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SynthBendText3D: a framework for generating scene text data in arbitrary orientations using a 3D graphics engine

International Journal of Information and Communication Technology 2025年第1期26卷 38-54页

作者： Guan, Zhao Zhang, Weilong School of Cyberspace Security and Computer Hebei University Baoding071000 China

To address the domain distribution mismatch between synthetic scene text data and real-world scene text data in arbitrary orientations, we introduce SynthBendText3D – a framework based on a 3D graphics engine that synthesises scene text data in various orientations. The framework generates a large number of text instances in arbitrary directions and constructs a 3D scene to position these instances. By leveraging domain randomisation techniques, it randomises scene parameters such as object arrangement, materials, lighting, and camera angles, ensuring a high degree of diversity in the synthesised data. Moreover, the framework incorporates a polygon reconstruction algorithm to annotate each synthesised text instance with polygonal bounding boxes. Experimental results demonstrate the effectiveness of the data generated by our framework. Copyright © The Author(s) 2024.

关键词： Three dimensional computer graphics

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Efficient and Secure Spatial Fuzzy Keyword Query

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IEEE Internet of Things Journal 2025年第10期12卷 14752-14770页

作者： Xie, Qingqing Zhu, Fatong Feng, Xia Jiangsu University School of Computer Science and Communication Engineering China Hainan University School of Cyberspace Security China

With the popularity of Location-based services and the explosive growth of spatial data, massive spatial data has been outsourced to cloud servers for storage and query services, such as spatial range and fuzzy keyword query. To protect data and user privacy, it is essential to encrypt the data and query requests before uploading them. However, encrypting the data makes search on ciphertext more challenging. Moreover, the existing schemes do not support spatial range and fuzzy keyword query (SFKQ for short) simultaneously. To this end, we first leverage the indecomposable property of primes and the Geohash algorithm to create an index vector and query trapdoor vector. We then propose concrete constructions for SFKQ, which support both spatial range query and fuzzy multi-keyword query in a single interaction. Additionally, we carefully design a novel Geohash-based index structure (referred to as GeoTree) along with a pruning strategy to accelerate search efficiency. Finally, we provide the formal security analysis to demonstrate that the proposed SFKQ scheme is indistinguishable under Chosen-Plaintext attacks (IND-CPA), and we conduct comprehensive experiments to validate the accuracy and efficiency. © 2025 IEEE.

关键词： Structured Query Language

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GSA-DT: A Malicious Traffic Detection Model Based on Graph Self-Attention Network and Decision Tree

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IEEE Transactions on Network and Service Management 2025年第2期22卷 2059-2073页

作者： Cai, Saihua Tang, Han Chen, Jinfu Lv, Tianxiang Zhao, Wenjun Huang, Chunlei Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212013 China Jiangsu Provincial Key Laboratory of Industrial Cyberspace Security Technology Zhenjiang212013 China

Malicious attack has shown a rapid growth in recent years, it is very important to accurately detect malicious traffic to defend against malicious attacks. Compared with machine learning and deep learning technologies, graph convolutional neural network (GCN) achieves better detection results of malicious traffic due to additional consideration of the correlation between network traffic features. However, existing GCN-based detection models suffer from fixed weight assignment, only focusing on local features, lack the ability to model graph structure and relationships as well as having gradient disappearance. To solve these problems, this paper proposes the GSA-DT model based on graph self-attention network and decision tree. GSA-DT first preprocesses the original network traffic to obtain better traffic features and labels, and then uses GCN to extract the topological structure of network traffic as well as capture the correlation relationships among traffic features, where the ReLU activation function is replaced by LeakyReLU to overcome the problems of neuron "death"and gradient disappearance during the training process;It also introduces the self-attention mechanism into GCN to assign larger weights to the key features to reduce the interference of redundant features. Finally, GSA-DT uses decision tree to perform the detection of malicious traffic. Experimental results on five network traffic datasets show that GSA-DT model improves the detection accuracy over 1% on average than seven advanced malicious traffic detection models, and it also performs better in F1-measure, TPR, FPR as well as stability. © 2004-2012 IEEE.

