IEEE Transactions on Information Forensics and Security最新文献_第9页

Online Writer Retrieval With Chinese Handwritten Phrases: A Synergistic Temporal-Frequency Representation Learning Approach 中文手写词组的在线作家检索：时频协同表征学习法

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-07 DOI: 10.1109/TIFS.2024.3493594

Peirong Zhang;Lianwen Jin

{"title":"Online Writer Retrieval With Chinese Handwritten Phrases: A Synergistic Temporal-Frequency Representation Learning Approach","authors":"Peirong Zhang;Lianwen Jin","doi":"10.1109/TIFS.2024.3493594","DOIUrl":"10.1109/TIFS.2024.3493594","url":null,"abstract":"Currently, the prevalence of online handwriting has spurred a critical need for effective retrieval systems to accurately search relevant handwriting instances from specific writers, known as online writer retrieval. Despite the growing demand, this field suffers from a scarcity of well-established methodologies and public large-scale datasets. This paper tackles these challenges with a focus on Chinese handwritten phrases. First, we propose DOLPHIN, a novel retrieval model designed to enhance handwriting representations through synergistic temporal-frequency analysis. For frequency feature learning, we propose the HFGA block, which performs gated cross-attention between the vanilla temporal handwriting sequence and its high-frequency sub-bands to amplify salient writing details. For temporal feature learning, we propose the CAIR block, tailored to promote channel interaction and reduce channel redundancy. Second, to address data deficit, we introduce OLIWER, a large-scale online writer retrieval dataset encompassing over 670,000 Chinese handwritten phrases from 1,731 individuals. Through extensive evaluations, we demonstrate the superior performance of DOLPHIN over existing methods. In addition, we explore cross-domain writer retrieval and reveal the pivotal role of increasing feature alignment in bridging the distributional gap between different handwriting data. Our findings emphasize the significance of point sampling frequency and pressure features in improving handwriting representation quality and retrieval performance. Code and dataset are available at \u0000<uri>https:// github.com/SCUT-DLVCLab/DOLPHIN</uri>\u0000.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"10387-10399"},"PeriodicalIF":6.3,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142597477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DEFending Integrated Circuit Layouts DEFending 集成电路布局

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-06 DOI: 10.1109/tifs.2024.3492810

Jitendra Bhandari, Jayanth Gopinath, Mohammed Ashraf, Johann Knechtel, Ozgur Sinanoglu, Ramesh Karri

引用次数: 0

PointNCBW: Towards Dataset Ownership Verification for Point Clouds via Negative Clean-label Backdoor Watermark PointNCBW：通过负清洁标签后门水印实现点云数据集所有权验证

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-06 DOI: 10.1109/tifs.2024.3492792

Cheng Wei, Yang Wang, Kuofeng Gao, Shuo Shao, Yiming Li, Zhibo Wang, Zhan Qin

引用次数: 0

Communication Efficient Ciphertext-Field Aggregation in Wireless Networks via Over-the-Air Computation 通过空中计算实现无线网络中的通信高效密文-字段聚合

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-05 DOI: 10.1109/tifs.2024.3490400

Xin Xie, Jianan Hong, Cunqinq Hua, Yanhong Xu

引用次数: 0

Evaluating Security and Robustness for Split Federated Learning against Poisoning Attacks 评估针对中毒攻击的拆分联合学习的安全性和鲁棒性

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-04 DOI: 10.1109/tifs.2024.3490861

Xiaodong Wu, Henry Yuan, Xiangman Li, Jianbing Ni, Rongxing Lu

引用次数: 0

SemantiChain: A Trust Retrieval Blockchain Based on Semantic Sharding SemantiChain：基于语义分片的信任检索区块链

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-04 DOI: 10.1109/TIFS.2024.3488501

