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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Feng, Zhengqian Yue, Xiyao Li, Wang Zhou, Mingle Han, Delong Li, Gang Shandong Scicom Information and Economy Research Institute CO.. Ltd China Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Deep learning technology has been widely applied in industrial quality inspection tasks to improve the accuracy of defect detection, object recognition, and classification. However, in the task of object detection, both feature-based and regression-based traditional knowledge distillation methods impose overly strict constraints on the student model. To address the above issues, this article proposes Global Fusion and Feature Restoration Knowledge Distillation(FRD). FRD integrates contextual information into the channel through a Global Fusion Module (GFM), and further utilizes attention mechanisms to adaptively focus on the distillation region after separating the foreground background. FRD also uses a Mask Feature Restoration (MFR) to mask and restore a portion of student features, improving the learnability of the model. At the same time, FRD adopts a comprehensive approach of multiple loss superposition constraints, rather than simply using MSE losses to imitate the features of teachers. Experiments have shown that FRD can effectively improve model performance in industrial defect detection tasks. On the aluminum surface defect dataset, FRD increased the mAP index of RetinaNet-Res50 from 57.7% to 61.7%. We also confirmed the effectiveness of FRD for general object detection on the Coco dataset. On a randomly selected COCO dataset containing 4000 images, FRD increased the mAP metric of RetinaNet-Res50 from 21.5% to 43.6%. © 2024 IEEE.

关键词： Students

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Advancing MobileNet security: Weighted Adversarial Learning in Convolutional Neural networks 11

Advancing MobileNet Security: Weighted Adversarial Learning ...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Quadri, Hakeem Gu, Bruce Wang, Hua Islam, Sardar Victoria University Department of Information Technology Melbourne Australia Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Jinan China Victoria University Institute for sustainable Industries and Liveable Cities Melbourne Australia

ISBN: (纸本)9798350385670

Convolutional Neural networks (CNN), particularly low latency models such as MobileNets have excelled in many applications, including object detection in images, speech recognition and natural language processing, but they are vulnerable to subtle perturbations that are virtually imperceptible to the human eye yet can deceive the network into misclassifying images. To enhance the robustness of such CNNs against adversaries, conventional adversarial training methods treat all data points as equally important and susceptible to attack. A weighted adversarial learning algorithm is developed in a Stackelberg game framework. This approach prioritizes data points that are more susceptible to attacks during network training. To further optimize the algorithm, we employ a Reinforcement Learning (R L) agent to fine-tune the hyperparameters of the model, thereby increasing its robustness. Our findings indicate an increased robustness of 66.18 % of the Weighted Adversary Reinforced Stackelberg Learning (WARS) against the traditional adversarial training of 64.72 % in a one epoch training, using the CIFAR-10 dataset. We conclude that the WARS represents a valuable adversarial training method for bolstering the robustness of low latency CNN models. © 2024 IEEE.

关键词： Adversarial machine learning

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A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras

arXiv

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arXiv 2025年

作者： Xiao, Teng Hu, Qi Yan, Qingsong Liu, Wei Ye, Zhiwei Deng, Fei School of Computer Science Hubei University of Technology Wuhan China Hubei Key Laboratory of Green Intelligent Computing Power Network Wuhan China School of Surveying and Mapping Wuhan University Wuhan China Wuhan Tianjihang Information Technology Co. Ltd. Wuhan China

Pan-Tilt-Zoom (PTZ) cameras with wide-angle lenses are widely used in surveillance but often require image rectification due to their inherent nonlinear distortions. Current deep learning approaches typically struggle to maintain fine-grained geometric details, resulting in inaccurate rectification. This paper presents a Forward Distortion and Backward Warping network (FDBW-Net), a novel framework for wide-angle image rectification. It begins by using a forward distortion model to synthesize barrel-distorted images, reducing pixel redundancy and preventing blur. The network employs a pyramid context encoder with attention mechanisms to generate backward warping flows containing geometric details. Then, a multi-scale decoder is used to restore distorted features and output rectified images. FDBW-Net’s performance is validated on diverse datasets: public benchmarks, AirSim-rendered PTZ camera imagery, and real-scene PTZ camera datasets. It demonstrates that FDBW-Net achieves SOTA performance in distortion rectification, boosting the adaptability of PTZ cameras for practical visual applications. © 2025, CC BY.

