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arXiv 2022年

作者： Wang, Tianyi Liao, Xin Chow, Kam Pui Lin, Xiaodong Wang, Yinglong Department of Computer Science The University of Hong Kong Hong Kong College of Computer Science and Electronic Engineering Hunan University China School of Computer Science University of Guelph Canada Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. We identify three reliability-oriented research challenges in the current Deepfake detection domain: transferability, interpretability, and robustness. Moreover, while solutions have been frequently addressed regarding the three challenges, the general reliability of a detection model has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake-related cases in court. We, therefore, introduce a model reliability study metric using statistical random sampling knowledge and the publicly available benchmark datasets to review the reliability of the existing detection models on arbitrary Deepfake candidate suspects. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of the reliably qualified detection models as reviewed in this survey. Reviews and experiments on the existing approaches provide informative discussions and future research directions for Deepfake detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Data reliability

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Image Electronic Evidence Screening Based on Improved SSD

Image Electronic Evidence Screening Based on Improved SSD

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Yafei Liu Liehui Jiang Tieming Liu Youwei Zhang Zhongyuan Network Security Research institute Zhengzhou University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou China

ISBN: (纸本)9781665447300

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. This method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. The experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm.

关键词： Image recognition Convolution Target recognition Fuses Neural networks Stacking Signal processing algorithms

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An improved RL-based framework for multiple biomedical event extraction via self-supervised learning

An improved RL-based framework for multiple biomedical event...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zhao, Yao Zhao, Weizhong Jiang, Xingpeng He, Tingting Su, Bianping Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Hubei Wuhan430079 China School of Computer Central China Normal University Hubei Wuhan430079 China National Language Resources Monitoring and Research Center for Network Media Central China Normal University Hubei Wuhan430079 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal University Guilin541004 China Xi'An University of Architecture and Technology Shanxi Xi'an China

ISBN: (纸本)9781665401265

The main goal of biomedical event extraction is to structurally extract biomedical events from texts, however, the specificity of the domain makes both text modeling and data annotation very difficult. We propose a self-supervised learning-based data augmentation method in this paper and design specific augmentation strategies for biomedical entities and event triggers in biomedical texts, which solves the problem of sparse annotation data to some extent. In addition we improve the reinforcement learning-based event extraction method to improve the training efficiency of the model. The experiments on two datasets demonstrate the effectiveness of our method. © 2021 IEEE.

关键词： Reinforcement learning

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Research on the Current State of Privacy Risks in Children’s Mobile Applications

Research on the Current State of Privacy Risks in Children’...

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Computer technology and Information Science (ISCTIS), International Symposium on

作者： Xinrui Geng Tao Liu Chong Shang Jianxiang Chen Yueran Lu Yan Zhang Lifeng Wang School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou China Key Laboratory of Mobile Application Innovation and Governance Technology MIIT Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Agricultural Engineering Shanxi Agricultural University China School of Software Engineering Tianjin Normal University Tianjin China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China Shandong Yeda Smart City Operation Technology Co. Ltd. Shandong China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In the digital age, children increasingly access a vast world of information through mobile applications. Despite global regulations and protective measures by app stores, privacy breaches in these apps persist. This study investigates privacy issues and threats in children’s mobile applications by comparing APKs and legal provisions across different countries. It examines 70 representative children’s apps from China, Europe, the U.S., and Japan through static code analysis and dynamic runtime testing. Using a mixed-methods approach, the research identifies integrated third-party services, data transmission protocols, permission requests, and conducts code audits to assess privacy compliance. It also simulates real usage scenarios to track data flow and detect unauthorized data collection and sharing practices. Furthermore, the study compares national privacy laws, focusing on data minimization, parental consent mechanisms, data encryption, and storage duration regulations. The goal is to provide empirical evidence and recommendations for optimizing global children’s online privacy protection policies.

关键词： Pediatrics Data privacy Codes Law Europe Privacy breach Regulation Mobile applications Protection Testing

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Integrated Multi-featured Android Malicious Code Detection 5th

Integrated Multi-featured Android Malicious Code Detection

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5th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2019

作者： Yu, Qing Zhao, Hui Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9789811501173

