ACM Computing Surveys最新文献_第8页

Advancements in AI-Generated Content Forensics: A Systematic Literature Review 人工智能生成内容取证的进展：系统的文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-13 DOI: 10.1145/3760526

Qiang Xu, Wenpeng Mu, Jianing Li, Tanfeng Sun, Xinghao Jiang

{"title":"Advancements in AI-Generated Content Forensics: A Systematic Literature Review","authors":"Qiang Xu, Wenpeng Mu, Jianing Li, Tanfeng Sun, Xinghao Jiang","doi":"10.1145/3760526","DOIUrl":"https://doi.org/10.1145/3760526","url":null,"abstract":"The rapid proliferation of AI-Generated Content (AIGC), spanning text, images, video, and audio, has created a dual-edged sword of unprecedented creativity and significant societal risks, including misinformation and disinformation. This survey provides a comprehensive and structured overview of the current landscape of AIGC detection technologies. We begin by chronicling the evolution of generative models, from foundational GANs to state-of-the-art diffusion and transformer-based architectures. We then systematically review detection methodologies across all modalities, organizing them into a novel taxonomy of External Detection and Internal Detection. For each modality, we trace the technical progression from early feature-based methods to advanced deep learning, while also covering critical tasks like model attribution and tampered region localization. Furthermore, we survey the ecosystem of publicly available detection tools and practical applications. Finally, we distill the primary challenges facing the field—including generalization, robustness, interpretability, and the lack of universal benchmarks—and conclude by outlining key future directions, such as the development of holistic AI Safety Agents, dynamic evaluation standards, and AI-driven governance frameworks. This survey aims to provide researchers and practitioners with a clear, in-depth understanding of the state of the art and critical frontiers in the ongoing endeavor to ensure a safe and trustworthy AIGC ecosystem.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"14 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850855","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

Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances 分布外检测：以任务为导向的最新研究进展

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-11 DOI: 10.1145/3760390

Shuo Lu, Yingsheng Wang, Lijun Sheng, Lingxiao He, Aihua Zheng, Jian Liang

{"title":"Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advances","authors":"Shuo Lu, Yingsheng Wang, Lijun Sheng, Lingxiao He, Aihua Zheng, Jian Liang","doi":"10.1145/3760390","DOIUrl":"https://doi.org/10.1145/3760390","url":null,"abstract":"Out-of-distribution (OOD) detection aims to detect test samples outside the training category space, which is an essential component in building reliable machine learning systems. Existing reviews on OOD detection primarily focus on method taxonomy, surveying the field by categorizing various approaches. However, many recent works concentrate on non-traditional OOD detection scenarios, such as test-time adaptation, multi-modal data sources and other novel contexts. In this survey, we uniquely review recent advances in OOD detection from the task-oriented perspective for the first time. According to the user’s access to the model, that is, whether the OOD detection method is allowed to modify or retrain the model, we classify the methods as training-driven or training-agnostic. Besides, considering the rapid development of pre-trained models, large pre-trained model-based OOD detection is also regarded as an important category and discussed separately. Furthermore, we provide a discussion of the evaluation scenarios, a variety of applications, and several future research directions. We believe this survey with new taxonomy will benefit the proposal of new methods and the expansion of more practical scenarios. A curated list of related papers is provided in the Github repository: https://github.com/shuolucs/Awesome-Out-Of-Distribution-Detection","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"31 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850815","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

Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art 变压器在小目标检测中的应用：一个基准和最新的调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-11 DOI: 10.1145/3758090

Aref Miri Rekavandi, Shima Rashidi, Farid Boussaid, Stephen Hoefs, Emre Akbas, Mohammed Bennamoun

