CommunicationGlobal Energy Interconnection Research InstituteNanjing210003China Anhui Jiyuan Software Co.Ltd SGITGHefei230088China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Engineering Research Center of Broadband Wireless Communication Technology of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and *** existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in *** key distribution and management costs are high,which hinders the development of power IoT security *** Public Key(CPK)technology uses a small number of seeds to generate unlimited public *** is very suitable for identity authentication in the power Internet of *** this paper,we propose a novel identity authentication scheme for power *** scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and *** proposed scheme does not require third-party authentication and improves the security of identity authentication for power ***,the scheme reduces the resource consumption of power IoT *** improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key *** results show that the amount of storage resources occupied in our scheme is *** proposed scheme is more suitable for the power IoT.

关键词： Power Internet of Things terminal equipment identity authentication CPK PUF

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A Superalignment Framework in Autonomous Driving with Large Language Models

A Superalignment Framework in Autonomous Driving with Large ...

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IEEE Symposium on Intelligent Vehicle

作者： Xiangrui Kong Thomas Braunl Marco Fahmi Yue Wang Department of Electrical Electronic and Computer Engineering University of Western Australia Crawley WA Australia Department of Transport and Main Roads Queensland Government Brisbane QLD Australia Center for Data Science Queensland University of Technology Brisbane QLD Australia

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Over the last year, significant advancements have been made in the realms of large language models (LLMs) and multi-modal large language models (MLLMs), particularly in their application to autonomous driving. These models have showcased remarkable abilities in processing and interacting with complex information. In autonomous driving, LLMs and MLLMs are extensively used, requiring access to sensitive vehicle data such as precise locations, images, and road conditions. This data is transmitted to an LLM-based inference cloud for advanced analysis. However, concerns arise regarding data security, as the protection against data and privacy breaches primarily depends on the LLM’s inherent security measures, without additional scrutiny or evaluation of the LLM’s inference outputs. Despite its importance, the security aspect of LLMs in autonomous driving remains underexplored. Addressing this gap, our research introduces a novel security framework for autonomous vehicles, utilizing a multi-agent LLM approach. This framework is designed to safeguard sensitive information associated with autonomous vehicles from potential leaks, while also ensuring that LLM outputs adhere to driving regulations and align with human values. It includes mechanisms to filter out irrelevant queries and verify the safety and reliability of LLM outputs. Utilizing this framework, we evaluated the security, privacy, and cost aspects of eleven large language model-driven autonomous driving cues. Additionally, we performed QA tests on these driving prompts, which successfully demonstrated the framework’s efficacy.

关键词： Large language models Roads Privacy breach Information filters Regulation Safety Reliability

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Neuroprotective effects of Longxue Tongluo Capsule on ischemic stroke rats revealed by LC-MS/MS-based metabolomics approach

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Chinese Herbal Medicines 2023年第3期15卷 430-438页

作者： Jing Sun Xianyang Chen Yongru Wang Yuelin Song Bo Pan Bei Fan Fengzhong Wang Xiaonan Chen Pengfei Tu Jiarui Han Huixia Huo Jun Li Modern Research Center for Traditional Chinese Medicine School of Chinese Materia MedicaBeijing University of Chinese MedicineBeijing 100029China Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process Laboratory of Agro-products Quality Safety Risk AssessmentMinistry of AgricultureInstitute of Food Science and TechnologyChinese Academy of Agricultural SciencesBeijing 100193China Baofeng Key Laboratory of Genetics and Metabolism Zhongguancun Biological and Medical Big Data CenterBeijing 101300China

Objective:The present study aimed to evaluate the therapeutic effect and explore the underlying mechanisms of Longxue Tongluo Capsule(LTC)on ischemic stroke ***:Twenty-six rats were randomly divided into four groups,including sham group,sham+LTC group,MCAO group,and MCAO+LTC *** stroke rats were simulated by middle cerebral artery occlusion(MCAO),and LTC treatment group were orally administrated with 300 mg/kg of LTC once daily for seven consecutive *** therapy was validated in terms of neurobehavioral abnormality evaluation,cerebral infarct area,and histological *** plasma metabolome comparisons amongst different groups were conducted by UHPLC-Q Exactive MS in combination with subsequent multivariate statistical analysis,aiming to finding the molecules in respond to the surgery or LTC ***:Intragastric administration of LTC significantly decreased not only the neurobehavioral abnormality scores but also the cerebral infarct area of MCAO *** interstitial edema,atrophy,and pyknosis of glial and neuronal cells occurred in the infarcted area,core area,and marginal area of cerebral cortex were improved after LTC treatment.A total of 13 potential biomarkers were observed,and Youden index of 11 biomarkers such as LysoPC,SM,and PE were more than 0.7,which were involved in neuroprotective *** correlation and pathway analysis showed that LTC was beneficial to ischemic stroke rats via regulating glycerophospholipid and sphingolipid metabolism,together with nicotinate and nicotinamide *** and ternary analysis indicated the synergistic effect of carbohydrates and lipids may be induced by flavonoid intake from ***:The present study could provide evidence that metabolomics,as systematic approach,revealed its capacity to evaluate the holistic efficacy of TCM,and investigate the molecular mechanism underlying the clinical treatment of LTC on ischemic stroke.