关键词： Nonlinear distortion

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Neural network-Based Safety Optimization Control for Constrained Discrete-Time Systems 49

Neural Network-Based Safety Optimization Control for Constra...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wang, Xueli Zhao, Shangwei Yang, Ming Wu, Xiaoming Wang, Xin Xu, Shuo Hu, Xu Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Jinan China Shanghai Jiao Tong University Department of Automation Shanghai China Public Security Bureau of Jinan Jinan China

ISBN: (纸本)9798350331820

This paper proposes a constraint-aware safety control approach via adaptive dynamic programming (ADP) to address the control optimization issues for discrete-time systems subjected to state constraints. First, the constrained control framework is established via the primal-dual approach with the modified Lagrangian based on the relaxed barrier function. Herein, the Lagrangian multiplier is designed to achieve the trade-off between optimization performance and state constraints. In addition, the sub optimality of the dual method is built by proving that the dual gap can be arbitrarily small. And the value iteration algorithm is utilized to implement the constraint-aware ADP controller. Furthermore, the weight estimation error is proved to be bounded when the learning rate satisfies a given sufficient condition. Finally, numerical simulation proves the effectiveness and superiority of the proposed method. © 2023 IEEE.

关键词： Iterative methods

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JTEA: Implementing Semi-Supervised Entity Alignment Using Joint Teaching Strategies

JTEA: Implementing Semi-Supervised Entity Alignment Using Jo...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Kai Zhao, Jing Ding, Lichao Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method. © 2024 IEEE.

关键词： Mapping

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Dynamic detection technology of encryption algorithm based on Pin 9

Dynamic detection technology of encryption algorithm based o...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Qu, Junpeng Du, Gaolei Sun, Yunge Fu, Yong Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Quan Cheng Laboratory Jinan250103 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

Aiming at the problem that malicious software uses encryption algorithm to hide its own malicious code and avoid detection, a dynamic detection technology based on Pin encryption algorithm is proposed. Use Pin to get the instruction flow of executable file running, then identify the function call in the instruction flow to get the function call framework, find the loop body in the calling function, and then carry out the encryption algorithm instruction detection and encryption algorithm constant detection on the loop body. If the detection passes, it can be considered that a component of the encryption algorithm is found. Then the complete encryption algorithm is found according to the generated function call framework. Experiments show that this method can effectively identify the encryption algorithms hidden in executable files. © 2024 IEEE.

关键词： Encryption algorithms

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BLAC: A Blockchain-Based Lightweight Access Control Scheme in Vehicular Social networks 25th

BLAC: A Blockchain-Based Lightweight Access Control Scheme i...

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25th International Conference on information and Communications security, ICICS 2023

作者： Zuo, Yuting Xu, Li Zhang, Yuexin Kang, Zhaozhe Zhao, Chenbin Fujian Normal University Fuzhou China Fujian provincial Key Laboratory of Network Security and Cryptology Fuzhou China Wuhan University Wuhan China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan China

ISBN: (纸本)9789819973552

Vehicular Social networks (VSNs) rely on data shared by users to provide convenient services. Data is outsourced to the cloud server and the distributed roadside unit in VSNs. However, roadside unit has limited resources, so that data sharing process is inefficient and is vulnerable to security threats, such as illegal access, tampering attack and collusion attack. In this article, to overcome the shortcomings of security, we define a chain tolerance semi-trusted model to describe the credibility of distributed group based on the anti tampering feature of blockchain. We further propose a Blockchain-based Lightweight Access Control scheme in VSNs that resist tampering and collusion attacks, called BLAC. To overcome the shortcomings of efficiency, we design a ciphertext piece storage algorithm and a recovery one to achieve lightweight storage cost. In the decryption operation, we separate a pre-decryption algorithm based on outsourcing to achieve lightweight decryption computation cost on the user side. Finally, we present the formal security analyses and the simulation experiments for BLAC, and compare the results of experiments with existing relevant schemes. The security analyses show that our scheme is secure, and the results of experiments show that our scheme is lightweight and practical. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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An Encryption Algorithm Cycle Identification Method Based on Bit Execution 9