{"title":"Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art","authors":"Aref Miri Rekavandi, Shima Rashidi, Farid Boussaid, Stephen Hoefs, Emre Akbas, Mohammed Bennamoun","doi":"10.1145/3758090","DOIUrl":"https://doi.org/10.1145/3758090","url":null,"abstract":"Transformers have rapidly gained popularity in computer vision, especially in the field of object detection. Upon examining the outcomes of state-of-the-art object detection methods, we noticed that transformers consistently outperformed well-established CNN-based detectors in almost every video or image dataset. Small objects have been identified as one of the most challenging object types in detection frameworks due to their low visibility. This paper aims to explore the performance benefits offered by such extensive networks and identify potential reasons for their Small Object Detection (SOD) superiority. We aim to investigate potential strategies that could further enhance transformers’ performance in SOD. This survey presents a taxonomy of over 60 research studies on developed transformers for the task of SOD, spanning the years 2020 to 2023. These studies encompass a variety of detection applications, including small object detection in generic images, aerial images, medical images, active millimeter images, underwater images, and videos. We also compile and present a list of 12 large-scale datasets suitable for SOD that were overlooked in previous studies and compare the performance of the reviewed studies using popular metrics such as mean Average Precision (mAP), Frames Per Second (FPS) and number of parameters. Researchers can keep track of newer studies on our web page, which is available at: https://github.com/arekavandi/Transformer-SOD.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"39 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850844","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

Underwater Optical Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future 人工智能时代的水下光学目标检测：现状、挑战与未来

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-07 DOI: 10.1145/3759243

Long Chen, Yuzhi Huang, Junyu Dong, Qi Xu, Sam Kwong, Huimin Lu, Huchuan Lu, Chongyi Li

{"title":"Underwater Optical Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future","authors":"Long Chen, Yuzhi Huang, Junyu Dong, Qi Xu, Sam Kwong, Huimin Lu, Huchuan Lu, Chongyi Li","doi":"10.1145/3759243","DOIUrl":"https://doi.org/10.1145/3759243","url":null,"abstract":"Underwater optical object detection (UOD), aiming to identify and localise objects in underwater optical images or videos, presents significant challenges due to the optical distortion, water turbidity, and changing illumination in underwater scenes. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD. To further facilitate future advancements, we comprehensively study AI-based UOD. In this survey, we first categorise existing algorithms into traditional machine learning-based methods and deep learning-based methods, and summarise them by considering learning strategies, experimental datasets, learning stages, employed features or techniques, and underlying frameworks. Next, we discuss the potential challenges and suggest possible solutions and new directions. We also perform both quantitative and qualitative evaluations of mainstream algorithms across multiple benchmark datasets, taking into account the diversity and biases in experimental setups. Finally, we introduce two off-the-shelf detection analysis tools, Diagnosis and TIDE, which will examine the effects of object characteristics and various types of errors on detector performance. These tools help identify the strengths and weaknesses of different detectors, providing insights for further improvement. The source code, trained models, utilized datasets, detection results, and detection analysis tools are publicly available at https://github.com/LongChenCV/UODReview and will be regularly updated.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"37 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144792851","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

Load Balancing in the Internet of Vehicles: A Comprehensive Review of SDN and Machine Learning Approaches 车联网中的负载平衡：SDN和机器学习方法的综合综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-07 DOI: 10.1145/3759242

Phibadeity S Marwein, Debdatta Kandar

引用次数: 0

Blockchain Agnostic Protocols: An Analysis of the State of the Art 区块链不可知论协议：对最新技术的分析

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-05 DOI: 10.1145/3758089

Alessandro Bigiotti, Leonardo Mostarda, Alfredo Navarra, Andrea Pinna, Davide Sestili, Roberto Tonelli, Matteo Vaccargiu