关键词： Graph neural networks

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Blockchain-Based Framework for Secure Sharing of Cross-Border Trade Data

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computers, Materials & Continua 2025年第5期83卷 2351-2373页

作者： Shenjian Xiao Xiaoli Qin Yanzhao Tian Zhongkai Dang School of Cyberspace Security Hainan UniversityHaikou570208China National Computer Network Emergency Response Technical Team Coordination Center of ChinaBeijing100029China

The advent of the digital age has consistently provided impetus for facilitating global trade,as evidenced by the numerous customs clearance documents and participants involved in the international trade process,including enterprises,agents,and government ***,the urgent issue that requires immediate attention is how to achieve secure and efficient cross-border data sharing among these government departments and enterprises in complex trade *** addressing this need,this paper proposes a data exchange architecture employing Multi-Authority Attribute-Based Encryption(MA-ABE)in combination with blockchain *** scheme supports proxy decryption,attribute revocation,and policy update,while allowing each participating entity to manage their keys autonomously,ensuring system security and enhancing trust among *** order to enhance system decentralization,a mechanism has been designed in the architecture where multiple institutions interact with smart contracts and jointly participate in the generation of public *** with the multi-party process execution engine Caterpillar has been shown to boost the transparency of cross-border information flow and cooperation between different *** scheme ensures the auditability of data access control information and the visualization of on-chain data *** MA-ABE scheme is statically secure under the q-Decisional Parallel Bilinear Diffie-Hellman Exponent(q-DPBDHE2)assumption in the random oracle model,and can resist ciphertext rollback attacks to achieve true backward and forward *** analysis and experimental results demonstrate the appropriateness of the scheme for cross-border data collaboration between different institutions.

关键词： Multi-authority attribute based encryption blockchain data sharing access control

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Adversarial Robustness Encoder as a Service for Classifiers on Internet of Things Devices

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IEEE Internet of Things Journal 2025年第9期12卷 11690-11697页

作者： Guo, Enting Zhao, Hong Pan, Shengli Su, Chunhua Li, Peng University of Aizu Division of Computer Science Fukushima965-0053 Japan North University of China School of Computer Science and Technology Taiyuan030051 China Beijing University of Posts and Telecommunications School of Cyberspace Security Beijing100876 China

Within the Internet of Things (IoT) landscape, Encoder as a Service (EaaS) is a cloud-based service for many AI empowered scenarios, such as autopilot and face-scan payment, enabling IoT devices to keep a light classifier model at local while remotely accessing powerful encoding models. However, adversarial examples like an image with imperceptible tiny perturbations that can lead to incorrect classifying results, make it challenging to achieve a robust EaaS-based classifier. Certified radius (R) emerges as a measure against such imperceptible tiny perturbations, and guarantees the trustworthiness of any input with perturbations smaller than R. Despite offering R related services, the substantial computational overhead from traditional methods, stemming from the extensive searches of R for each request, poses a barrier to their practical deployment. In this article, we identify the repetitive computations in the EaaS framework due to the fixed encoding model and propose a novel search cache scheme to speed up the R-related computation. Therefore, we propose large-scale efficient robust EaaS (ScaleRES) to address different R-related services: First, ScaleRES strategically stores previous computation results of R, to enable the reuse and refining of R across users. Then, ScaleRES obtains the average certified radius (ACR) efficiently with selective cached R. Finally, ScaleRES performs R filtering to enhance adversarial training for a robust EaaS-based classifier. Comprehensive evaluations demonstrate that in three distinct computations, ScaleRES offers significant savings in computational overhead compared to the conventional approach—70% for R computation as the number of clients increases, 35% for ACR of the entire test set, and 40% for adversarial training with robustness comparable to other works. © 2014 IEEE.

关键词： Lead refining

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Shorter ZK-SNARKs from square span programs over ideal lattices

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Cybersecurity 2025年第1期8卷 208-226页

作者： Xi Lin Heyang Cao Feng‑Hao Liu Zhedong Wang Mingsheng Wang Key Laboratory of Cyberspace Security Defense Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina School of Electrical Engineering&Computer Science Washington State UniversityPullmanWAUSA School of Cyber Science and Engineering Shanghai Jiaotong UniversityShanghaiChina

Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑tion for their appealing applications,e.g.,verifable computation and anonymous payment *** with the pre-quantum case,the practicability of this primitive in the post-quantum setting is still unsatisfactory,espe‑cially for the space *** tackle this issue,this work seeks to enhance the efciency and compactness of lat‑tice-based zk-SNARKs,including proof length and common reference string(CRS)*** this paper,we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic *** with previous works,our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK ***,the proof length of our scheme is around 23.3%smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al.(in:Proceedings of the 2021 ACM SIGSAC conference on computer and communications security,pp 212-234,2021),and the CRS length is 3.6×*** constructions follow the framework of Gennaro et al.(in:Proceedings of the 2018 ACM SIGSAC conference on computer and com‑munications security,pp 556-573,2018),and adapt it to the ring setting by slightly modifying the knowledge *** develop concretely small constructions by using module-switching and key-switching procedures in a novel way.