Zihang Zhen;Xiaoding Wang;Xu Yang;Jiwu Shu;Jia Hu;Hui Lin;Xun Yi

{"title":"SemantiChain: A Trust Retrieval Blockchain Based on Semantic Sharding","authors":"Zihang Zhen;Xiaoding Wang;Xu Yang;Jiwu Shu;Jia Hu;Hui Lin;Xun Yi","doi":"10.1109/TIFS.2024.3488501","DOIUrl":"10.1109/TIFS.2024.3488501","url":null,"abstract":"Since its inception, blockchain technology has found wide-ranging applications in various fields including agriculture, energy, and so on, owing to its immutable and decentralized nature. However, existing blockchains encounter significant challenges in scenarios that demand efficient retrieval of big data. This is primarily because current blockchains cannot directly store and process diverse types of rich media information. Additionally, the semantic relationships between data within the blockchains are weak, complicating the categorization and retrieval of data and transactions. Moreover, the scalability of current blockchains is limited, with the capacity of full nodes continually increasing. Although some semantic-based blockchain solutions that combine off-chain scalability have been proposed, they are limited in effectiveness and applications. To address these issues, this paper introduces a brand-new blockchain sharding technique called Semantic Sharding, which enhances blockchain scalability through a hybrid on/off-chain approach. Building on this, we propose a semantic sharding blockchain architecture, SemantiChain, which enables the on-chain storage and retrieval of transaction semantic features. Furthermore, through the Po2RW consensus protocol, we balance the scalability and security of SemantiChain. Security analysis proves that SemantiChain can resist security risks such as man-in-the-middle attacks, malicious node attacks and on/off-chain data inconsistency. Experimental results demonstrate that SemantiChain can reduce search time and memory usage by at least 32.29% and 77.97% respectively under the same retrieval performance, compared to mainstream approximate nearest neighbour retrieval algorithms. Furthermore, compared to the SOTA semantic blockchain, SemantiChain achieves a retrieval performance improvement of at least 45.88% and reduces retrieval memory usage by 95.76%.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"10339-10354"},"PeriodicalIF":6.3,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Homomorphic Matrix Operations under Bicyclic Encoding 双环编码下的同态矩阵运算

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-04 DOI: 10.1109/tifs.2024.3490862

Jingwei Chen, Linhan Yang, Wenyuan Wu, Yang Liu, Yong Feng

引用次数: 0

LD-PA: Distilling Univariate Leakage for Deep Learning-based Profiling Attacks LD-PA：为基于深度学习的剖析攻击提取单变量泄密信息

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-04 DOI: 10.1109/tifs.2024.3490782

Chong Xiao, Ming Tang, Sengim Karayalcin, Wei Cheng

引用次数: 0

IEEE Transactions on Information Forensics and Security publication information IEEE Transactions on Information Forensics and Security 出版信息

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-01 DOI: 10.1109/TIFS.2024.3444409

引用次数: 0

ASRL: Adaptive Swarm Reinforcement Learning for Enhanced OSN Intrusion Detection ASRL：用于增强型 OSN 入侵检测的自适应蜂群强化学习

IF 6.3 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2024-11-01 DOI: 10.1109/TIFS.2024.3488506

Edward Kwadwo Boahen;Rexford Nii Ayitey Sosu;Selasi Kwame Ocansey;Qinbao Xu;Changda Wang

{"title":"ASRL: Adaptive Swarm Reinforcement Learning for Enhanced OSN Intrusion Detection","authors":"Edward Kwadwo Boahen;Rexford Nii Ayitey Sosu;Selasi Kwame Ocansey;Qinbao Xu;Changda Wang","doi":"10.1109/TIFS.2024.3488506","DOIUrl":"10.1109/TIFS.2024.3488506","url":null,"abstract":"Online Social Networks (OSNs) face escalating security threats that imperil user privacy. Conventional Deep Learning methods, relying predominantly on fixed learning rates, encounter limitations when capturing the nuanced intricacies of OSN traffic that arise from shifting user behaviors, diverse content types, and evolving interaction patterns because of social trending topics changes. To tackle these challenges, our paper delves into the diverse variations and transitions from a uniform approach, where a single method is employed for various types of data, to a multi-variation methodology. This methodology dynamically adapts to the special characteristics of each data type, resulting in more effective data representation while alleviating the limitations associated with fixed-rate calibration. Therefore, we devise the Adaptive Swarm Reinforcement Learning (ASRL) method that leverages adaptive learning to intricately analyze a wide range of user interactions, endowing our proposed method with the capacity to flexibly adjust to the constantly shifting OSN patterns. The experiments show that the proposed ASRL method achieves an accuracy of 98.59% in detecting a range of threat patterns, surpassing other prevalent methods by an average of 5% across the datasets from Facebook, Google+, and Twitter. Meanwhile, ASRL logs suspicious activities to identify the intruder for forensic analysis. The implementation of our proposed method is now publicly accessible at \u0000<uri>https://github.com/don2c/asrl_Project</uri>\u0000.","PeriodicalId":13492,"journal":{"name":"IEEE Transactions on Information Forensics and Security","volume":"19 ","pages":"10258-10272"},"PeriodicalIF":6.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0