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Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

International Journal of Computers and Applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

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Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

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Computers, Materials & Continua 2024年第8期80卷 3047-3065页

作者： Jianfeng Lu Caijin Li Xiangye Huang Chen Cui Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310000China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

The widespread availability of digital multimedia data has led to a new challenge in digital *** source camera identification algorithms usually rely on various traces in the capturing ***,these traces have become increasingly difficult to extract due to wide availability of various image processing *** Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera ***,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall *** this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these *** proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature *** representation is then fed into a subsequent camera fingerprint classification *** upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone ***,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

关键词： Source camera identification camera forensics convolutional neural network feature fusion transformer block graph convolutional network

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Quantum-Resistant Multi-Feature Attribute-Based Proxy Re-Encryption Scheme for Cloud Services

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Computer Modeling in Engineering & Sciences 2024年第1期138卷 917-938页

作者： Jinqiu Hou Changgen Peng Weijie Tan Hongfa Ding State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Advanced Manufacturing Technology Ministry of EducationGuizhou UniversityGuiyang550025China College of Information Guizhou University of Finance and EconomicsGuiyang550025China

Cloud-based services have powerful storage functions and can provide accurate ***,the question of how to guarantee cloud-based services access control and achieve data sharing security has always been a research *** the attribute-based proxy re-encryption(ABPRE)schemes based on number theory can solve this problem,it is still difficult to resist quantum attacks and have limited expression *** address these issues,we present a novel linear secret sharing schemes(LSSS)matrix-based ABPRE scheme with the fine-grained policy on the lattice in the ***,to detect the activities of illegal proxies,homomorphic signature(HS)technology is introduced to realize the verifiability of ***,the non-interactivity,unidirectionality,proxy transparency,multi-use,and anti-quantum attack characteristics of our system are all ***,it can efficiently prevent the loss of processing power brought on by repetitive authorisation and can enable precise and safe data sharing in the ***,under the standard model,the proposed learning with errors(LWE)-based scheme was proven to be IND-sCPA secure.

关键词： Lattice learning with errors attribute-based proxy re-encryption linear secret sharing schemes

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Robust Deep One-Class Classification Time Series Anomaly Detection

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Computers, Materials and Continua 2025年第3期83卷 5181-5197页

作者： Yang, Zhengdao Wang, Xuewei Chen, Yuling Dou, Hui Sang, Haiwei State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China College of Computer Science and Technology Weifang University of Science and Technology Weifang261000 China School of Mathematics and Big Data Guizhou Education University Guiyang550018 China

Anomaly detection (AD) in time series data is widely applied across various industries for monitoring and security applications, emerging as a key research focus within the field of deep learning. While many methods based on different normality assumptions perform well in specific scenarios, they often neglected the overall normality issue. Some feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection, leading to decreased performance. Additionally, real-world time series samples are rarely free from noise, making them susceptible to outliers, which further impacts detection accuracy. To address these challenges, we propose a novel anomaly detection method called Robust One-Class Classification Detection (ROC). This approach utilizes an autoencoder (AE) to learn features while constraining the context vectors from the AE within a sufficiently small hypersphere, akin to One-Class Classification (OC) methods. By simultaneously optimizing two hypothetical objective functions, ROC captures various aspects of normality. We categorize the input raw time series into clean and outlier sequences, reducing the impact of outliers on compressed feature representation. Experimental results on public datasets indicate that our approach outperforms existing baseline methods and substantially improves model robustness. Copyright © 2025 The Authors.

关键词： Support vector machines

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Achieving reliable and anti-collusive outsourcing computation and verification based on blockchain in 5G-enabled IoT

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Digital Communications and Networks 2022年第5期8卷 644-653页

作者： Linjie Wang Youliang Tian Jinbo Xiong School of Data Science Tongren UniversityTongren554300China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou UniversityGuiyang550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things(IoT)cloud to improve transmission efficiency,which has created a novel paradigm for improving the speed of common connected objects in ***,although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services,security concerns,such as a lack of reliability and collusion attacks,still exist in the outsourcing *** this paper,we propose a reliable,anti-collusion outsourcing computation and verification protocol,which uses distributed storage solutions in response to the issue of centralized storage,leverages homomorphic encryption to deal with outsourcing computation and ensures data ***,we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum,which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion *** results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure,reliable,and more effective compared with state-of-the-art approaches.

关键词： Outsourcing computation Ethereum Smart contract public verification Homomorphic encryption

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Vehicle data security based on Underground Garage Location - Dead Reckoning Method 3

Vehicle Data Security based on Underground Garage Location -...