关键词： Zk-SNARKs Post-quantum Succinct argument

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Online Energy and Interference Management for Dynamic Target Tracking with Cellular-Connected UAV

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5496-5510页

作者： Zhan, Cheng Yan, Huan Fan, Rongfei Hu, Han Xu, Shubin Yang, Jian Southwest University School of Computer and Information Science Chongqing400715 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Institute of Technology School of Information and Electronics Beijing China CETC Research Institute of Cyberspace Security Beijing100846 China University of Science and Technology of China School of Information Science and Technology Hefei China

Cellular-connected Unmanned Aerial Vehicles (UAVs) have significant potential for target tracking in future cellular networks due to their broad coverage and operational flexibility. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV for target tracking, which encounters challenges such as unpredictable flight energy consumption from the stochastic movements of the tracking target and severe uplink interference from ground devices (GDs). To tackle these challenges, we propose a multi-stage stochastic optimization framework focused on energy-efficient target tracking with interference coordination. Our objective is to optimize the long-Term average uplink throughput of both aerial users and GDs by jointly optimizing the UAV's trajectory, power allocation, and cell association across multiple orthogonal communication resource blocks (RBs). The formulated stochastic non-convex problem is first transformed into a deterministic problem for each time slot by using the Lyapunov optimization framework. An online optimization strategy is proposed, utilizing the optimal structure, alternative optimization, and successive convex approximation (SCA) techniques. Simulation results show that the proposed approach significantly enhances network throughput and UAV energy queue stability compared to existing baseline schemes. © 2002-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

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Frontiers of computer Science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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Chinese Character Component Segmentation Based on Character Structure Masks 17th

Chinese Character Component Segmentation Based on Character...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Li, Haiyan Yang, Fang School of Cyberspace Security and Computer Hebei University Baoding China Intelligent graphic Information Processing Institute Hebei University Baoding China

ISBN: (纸本)9789819608843

To address the issue where rectangular anchor boxes in object detection-based Chinese character component segmentation cannot segment semi-enclosed Chinese characters, this paper proposes a method for segmenting Chinese character components based on Chinese character structure masks. This method utilizes a U-Net encoder with ResNet as the backbone network, transforming the segmentation of Chinese character components into the generation of Chinese character structure masks. First, this study proposes a Res-CBAM module, which leverages the structural features of Chinese characters by incorporating CBAM into the residual U-Net network, effectively solving the problem of incomplete segmentation of Chinese character components. Secondly, a vector-guided supervision mechanism is designed to guide the training process of the model by designing structure vectors of Chinese characters, effectively addressing the issue of component adhesion in Chinese characters. Experimental results demonstrate that compared to traditional object detection methods, this method can achieve fast and efficient segmentation in lightweight networks by training small datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object detection

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Blockchain-Assisted Anonymous Data Sharing Scheme with Full Accountability for CloudIoT 24th

Blockchain-Assisted Anonymous Data Sharing Scheme with Full...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Yu, Chuntang Feng, Xia Zhan, Yongzhao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Zhenjiang212013 China School of Cyberspace Security Hainan University Hainan Haikou570228 China

ISBN: (纸本)9789819615414

The combination of cloud computing and the Internet of Things has created numerous possibilities for data sharing applications that prioritize privacy, accountability, and efficiency. Currently, many data sharing schemes based on Attribute-Based Encryption (ABE) show promise due to their fine-grained access control and one-to-many data encryption. However, existing schemes often struggle to find a balance between protecting user identity privacy and ensuring accountability. To address this limitation, this paper proposes a Blockchain-Assisted Anonymous Data Sharing scheme (BAADS) that provides full accountability. BAADS incorporates ElGamal encryption, signature of knowledge, and blockchain technology to design an anonymous identity management mechanism. Additionally, it integrates a reconstructed attribute encryption scheme that supports data auditing, identity tracking, and verifiable outsourced decryption. Compared to existing schemes, the key advantage of BAADS is that it enables both anonymous data sharing and full accountability for malicious or negligent behavior of all participants (data owners, data users, and cloud service providers). We have formalized the security aspects of BAADS and demonstrated its security in the random oracle model. Moreover, we have implemented the scheme using Hyperledger Fabric and OpenABE, and the experimental results have shown the effectiveness and availability of the scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accountability Anonymous data sharing Blockchain Privacy-preserving

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