An Encryption Algorithm Cycle Identification Method Based on...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Sun, Yunge Du, Gaolei Qu, Junpeng Fu, Yong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Quan Cheng Laboratory Jinan250103 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

Ransomware uses symmetric encryption algorithms to lock user files to extort money. This paper proposes a cryptographic algorithm loop identification method based on bit-precise execution, aiming to solve the problem of identifying unknown loops. The structure of the loop body of encryption algorithms is fixed and usually includes nonlinear operations and linear operations. This article converts the loop body in the unknown software into a set of Boolean formulas, and converts the malware into a register data flow through the symbolic executor, thereby converting it into a set of Boolean formulas for execution. Execute known encryption algorithms with bit accuracy and establish a Boolean formula input-output relationship library for cryptographic components. The two sets of Boolean formulas are passed through a designed input matrix to achieve the Boolean formula mapping and input variable mapping of the confirmation algorithm, which can effectively judge the encryption algorithms in some malware. © 2024 IEEE.

关键词： Encryption algorithms

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Enhancing Stance Detection on Social Media via Core Views Discovery 27

Enhancing Stance Detection on Social Media via Core Views Di...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Yan, Yu Shen, Yinghan Liu, Teli Jiang, Xuhui Yin, Dechun School of Information and Network Security People Public Security University of China China Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781643685489

Stance detection aims to identify the expressed attitude towards a target from the text, which is significant for learning public cognition from social media. The short and implicit nature of social media users' expressions potentially results in the stance understanding bias of the model. To address this problem, introducing external background information is helpful to mitigate these biases and enhance explainability. The core view, reflecting the motivations and reasons behind an individual's stance toward the target, can be summarized and extracted from collective tweets, which can serve as a reference for stance detection. In this study, we propose the Stance Detection via Core View Discovery (SD-CVM), where the core views are used for background information modeling. Specifically, we construct a joint classifier combining the semantic understanding of tweets and their relevant core views from the public. We utilize the Large Language Model (LLM) to extract core views with stances from tweets and use these core views as background references for tweets. To further optimize the tweet understanding, we develop the contrastive and rebalancing mechanism by incorporating stance supervision signals for training. Experiments on two representative datasets demonstrate the excellent performance of our method. © 2024 The Authors.

关键词： Tweets

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An automated construction method for symmetric encryption round function models based on dynamic analysis 9

An automated construction method for symmetric encryption ro...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Du, Gaolei Sun, Yunge Qu, Junpeng Fu, Yong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Quan Cheng Laboratory Jinan250103 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798350391367

With the deepening of informatization, the incidence of cybercrimes utilizing malicious software for attacks is on the rise. This paper addresses the detection issue of symmetric encryption in malicious software from the perspective of cryptography, proposing an automated method for constructing a symmetric encryption round function model based on dynamic analysis. This method tracks data flow at the granularity of registers to characterize Directed Acyclic Graph (DAG) construction of round function models. Initially, dynamic execution traces of binary files are captured using Intel Pin, identifying loops within them. Data flow tracking at the register level within loops is performed, creating DAG, and analyzing whether the loops exhibit characteristics of symmetric cipher round functions, thus constructing the round function model. This approach enables the detection of symmetric encryption within binary files and the construction of round function models, offering a novel solution for fine-grained symmetric cipher recognition and laying a foundation for detecting malicious software containing symmetric encryption. © 2024 IEEE.

关键词： Data flow graphs

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