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3rd IEEE International Conference on Information technology, big data and Artificial Intelligence, ICIBA 2023

作者： Zhang, Xi Li, Fangwei Guo, Shuang Pei, Yadong Zhang, Yanlin Chongqing College of Mobile Communication 401520 China Chongqing Key Laboratory of Public Big Data Security Technology 401420 Chongqing401520 China

ISBN: (纸本)9781665490788

With the widespread popularity of the Internet of Things and the rapid development of vehicle communication technology, it has gradually become normal to realize vehicle evaluation, selection, and large-scale data distribution based on edge infrastructure. Collaborative data distribution through Wifi and CV modes effectively improves traffic efficiency, reduces road traffic accident rates, and improves user driving experience. Therefore, this paper will study the data security technology of Internet of Vehicles collaboration based on the dead reckoning method. To realize the security of data interaction, this paper describes the research of indoor positioning and the Internet of vehicles, respectively. © 2023 IEEE.

关键词： Vehicles

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Cross-Chain Asset Trading Scheme Without Trusted Third-Party 2nd

Cross-Chain Asset Trading Scheme Without Trusted Third-Party

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2nd International Conference on data security and Privacy Protection, DSPP 2024

作者： Li, Bo Chen, Yuling Huang, Siyuan Yang, Yuxiang State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Guiyang550025 China

ISBN: (纸本)9789819785391

The development of blockchain technology is influenced by user diversity and application scenarios, resulting in limitations on the applicability of assets. Although cloud computing is renowned for facilitating cross-chain token transfers, it lacks a robust trust mechanism. Existing cross-chain transaction solutions are typically confined to interactions between two blockchains or within established networks. Hence, constructing a trustless cross-chain transaction protocol remains a significant challenge. This study proposes a trustless third-party cross-chain asset trading solution, Vchain, based on a Blockchain of Blockchains architecture and driven by a relay chain. This solution supports various blockchain ecosystem accounts and leverages cloud computing to integrate external data sources and API calls to obtain the latest token exchange rates, ensuring transaction accuracy and real-time performance. Simulation results demonstrate that Vchain’s cross-chain transaction costs are 69.25% lower than those of existing models. The research indicates that Vchain offers significant advantages in enhancing transaction efficiency and reducing costs, effectively addressing current challenges in cross-chain transactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Asset transactions Cross-chain Relay chain

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RUP2S- YOLO: An Improved YOLOv8-Based Algorithm for Dense Pedestrian Detection 5

RUP2S- YOLO: An Improved YOLOv8-Based Algorithm for Dense Pe...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Wang, Juanmin Huang, Kun Pi, Jianyong College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guizhou Guiyang China

ISBN: (纸本)9798350385557

Dense pedestrian detection is a key research direction in the field of computer vision, which plays a significant role in large-scale crowded public spaces. It provides strong technical support for security assurance, traffic management, and behavioral analysis in public areas. To address the issue of missed detection of small-sized occluded pedestrians in dense scenarios, this paper proposes an improved detection algorithm based on YOLOv8, named RUP2S-YOLO. For the backbone network, the RFCBAM module is used to replace the convolution module to enhance the capability of capturing subtle features and better extract the characteristics of small-scale pedestrians;for the C2f module, the UKMS module is designed as a substitution aiming to improve the model's capacity to process information of different scales;for the detection head, the P2S module is introduced for the accurate positioning of small-scale targets. Experiments on dense pedestrian datasets show that the improved RUP2S-YOLO outperforms the original algorithm in terms of performance, achieving an AP50 of 82.8%, and an AP50:95 of 55.8%, which represents an increase of 2.2% and 2.8% respectively compared to the original algorithm. Meanwhile, in comparison with other leading pedestrian detection models, the improved YOLOv8 algorithm presented in this article also demonstrates competitive advantages. © 2024 IEEE.

关键词： Object detection

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Indoor Location and data security of Traffic Flow based on Fingerprint database Filtering 3

Indoor Location and Data Security of Traffic Flow based on F...

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3rd IEEE International Conference on Information technology, big data and Artificial Intelligence, ICIBA 2023

ISBN: (纸本)9781665490788

With the development of urbanization, the number of vehicles is gradually increasing, and the vehicle is gradually developing toward the direction of intelligence. Internet of vehicles technology is gradually entering millions of homes. In this paper, traffic flow data location and security algorithm are designed, and node data verification is carried out. This paper mainly studies the influence of fingerprint database filtering and matching methods on WiFi indoor locations based on RSSI. The results of this paper show that the designed system can transmit traffic flow data safely and accurately. © 2023 IEEE.

关键词： Location

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