关键词： ischemic stroke Longxue Tongluo Capsule metabolomics middle cerebral artery occlusion phenolic compounds UHPLC-Q Exactive MS

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A Superalignment Framework in Autonomous Driving with Large Language Models

arXiv

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arXiv 2024年

作者： Kong, Xiangrui Braunl, Thomas Fahmi, Marco Wang, Yue The Department of Electrical Electronic and Computer Engineering University of Western Australia CrawleyWA6009 Australia The Department of Transport and Main Roads Queensland Government BrisbaneQLD4000 Australia The Center for Data Science Queensland University of Technology BrisbaneQLD4000 Australia

关键词： Autonomous vehicles

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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Computer Systems science & Engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

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Engineering 2021年第6期7卷 845-856页

作者： Yue Hou Qiuhan Li Chen Zhang Guoyang Lu Zhoujing Ye Yihan Chen Linbing Wang Dandan Cao Beijing Key Laboratory of Traffic Engineering Beijing University of TechnologyBeijing 100124China Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China National Center for Materials Service Safety University of Science and Technology BeijingBeijing 100083China School of Transportation Southeast UniversityNanjing 211189China Department of Civil and Environmental Engineering Virginia TechBlacksburgVA 24061USA

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and *** service quality and service life are of great importance for civil engineers as they directly affect the regular service for the ***,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation *** pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface *** technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning *** review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

关键词： Pavement monitoring and analysis The state-of-the-art review Intrusive sensing Image processing techniques Machine learning methods

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Simulating the multi-media fate of perfluorooctane sulfonate (PFOS) and its precursors in the Bohai coastal region of China

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science of the Total Environment 2024年 957卷 177772页

作者： Han, Guoxiang Song, Shuai Du, Di Cui, Haotian Lu, Yonglong Wang, Rui State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coastal Wetland Ecosystems College of the Environment and Ecology Xiamen University Fujian 361102 China University of Chinese Academy of Sciences Beijing100049 China Ministry of Emergency Management Big Data Center Beijing100010 China Key Laboratory of Cyberspace and Data Security Ministry of Emergency Management Beijing100010 China School of Ecology and Nature Conservation Beijing Forestry University Beijing100083 China Transport Planning and Research Institute Ministry of Transport Laboratory of Transport Pollution Control and Monitoring Technology Beijing100028 China

PFOS and its precursors are of great concern due to their persistence and widespread presence in the environment. However, few studies have been conducted on their transformation and fate at a regional scale. We aim to address this gap by investigating their fate in the Bohai coastal region of China. Emissions of PFOS and its precursors are estimated based on anticipated POSF production in China from 1976 to 2025. Comparison of emission sources revealed that PFOS primarily originated from direct emissions, while precursors were notably influenced by indirect sources. Dynamic concentration curves of these pollutants in various compartments were simulated and validated against measured values. Spatial analysis indicated higher concentrations of these pollutants in sections 25 (Beijing), 26 (Tianjin), and 48 (Shenyang), with lower concentrations observed in sections 8, 16, and 24. The mass flux results highlighted significantly different pathways: precursors primarily interacted between upper and lower air with notable degradation, while PFOS mainly moved from lower air to soil and freshwater. Mass fluxes into the Bohai Sea suggested that PFOS mainly entered via water flow, while precursors primarily came through air deposition. Uncertainty analysis revealed higher uncertainties in soil and sediments than the oceans, with additional uncertainties in estimated emissions, and seawater salinity. This study provides a theoretical basis for modelling and regulating PFOS. © 2024

关键词： Miocene

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Learnable Operational Design Condition Monitor for Failure Prediction in Autonomous Driving

SSRN

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SSRN 2025年

作者： Xu, Haowei Bian, Yougang Li, Yang Qin, Hongmao Zhou, Hanchu Chang, Fangrong Wang, Shaofei Ye, Qing State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Research and Development Center of Transport Industry of Self-driving Technology China Merchants Chongqing Communications Technology Research and Design Institute Co. Ltd. Chongqing China School of Traffic and Transportation Engineering Central South University Changsha China Department of Data and System Engineering The University of Hong Kong Hong Kong School of Resources and Safety Engineering Central South University Changsha China School of Data Science China

Ensuring the safety of Autonomous Driving Systems (ADS) across diverse and unpredictable operational scenarios remains a significant challenge for accident prevention. Central to this effort is the continuous monitoring of Operational Design Conditions (ODC), which encompass both in-vehicle factors and external Operational Design Domain (ODD). However, the absence of robust methodologies to precisely identify ODC boundaries hinders the deployment and adoption of high-level ADS. This paper introduces a learnable ODC monitor designed to identify implicit operational design condition boundaries to prevent ADS failures. By integrating a state-space duality encoder and a Bayesian neural network decoder, we propose an efficient learnable ODC monitor, termed ODCNet. Furthermore, to further enhance prediction reliability, residual error analysis with linear discrimination is applied for calibrating prediction. Experimental results demonstrate ODCNet's superiority in failure prediction compared to baseline models, achieving a balance between safety and coverage. These findings contribute to advancing data-driven accident prediction and prevention strategies, ensuring safer deployments of ADS in complex environments. © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

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