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SSRN 2023年

作者： Jiang, Dong Yuan, Zhen Li, Wen-Xin Wang, Hai-Tao Lu, Liang-Liang School of Internet Anhui University Hefei230039 China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui University Hefei230601 China Key Laboratory of Optoelectronic Technology of Jiangsu Province Nanjing Normal University Nanjing210023 China National Laboratory of Solid State Microstructures Nanjing University Nanjing210093 China Hefei National Laboratory Hefei230088 China

Due to the strong correlation among adjacent pixels, image encryption schemes typically perform multiple rounds of confusion and diffusion to protect images against various attacks, which is time-consuming and cannot meet the real-time requirements for video encryption. Existing works, therefore, realize video encryption by simplifying encryption process or selectively encrypting specific pixels, resulting in lower security compared to image encryption. This paper proposes a real-time chaotic video encryption strategy based on parallel computing. It splits video frame into sub-frames, creates a set of threads to concurrently perform confusion and diffusion operations on corresponding sub-frames, and efficiently outputs encrypted frames. To assess its performance, two cryptosystems are implemented using different chaotic systems. Encryption speed evaluation demonstrates a significant acceleration in byte generation, confusion, and diffusion phases, enabling real-time encryption on different X86 platforms. The average encryption time is less than 42ms, despite performing five rounds of confusion and diffusion operations on each frame. Statistical and security analysis prove that the deployed cryptosystems exhibit exceptional statistical properties and provide resistance to different attacks. Moreover, our method is adopted to implement a multi-user, real-time mobile video communication system using embedded systems, validating the feasibility of the proposed strategy for various application scenarios. © 2023, The Authors. All rights reserved.

关键词： Cryptography

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Dual Progressive Transformations for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2022年

作者： Huo, Dongjian Su, Yukun Wu, Qingyao School of Software and Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Nanyang Technological University Singapore Pazhou Lab Guangzhou China

Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision. The current state-of-the-art CNN-based methods usually adopt Class-Activation-Maps (CAMs) to highlight the potential areas of the object, however, they may suffer from the part-activated issues. To this end, we try an early attempt to explore the global feature attention mechanism of vision transformer in WSSS task. However, since the transformer lacks the inductive bias as in CNN models, it can not boost the performance directly and may yield the over-activated problems. To tackle these drawbacks, we propose a Convolutional Neural Networks Refined Transformer (CRT) to mine a globally complete and locally accurate class activation maps in this paper. To validate the effectiveness of our proposed method, extensive experiments are conducted on PASCAL VOC 2012 and CUB-200-2011 datasets. Experimental evaluations show that our proposed CRT achieves the new state-of-the-art performance on both the weakly supervised semantic segmentation task the weakly supervised object localization task, which outperform others by a large margin. Code is available at https://***/huodongjian0603/crt Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

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A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

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Vicinagearth 2025年第1期2卷 1-18页

作者： Wang, Bingshu Li, Changping Zou, Wenbin Zhang, Yongjun Chen, Xuhang Chen, C.L. Philip School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing & Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen University Shenzhen China Yongjun Zhang is with the State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou China

With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.

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TBDD: A New Trust-based, DRL-driven Framework for Blockchain Sharding in IoT

arXiv

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arXiv 2023年

作者： Zhang, Zixu Yu, Guangsheng Sun, Caijun Wang, Xu Wang, Ying Zhang, Ming Ni, Wei Liu, Ren Ping Georgalas, Nektarios Reeves, Andrew The Global Big Data Technologies Centre University of Technology Sydney2007 Australia The Data61 CSIRO Sydney2015 Australia Zhejiang Lab Hangzhou China The School of Electronics and Information Hangzhou Dianzi University Hangzhou310018 China The School of Cyber Engineering Xidian University Xi’an710071 China British Telecom Martlesham United Kingdom

Integrating sharded blockchain with IoT presents a solution for trust issues and optimized data flow. Sharding boosts blockchain scalability by dividing its nodes into parallel shards, yet it’s vulnerable to the 1% attacks where dishonest nodes target a shard to corrupt the entire blockchain. Balancing security with scalability is pivotal for such systems. Deep Reinforcement Learning (DRL) adeptly handles dynamic, complex systems and multi-dimensional optimization. This paper introduces a Trust-based and DRL-driven (TBDD) framework, crafted to counter shard collusion risks and dynamically adjust node allocation, enhancing throughput while maintaining network security. With a comprehensive trust evaluation mechanism, TBDD discerns node types and performs targeted resharding against potential threats. The model maximizes tolerance for dishonest nodes, optimizes node movement frequency, ensures even node distribution in shards, and balances sharding risks. Rigorous evaluations prove TBDD’s superiority over conventional random-, community-, and trust-based sharding methods in shard risk equilibrium and reducing cross-shard transactions. © 2023, CC BY.

