IEEE Open Journal of the Computer Society最新文献_第10页

A Survey on ChatGPT: AI–Generated Contents, Challenges, and Solutions ChatGPT调查：人工智能生成的内容、挑战和解决方案

IEEE Open Journal of the Computer Society Pub Date : 2023-08-16 DOI: 10.1109/OJCS.2023.3300321

Yuntao Wang;Yanghe Pan;Miao Yan;Zhou Su;Tom H. Luan

{"title":"A Survey on ChatGPT: AI–Generated Contents, Challenges, and Solutions","authors":"Yuntao Wang;Yanghe Pan;Miao Yan;Zhou Su;Tom H. Luan","doi":"10.1109/OJCS.2023.3300321","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3300321","url":null,"abstract":"With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative large AI algorithms to assist or replace humans in creating massive, high-quality, and human-like content at a faster pace and lower cost, based on user-provided prompts. Despite the recent significant progress in AIGC, security, privacy, ethical, and legal challenges still need to be addressed. This paper presents an in-depth survey of working principles, security and privacy threats, state-of-the-art solutions, and future challenges of the AIGC paradigm. Specifically, we first explore the enabling technologies, general architecture of AIGC, and discuss its working modes and key characteristics. Then, we investigate the taxonomy of security and privacy threats to AIGC and highlight the ethical and societal implications of GPT and AIGC technologies. Furthermore, we review the state-of-the-art AIGC watermarking approaches for regulatable AIGC paradigms regarding the AIGC model and its produced content. Finally, we identify future challenges and open research directions related to AIGC.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"280-302"},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/10016900/10221755.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67880863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Twitter Bot Detection Using Neural Networks and Linguistic Embeddings 使用神经网络和语言嵌入的Twitter Bot检测

IEEE Open Journal of the Computer Society Pub Date : 2023-08-07 DOI: 10.1109/OJCS.2023.3302286

Feng Wei;Uyen Trang Nguyen

{"title":"Twitter Bot Detection Using Neural Networks and Linguistic Embeddings","authors":"Feng Wei;Uyen Trang Nguyen","doi":"10.1109/OJCS.2023.3302286","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3302286","url":null,"abstract":"Twitter is a web application playing the dual role of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. In this article, we propose a Twitter bot detection model using recurrent neural networks, specifically bidirectional lightweight gated recurrent unit (BiLGRU), and linguistic embeddings. To the best of our knowledge, our Twitter bot detection model is the first that does not require any handcrafted features, or prior knowledge or assumptions about account profiles, friendship networks or historical behavior. The proposed model uses only textual content of tweets and linguistic embeddings to classify bot and human accounts on Twitter. Experimental results show that the proposed model performs better or comparably to state-of-the-art Twitter bot detection models while requiring no feature engineering, making it faster and easier to train and deploy in a real network. We also present experimental results that show the performance and computational costs of different types of linguistic embeddings and recurrence network variants for the task of Twitter bot detection. The results will potentially help researchers design high-performance deep-learning models for similar tasks.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"218-230"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/10016900/10210119.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67880872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Practical Anti-Fuzzing Techniques With Performance Optimization 具有性能优化的实用反引信技术

IEEE Open Journal of the Computer Society Pub Date : 2023-08-04 DOI: 10.1109/OJCS.2023.3301883

Zhengxiang Zhou;Cong Wang

{"title":"Practical Anti-Fuzzing Techniques With Performance Optimization","authors":"Zhengxiang Zhou;Cong Wang","doi":"10.1109/OJCS.2023.3301883","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3301883","url":null,"abstract":"Fuzzing, an automated software testing technique, has achieved remarkable success in recent years, aiding developers in identifying vulnerabilities. However, fuzzing can also be exploited by attackers to discover zero-day vulnerabilities. To counter this threat, researchers have proposed anti-fuzzing techniques, which aim to impede the fuzzing process by slowing the program down, providing misleading coverage feedback, and complicating data flow, etc. Unfortunately, current anti-fuzzing approaches primarily focus on enhancing defensive capabilities while underestimating the associated overhead and manual efforts required. In our paper, we present No-Fuzz, an efficient and practical anti-fuzzing technique. No-Fuzz stands out in binary-only fuzzing by accurately determining running environments, effectively reducing unnecessary fake block overhead, and replacing resource-intensive functions with lightweight arithmetic operations in anti-hybrid techniques. We have implemented a prototype of No-Fuzz and conducted evaluations to compare its performance against existing approaches. Our evaluations demonstrate that No-Fuzz introduces minimal performance overhead, accounting for less than 10% of the storage cost for a single fake block. Moreover, it achieves a significant 92.2% reduction in total storage costs compared to prior works for an equivalent number of branch reductions. By emphasizing practicality, our study sheds light on improving anti-fuzzing techniques for real-world deployment.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"206-217"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/10016900/10209185.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67880871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reverse Self-Distillation Overcoming the Self-Distillation Barrier 反自蒸馏克服自蒸馏障碍