To solve the problem that using a single feature cannot play the role of multiple features of Android application in malicious code detection, an Android malicious code detection mechanism is proposed based on integrated learning on the basis of dynamic and static detection. Considering three types of Android behavior characteristics, a three-layer hybrid algorithm was proposed. And it combined the malicious code detection based on digital signature to improve the detection efficiency. The digital signature of the known malicious code was extracted to form a malicious sample library. The authority that can reflect Android malicious behavior, API call and the running system call features were also extracted. An expandable hybrid discriminant algorithm was designed for the above three types of features. The algorithm was tested with machine learning method by constructing the optimal classifier suitable for the above features. Finally, the Android malicious code detection system was designed and implemented based on the multi-layer hybrid algorithm. The experimental results show that the system performs Android malicious code detection based on the combination of signature and dynamic and static features. Compared with other related work, the system has better performance in execution efficiency and detection rate. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Efficiency

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computing Power in the Sky: Digital Twin-assisted Collaborative computing with Multi-UAV networks

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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Shellshock Bash Vulnerability Modeling Analysis Based on Petri Net

Shellshock Bash Vulnerability Modeling Analysis Based on Pet...

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International Conference on networking and network Applications (NaNA)

作者： Liumei Zhang Xi Deng Yichuan Wang School of Computer Science Xi’an Shiyou University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi’an China

The enhancement of Internet connectivity and the increase of information transmission speed yield the increasing frequency of network attacks. The Shellshock attackers often exploit bash vulnerability to read the content behind the function definition when importing environment variable functions. Then, malicious scripts can be executed in systems and servers, which compromises everything. Therefore, this paper proposes a formal modeling analysis method for the Shellshock Bash basis and automates the analysis of the patched position of the model. The relationship between the established model and the actual attack process is also discussed, which is a feasible reference for exploring unknown vulnerabilities and the location of corresponding patches.

关键词： Analytical models Firing Petri nets Information processing Tools Software Internet

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Auction Theory and Game Theory Based Pricing of Edge computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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Intelligent decoding method for Ground-penetrating radar based on improved YOLOv5

Intelligent decoding method for Ground-penetrating radar bas...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： ZeYu Chen YongKang Wang HongLiang Liu Chuan Wang XinBo Jiang Ning Zhang Zhen Huang Lei Jiang 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) JiNan China Technological Innovation Department Shandong Hi-Speed Group Co Ltd JiNan China School of Qilu Transportation Shandong University JiNan China No.1 Engineering Development Co. Ltd. of China Railway 14Th Bureau Group RiZhao China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Ground-penetrating radar (GPR) is a non-invasive detection technique that effectively gathers information about subsurface materials. In tunneling projects, GPR can provide a fast and accurate forecast of the geological conditions in front of tunnel boring, which provides an important reference basis for tunnel construction. Currently, the interpretation of GPR images relies heavily on manual expertise, resulting in low efficiency and minimal automation. This creates an urgent need for effective and accurate identification of poor geological conditions depicted in radar images. In this study, we present the GPR-YOLOv5 model, which is designed to detect unfavorable geologic features ahead of the tunnel face from GPR images. We enhance this model with the CA attention mechanism, GSConv module, ghostC3 module, and the VariFocal loss function to improve decoding accuracy while minimizing computational load. Additionally, we integrate this algorithm with established modern object detection techniques, and our experimental results demonstrate its effectiveness.

关键词： YOLO Accuracy Ground penetrating radar Attention mechanisms Geology Computational modeling Radar imaging Tunneling Decoding Load modeling

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CODH++: Macro-semantic differences oriented instance segmentation network

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Expert Systems with Applications 2022年 202卷 1页

作者： Zhang, Wenchao Fu, Chong Cao, Lin Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China School of Computer Science The University of Auckland New Zealand

With the idea of divide and rule, there exist two different forms of semantic features flowing in the two stage instance segmentation paradigms. They are the global features at the image level and the instance features at the region-wise. The most significant distinction of the two macro-semantic morphological features lies in the different relevance of neighborhood features caused by background noise. Hence, we should consider different situations and make different schemes. Notice that the fields-of-view determines the range of local features that can be perceived in the convolution operation and implies the representation capability of the network. To this end, for FPN and Mask Head in two stage paradigms, we propose a more efficient methodology with Group-Inception and Asymmetric-Inception modules. This proposed methodology can act as a drop-in replacement to upgrade the plain convolution operation, which enables the network to look more via modeling long-range dependencies. Our method is simple yet effective. Quantitatively, we can significantly improve the state-of-the-art frameworks, including Mask R-CNN, Mask Scoring R-CNN, Cascade Mask R-CNN, and HTC by about 1.2%–2.2% AP on MS COCO test-dev yet with fewer parameters and FLOPs. Moreover, the proposed approach achieves competitive performances on the Scapes, KINS and SBD datasets. The source code of our method will be made available. © 2022 Elsevier Ltd

关键词： Semantics

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