关键词： Reinforcement learning

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Untargeted Adversarial Attack on Knowledge Graph Embeddings

arXiv

引用

arXiv 2024年

作者： Zhao, Tianzhe Chen, Jiaoyan Ru, Yanchi Lin, Qika Geng, Yuxia Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi’an China Department of Computer Science The University of Manchester Manchester United Kingdom National University of Singapore Singapore School of Computer Science Hangzhou Dianzi University Hangzhou China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi’an China

Knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations on low-quality KGs that are prevalent in the real world. Some recent studies propose adversarial attacks to investigate the vulnerabilities of KGE methods, but their attackers are target-oriented with the KGE method and the target triples to predict are given in advance, which lacks practicability. In this work, we explore untargeted attacks with the aim of reducing the global performances of KGE methods over a set of unknown test triples and conducting systematic analyses on KGE robustness. Considering logic rules can effectively summarize the global structure of a KG, we develop rule-based attack strategies to enhance the attack efficiency. In particular, we consider adversarial deletion which learns rules, applying the rules to score triple importance and delete important triples, and adversarial addition which corrupts the learned rules and applies them for negative triples as perturbations. Extensive experiments on two datasets over three representative classes of KGE methods demonstrate the effectiveness of our proposed untargeted attacks in diminishing the link prediction results. And we also find that different KGE methods exhibit different robustness to untargeted attacks. For example, the robustness of methods engaged with graph neural networks and logic rules depends on the density of the graph. But rule-based methods like NCRL are easily affected by adversarial addition attacks to capture negative rules. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph embeddings

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A Real-Time Demonstration of Bidirectional Multi-Format/Rate-Adjustable Integrated Laser Communication/Ranging for Satellite Communication

A Real-Time Demonstration of Bidirectional Multi-Format/Rate...

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Yizhou Wang Yuanxiang Wang Zhen Luo Linsheng Zhong Yang Zou Xueyuan Ao Jiale Liu Feng Fan Xiaoliang Li Xiaoxiao Dai Qi Yang Deming Liu Wuhan National Lab for Optoelectronics (WNLO) & National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Beijing Research Institute of Telemetry Beijing China Jinyinhu Laboratory Wuhan China

A multi-format/rate-adjustable integrated laser communication/ranging system is proposed, achieving adjustable rates between 0.625-10Gbit/s. Real-time experiment shows that at 1e-4 BER the ranging error is only 0.29% ... 详细信息

ISBN: (纸本)9798350369311

关键词： Laser theory Codes Sensitivity Lasers Measurement uncertainty Measurement by laser beam Electro-optic effects Real-time systems Distance measurement Satellite communications

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LF-YOLOv7: Improved YOLOv7 Based on Lightweight Modules and Novel Feature Fusion for Object Detection on Drone-Captured Scenarios

LF-YOLOv7: Improved YOLOv7 Based on Lightweight Modules and ...

引用

International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Wangyu Jiang Le Wang Guojun Mao Meng Sun Fayaz Ali Dharejo Ghulam Ali Mallah Fujian Provincial Key Lab of Big Data Mining&Applications Fujian University of Technology Fuzhou Fujian Province China Department of Electrical Engineering and Computer Science Khalifa Univeristy AbuDhabi UAE Department of Computer Science Shah Abdul Latif University Khairpur Pakistan

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

To address challenges in drone-captured images, including small-sized objects, multiple scales, and object diversity, we propose a novel lightweight and feature fusion based object detection model, called LF-YOLOv7. Firstly, a comprehensive high-resolution refinement design is investigated through simultaneously restructuring the head and optimizing the backbone, achieving more accurate assignment of multiple anchor boxes of different sizes and higher spatial resolution with lightweight mode. Furthermore, we present a new feature fusion module SM-BiFPN, which effectively integrates shallow details through cross-layer connections and enables the model to obtain more feature information related to small objects. Experimental evaluation shows that our method achieves a more competitive results than the benchmark and other compared state-of-the-art models in terms of mAP50 and the number of parameters.