IEEE Open Journal of the Computer Society Pub Date : 2023-06-21 DOI: 10.1109/OJCS.2023.3288227

Shuiping Ni;Xinliang Ma;Mingfu Zhu;Xingwang Li;Yu-Dong Zhang

引用次数: 0

IEEE Computer Society Information IEEE计算机学会信息

IEEE Open Journal of the Computer Society Pub Date : 2023-06-08 DOI: 10.1109/OJCS.2023.3243827

引用次数: 0

Disjunctive Threshold Networks for Tabular Data Classification 用于表格数据分类的虚拟阈值网络

IEEE Open Journal of the Computer Society Pub Date : 2023-06-05 DOI: 10.1109/OJCS.2023.3282948

Weijia Wang;Litao Qiao;Bill Lin

{"title":"Disjunctive Threshold Networks for Tabular Data Classification","authors":"Weijia Wang;Litao Qiao;Bill Lin","doi":"10.1109/OJCS.2023.3282948","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3282948","url":null,"abstract":"While neural networks have been achieving increasingly significant excitement in solving classification tasks such as natural language processing, their lack of interpretability becomes a great challenge for neural networks to be deployed in certain high-stakes human-centered applications. To address this issue, we propose a new approach for generating interpretable predictions by inferring a simple three-layer neural network with threshold activations, so that it can benefit from effective neural network training algorithms and at the same time, produce human-understandable explanations for the results. In particular, the hidden layer neurons in the proposed model are trained with floating point weights and binary output activations. The output neuron is also trainable as a threshold logic function that implements a disjunctive operation, forming the logical-OR of the first-level threshold logic functions. This neural network can be trained using state-of-the-art training methods to achieve high prediction accuracy. An important feature of the proposed architecture is that only a simple greedy algorithm is required to provide an explanation with the prediction that is human-understandable. In comparison with other explainable decision models, our proposed approach achieves more accurate predictions on a broad set of tabular data classification datasets.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"185-194"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/10016900/10144404.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67880869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Backdoor Attacks to Deep Learning Models and Countermeasures: A Survey 深度学习模型的后门攻击及其对策研究

IEEE Open Journal of the Computer Society Pub Date : 2023-04-14 DOI: 10.1109/OJCS.2023.3267221

Yudong Li;Shigeng Zhang;Weiping Wang;Hong Song

{"title":"Backdoor Attacks to Deep Learning Models and Countermeasures: A Survey","authors":"Yudong Li;Shigeng Zhang;Weiping Wang;Hong Song","doi":"10.1109/OJCS.2023.3267221","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3267221","url":null,"abstract":"Backdoor attacks have severely threatened deep neural network (DNN) models in the past several years. In backdoor attacks, the attackers try to plant hidden backdoors into DNN models, either in the training or inference stage, to mislead the output of the model when the input contains some specified triggers without affecting the prediction of normal inputs not containing the triggers. As a rapidly developing topic, numerous works on designing various backdoor attacks and developing techniques to defend against such attacks have been proposed in recent years. However, a comprehensive and holistic overview of backdoor attacks and countermeasures is still missing. In this paper, we provide a systematic overview of the design of backdoor attacks and the defense strategies to defend against backdoor attacks, covering the latest published works. We review representative backdoor attacks and defense strategies in both the computer vision domain and other domains, discuss their pros and cons, and make comparisons among them. We outline key challenges to be addressed and potential research directions in the future.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"134-146"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/10016900/10102775.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67881014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

FLIS: Clustered Federated Learning Via Inference Similarity for Non-IID Data Distribution FLIS：基于推理相似性的非IID数据分布集群联合学习

IEEE Open Journal of the Computer Society Pub Date : 2023-03-27 DOI: 10.1109/OJCS.2023.3262203