引用次数: 0

A Survey on Neural Radiance Fields 神经辐射场研究进展

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-02 DOI: 10.1145/3758085

Yun Liao, Yide Di, Hao Zhou, Kaijun Zhu, Mingyu Lu, Qing Duan, Junhui Liu

{"title":"A Survey on Neural Radiance Fields","authors":"Yun Liao, Yide Di, Hao Zhou, Kaijun Zhu, Mingyu Lu, Qing Duan, Junhui Liu","doi":"10.1145/3758085","DOIUrl":"https://doi.org/10.1145/3758085","url":null,"abstract":"View synthesis is a fundamental task in computer vision, known for its significantly higher complexity compared to conventional vision problems. The introduction of Neural Radiance Fields (NeRF) marked a major breakthrough in this field, substantially improving previous methods and pushing view synthesis to unprecedented levels. This survey aims to systematically review the progress of NeRF-based models in computer vision. We begin by explaining the core principles underlying the success of NeRF. Then, we delve into and analyze seven representative NeRF-based representation forms, including Implicit Representation, Neural Point Cloud, and others. Next, we provide a comprehensive comparison and analysis of 14 major research directions that enhance NeRF, such as Modeling Different Practical Capturing Scenarios, Generalization in Modeling, and Modeling Dynamic Scenes. In addition, we conduct both qualitative and quantitative evaluations of numerous NeRF-based methods on multiple datasets, comparing training time, rendering speed, and memory requirements. Finally, we discuss potential future research directions and challenges in this field. We hope that this work will inspire further interest and contribute to advancing the application and development of NeRF in computer vision.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"26 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763358","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

Analyzing and Categorizing Emerging Cybersecurity Regulations 分析和分类新兴网络安全法规

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-02 DOI: 10.1145/3757318

Angelica Marotta, Stuart Madnick

引用次数: 0

Secure Integration of Cyber Engineering and Medical Cyber-Physical System: A Survey and Open Issues 网络工程与医疗信息物理系统的安全集成：综述与有待解决的问题

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-02 DOI: 10.1145/3757731

Abhishek Kumar Pandey, Sherali Zeadally, Ashok Kumar Das, Neeraj Kumar

{"title":"Secure Integration of Cyber Engineering and Medical Cyber-Physical System: A Survey and Open Issues","authors":"Abhishek Kumar Pandey, Sherali Zeadally, Ashok Kumar Das, Neeraj Kumar","doi":"10.1145/3757731","DOIUrl":"https://doi.org/10.1145/3757731","url":null,"abstract":"Medical Cyber-Physical Systems (MCPS) are the prerequisites for a highly digital environment in medical facilities. All the technologies, methods, and problems that arise in developing MCPS should be mitigated systematically and continuously to meet this need. This study analytically discusses the currently available MCPS models, threat scenarios, attacks, and data breach statistics to evaluate the current security situation of the research community in the MCPS domain. The large and complex components of MCPS comprise many computational and physical aspects, making managing secure reliability challenging. To address this challenge, there is a need for security by design that incorporates built-in security functionality in the development of an MCPS. Our comprehensive review critically analyzes the current state-of-the-art in MCPS by considering various aspects, such as functionality, communication media, design characteristics, and security. Furthermore, we explore the security threats posed by MCPS as well as their potential impact. Finally, we propose a potential future pathway to effectively mitigate the identified threats by providing a promising direction for further investigation.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"30 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763361","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

Build Optimization: A Systematic Literature Review 建筑优化：系统文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-08-02 DOI: 10.1145/3757912

Henri Aïdasso, Mohammed Sayagh, Francis Bordeleau

{"title":"Build Optimization: A Systematic Literature Review","authors":"Henri Aïdasso, Mohammed Sayagh, Francis Bordeleau","doi":"10.1145/3757912","DOIUrl":"https://doi.org/10.1145/3757912","url":null,"abstract":"In modern software organizations, Continuous Integration (CI) consists of an automated build process triggered by change submissions and involving compilation, testing, and packaging to enable the continuous deployment of new software versions to end-users. While CI offers various advantages regarding software quality and delivery speed, it introduces challenges addressed by a large body of research. To better understand this literature, so as to help practitioners find solutions for their problems and guide future research, we conduct a systematic review of 97 studies published between 2006 and 2024, summarizing their goals, methodologies, datasets, and metrics. These studies target two main challenges: (1) long build durations and (2) build failures. To address the first, researchers have proposed techniques such as predicting build outcomes and durations, selective build execution, and build acceleration through caching or performance smell repair. On the other hand, build failure root causes have been studied, leading to techniques for predicting build script maintenance needs and automating repairs. Recent work also focuses on flaky build failures caused by environmental issues. Most techniques use machine learning and rely on build metrics, which we classify into five categories. Finally, we identify eight publicly available datasets to support future research on build optimization.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"15 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763360","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