关键词： Cross layer design Head Accuracy Scientific computing Computational modeling Object detection Benchmark testing

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A Hybrid Memory Architecture Supporting Fine-Grained data Migration

SSRN

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SSRN 2022年

作者： Chi, Ye Yue, Jianhui Liao, Xiaofei Liu, Haikun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Computer Science Michigan Technological University HoughtonMI United States

We propose Mocha, a non-hierarchical caching architecture that organizes DRAM and NVM in a flat address space physically, but manages DRAM/NVM in a cache/memory hierarchy in this paper. Since the commercial NVM device-Intel Optane DC Persist Memory Modules (DCPMM) actually access the physical media at a granularity of 256 bytes (an Optane block), we manage the DRAM cache at the 256-byte size to adapt to this device feature of Optane. This design not only enables fine-grained data migration and management for the DRAM cache, but also avoids write amplification for Optane DCPMM. We also create a the \textit{ Indirect Address Cache} (IAC) in \textit{Hybrid Memory Controller}(HMC) and propose a reverse address mapping table in the DRAM to speed up address translation and cache replacement. Besides, we exploit an utility-based caching mechanism to filter cold blocks in the NVM, and further improve DRAM cache effectiveness. We implement Mocha in an architecture simulator. Experimental results indicate that MOCHA can improve performance of applications by 8.2\% on average (up to 24.6\%), provide 6.9\% reduction in average energy consumption and 25.9\% reduction in average data migration traffic, compared to the most advanced hybrid memory architecture-HSCC. © 2022, The Authors. All rights reserved.

关键词： Dynamic random access storage

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GraphMI: Extracting private graph data from graph neural networks

arXiv

引用

arXiv 2021年

作者： Zhang, Zaixi Liu, Qi Huang, Zhenya Wang, Hao Lu, Chengqiang Liu, Chuanren Chen, Enhong Anhui Province Key Lab. of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Alibaba Group University of Tennessee Knoxville

As machine learning becomes more widely used for critical applications, the need to study its implications in privacy turns to be urgent. Given access to the target model and auxiliary information, model inversion attack aims to infer sensitive features of the training dataset, which leads to great privacy concerns. Despite its success in grid-like domains, directly applying model inversion techniques on non-grid domains such as graph achieves poor attack performance due to the difficulty to fully exploit the intrinsic properties of graphs and attributes of nodes used in Graph Neural Networks (GNN). To bridge this gap, we present Graph Model Inversion attack (GraphMI), which aims to extract private graph data of the training graph by inverting GNN, one of the state-of-the-art graph analysis tools. Specifically, we firstly propose a projected gradient module to tackle the discreteness of graph edges while preserving sparsity and smoothness of graph features. Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference. With the proposed methods, we study the connection between model inversion risk and edge influence and show that edges with greater influence are more likely to be recovered. Extensive experiments over several public datasets demonstrate the effectiveness of our method. We also show that differential privacy in its canonical form can hardly defend our attack while preserving decent utility. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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ProtGNN: Towards self-explaining Graph Neural Networks

arXiv

引用

arXiv 2021年

作者： Zhang, Zaixi Liu, Qi Wang, Hao Lu, Chengqiang Lee, Cheekong Anhui Province Key Lab. of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China Tencent America United States

Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to explain the predictions made by GNNs. Existing explanation methods mainly focus on post-hoc explanations where another explanatory model is employed to provide explanations for a trained GNN. The fact that post-hoc methods fail to reveal the original reasoning process of GNNs raises the need of building GNNs with built-in interpretability. In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs. In ProtGNN, the explanations are naturally derived from the case-based reasoning process and are actually used during classification. The prediction of ProtGNN is obtained by comparing the inputs to a few learned prototypes in the latent space. Furthermore, for better interpretability and higher efficiency, a novel conditional subgraph sampling module is incorporated to indicate which part of the input graph is most similar to each prototype in ProtGNN+. Finally, we evaluate our method on a wide range of datasets and perform concrete case studies. Extensive results show that ProtGNN and ProtGNN+ can provide inherent interpretability while achieving accuracy on par with the non-interpretable counterparts. © 2021, CC BY.

关键词： Graph neural networks

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