Mahdi Morafah;Saeed Vahidian;Weijia Wang;Bill Lin

{"title":"FLIS: Clustered Federated Learning Via Inference Similarity for Non-IID Data Distribution","authors":"Mahdi Morafah;Saeed Vahidian;Weijia Wang;Bill Lin","doi":"10.1109/OJCS.2023.3262203","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3262203","url":null,"abstract":"Conventional federated learning (FL) approaches are ineffective in scenarios where clients have significant differences in the distributions of their local data. The Non-IID data distribution in the client data causes a drift in the local model updates from the global optima, which significantly impacts the performance of the trained models. In this article, we present a new algorithm called FLIS that aims to address this problem by grouping clients into clusters that have jointly trainable data distributions. This is achieved by comparing the \u0000<italic>inference similarity</i>\u0000 of client models. Our proposed framework captures settings where different groups of users may have their own objectives (learning tasks), but by aggregating their data with others in the same cluster (same learning task), superior models can be derived via more efficient and personalized federated learning. We present experimental results to demonstrate the benefits of FLIS over the state-of-the-art approaches on the CIFAR-100/10, SVHN, and FMNIST datasets. Our code is available at \u0000<uri>https://github.com/MMorafah/FLIS</uri>\u0000.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"109-120"},"PeriodicalIF":0.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/10016900/10081485.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67881012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Promoting the Sustainability of Blockchain in Web 3.0 and the Metaverse Through Diversified Incentive Mechanism Design 通过多样化的激励机制设计促进区块链在Web 3.0和元宇宙中的可持续性

IEEE Open Journal of the Computer Society Pub Date : 2023-03-23 DOI: 10.1109/OJCS.2023.3260829

Daniel Mawunyo Doe;Jing Li;Niyato Dusit;Zhen Gao;Jun Li;Zhu Han

{"title":"Promoting the Sustainability of Blockchain in Web 3.0 and the Metaverse Through Diversified Incentive Mechanism Design","authors":"Daniel Mawunyo Doe;Jing Li;Niyato Dusit;Zhen Gao;Jun Li;Zhu Han","doi":"10.1109/OJCS.2023.3260829","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3260829","url":null,"abstract":"This article explores the role of blockchains in the development of Web 3.0 and the Metaverse. The success of these technologies is dependent on the utilization of decentralized systems like blockchains, which can store and validate data on identities and reputations and facilitate the exchange of virtual assets. Full nodes, which store the entire blockchain state and validate all transactions, are essential for the decentralization and reliability of the network. However, operating a full node is resource-intensive and can be expensive. To tackle this challenge, we propose an incentive mechanism that utilizes contract-theoretic methods to economically motivate users to support the sustainability and growth of the blockchain network. Our contract design addresses the problem of information asymmetry (e.g., users' revenue-generating capabilities and efforts) between users and the blockchain network. Additionally, we recommend providing diverse incentives based on the user's revenue-generating capabilities and efforts to assist the blockchain network in funding incentives. Our experimental results demonstrate that our proposed mechanism increases the blockchain network's utility by \u0000<inline-formula><tex-math>$48.48%-54.52%$</tex-math></inline-formula>\u0000 and reduces the users' cost by \u0000<inline-formula><tex-math>$38.46%-62.5%$</tex-math></inline-formula>\u0000 compared with the state-of-the-art implementations such as Celo, Vipnode, and Pocket Network.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"171-184"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/10016900/10078899.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67880867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Blockchain-Aided Secure Semantic Communication for AI-Generated Content in Metaverse Metaverse中人工智能生成内容的区块链辅助安全语义通信

IEEE Open Journal of the Computer Society Pub Date : 2023-03-23 DOI: 10.1109/OJCS.2023.3260732

Yijing Lin;Hongyang Du;Dusit Niyato;Jiangtian Nie;Jiayi Zhang;Yanyu Cheng;Zhaohui Yang

{"title":"Blockchain-Aided Secure Semantic Communication for AI-Generated Content in Metaverse","authors":"Yijing Lin;Hongyang Du;Dusit Niyato;Jiangtian Nie;Jiayi Zhang;Yanyu Cheng;Zhaohui Yang","doi":"10.1109/OJCS.2023.3260732","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3260732","url":null,"abstract":"The construction of virtual transportation networks requires massive data to be transmitted from edge devices to Virtual Service Providers (VSP) to facilitate circulations between the physical and virtual domains in Metaverse. Leveraging semantic communication for reducing information redundancy, VSPs can receive semantic data from edge devices to provide varied services through advanced techniques, e.g., AI-Generated Content (AIGC), for users to explore digital worlds. But the use of semantic communication raises a security issue because attackers could send malicious semantic data with similar semantic information but different desired content to break Metaverse services and cause wrong output of AIGC. Therefore, in this paper, we first propose a blockchain-aided semantic communication framework for AIGC services in virtual transportation networks to facilitate interactions of the physical and virtual domains among VSPs and edge devices. We illustrate a training-based targeted semantic attack scheme to generate adversarial semantic data by various loss functions. We also design a semantic defense scheme that uses the blockchain and zero-knowledge proofs to tell the difference between the semantic similarities of adversarial and authentic semantic data and to check the authenticity of semantic data transformations. Simulation results show that the proposed defense method can reduce the semantic similarity of the adversarial semantic data and the authentic ones by up to 30% compared with the attack scheme.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"72-83"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8782664/10016900/10079087.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67881009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16