ACM Computing Surveys最新文献_第2页

A Comprehensive Survey of Transformers in Text Recognition: Techniques, Challenges, and Future Directions 文本识别中变形的综合研究：技术、挑战和未来方向

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-09 DOI: 10.1145/3771273

Ali Afkari-Fahandari, Elham Shabaninia, Fatemeh Asadi-Zeydabadi, Hossein Nezamabadi-Pour

{"title":"A Comprehensive Survey of Transformers in Text Recognition: Techniques, Challenges, and Future Directions","authors":"Ali Afkari-Fahandari, Elham Shabaninia, Fatemeh Asadi-Zeydabadi, Hossein Nezamabadi-Pour","doi":"10.1145/3771273","DOIUrl":"https://doi.org/10.1145/3771273","url":null,"abstract":"Optical character recognition is a rapidly evolving field within pattern recognition, enabling the automatic conversion of printed or handwritten text images into machine-readable formats. This technology plays a critical role across various sectors, including banking, healthcare, government, and education. While Optical character recognition systems encompass multiple stages such as text detection, segmentation, and post-processing, this paper focuses on text recognition as a core and technically challenging component. In particular, we provide an in-depth review of recent advances driven by Transformer-based models, which have significantly pushed the state-of-the-art. To contextualize these advancements, a detailed comparative analysis of Transformer-based techniques is presented against earlier deep learning approaches, highlighting their respective limitations and the improvements introduced by Transformers, including parallel sequence processing, global context modeling, better handling of long-range dependencies, and enhanced robustness to irregular or noisy text layouts. We also examine widely used benchmark datasets in the literature and provide a detailed discussion of the performance achieved by recent state-of-the-art methods. Finally, this survey outlines open research challenges and potential future directions. It aims to serve as a comprehensive reference for both novice and experienced researchers by summarizing the latest developments in text recognition, including architectures, datasets, evaluation metrics, and practical considerations in model performance trade-offs and deployment.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"11 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247628","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

A Survey on Proactive Deepfake Defense: Disruption and Watermarking 主动深度伪造防御研究综述：干扰与水印

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-08 DOI: 10.1145/3771296

Hong-Hanh Nguyen-Le, Van-Tuan Tran, Thuc Nguyen, Nhien-An Le-Khac

{"title":"A Survey on Proactive Deepfake Defense: Disruption and Watermarking","authors":"Hong-Hanh Nguyen-Le, Van-Tuan Tran, Thuc Nguyen, Nhien-An Le-Khac","doi":"10.1145/3771296","DOIUrl":"https://doi.org/10.1145/3771296","url":null,"abstract":"The rapid proliferation of generative AI has led to led to unprecedented capabilities in synthesizing realistic deepfakes (DFs) across multiple modalities. This raises significant concerns regarding privacy, security, and copyright protection. Unlike passive detection approaches that operate after DFs have been created and distributed, proactive defense mechanisms aim to prevent the generation of malicious synthetic content at its source. This paper provides a comprehensive survey of current proactive DF defense strategies, including Disruption and Watermarking. Disruption approaches protect individuals’ data by introducing imperceptible perturbations that prevent unauthorized exploitation by generative models, while watermarking approaches embed verifiable messages into data or models to enable content authentication and attribution. We also analyze proactive approaches across various evaluation metrics (imperceptibility, protectability/detectability, transferability, traceability, and robustness), and examine their effectiveness in real-world settings. Furthermore, we review the evolution of DF generation techniques, highlighting their rapid developments. Finally, we identify key challenges and promising future research directions to enhance proactive defense mechanisms.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"7 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247631","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

Deep Learning Representations of Programs: A Systematic Literature Review 程序的深度学习表示：系统的文献综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-08 DOI: 10.1145/3769008

Deepika Shanmugasundaram, Pallavi Arivukkarasu, Huaming Chen, Haipeng Cai

{"title":"Deep Learning Representations of Programs: A Systematic Literature Review","authors":"Deepika Shanmugasundaram, Pallavi Arivukkarasu, Huaming Chen, Haipeng Cai","doi":"10.1145/3769008","DOIUrl":"https://doi.org/10.1145/3769008","url":null,"abstract":"In the contemporary era, deep learning (DL) is increasingly recognized as a promising approach for enabling and optimizing various techniques, notably in the domain of DL for code (software programs). In essence, deep learning is mainly representation learning, which naturally holds for this domain. Thus, at the core of DL for code is deep representation learning for programs. The learned program representations can then be applied to various coding related tasks, such as detecting vulnerabilities, providing recommendations for API usage, and extracting semantic and syntactic insights from extensive code lines. This is achieved by harnessing deep neural network architectures and deep-learning algorithms that take programs as inputs , serving various software engineering applications . In this paper, we conduct a systematic literature search to review studies pertaining to the representation of programs using deep learning approaches and their corresponding applications. Our search yielded 178 primary studies published between 2017 and 2023. Through these studies in the latest literature, we provide a systematization of knowledge in deep learning representation of programs, concerning the raw inputs to the learning pipeline, neural network architecture employed, learning algorithm utilized, and downstream tasks (i.e., applications ) of the learned representations. While examining the current landscape, we also identify limitations and challenges faced in the state of the art, as well as promising future research directions in deep program representation learning.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"56 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247629","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

A Survey on Privacy-Preserving Computing in the Automotive Domain 汽车领域隐私保护计算研究综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-08 DOI: 10.1145/3770580

Nergiz Yuca, Nikolay Matyunin, Ektor Arzoglou, Nikolaos Athanasios Anagnostopoulos, Stefan Katzenbeisser

引用次数: 0

Unsupervised Deep Learning for Anomaly Detection in Automotive Trucks: A Survey 无监督深度学习在汽车卡车异常检测中的应用研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-07 DOI: 10.1145/3771089

Manuel Hirth, Daniel Meier, Ottmar Gehring, Nasser Jazdi, Enkelejda Kasneci

引用次数: 0

A Survey on Stereotype Detection in Natural Language Processing 自然语言处理中的刻板印象检测研究综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-06 DOI: 10.1145/3770754

Alessandra Teresa Cignarella, Anastasia Giachanou, Els Lefever

引用次数: 0

A Survey of Defenses Against AI-Generated Visual Media: Detection, Disruption, and Authentication 对人工智能生成的视觉媒体的防御调查：检测、破坏和认证

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-04 DOI: 10.1145/3770916

Jingyi Deng, Chenhao Lin, Zhengyu Zhao, Shuai Liu, Zhe Peng, Qian Wang, Chao Shen

{"title":"A Survey of Defenses Against AI-Generated Visual Media: Detection, Disruption, and Authentication","authors":"Jingyi Deng, Chenhao Lin, Zhengyu Zhao, Shuai Liu, Zhe Peng, Qian Wang, Chao Shen","doi":"10.1145/3770916","DOIUrl":"https://doi.org/10.1145/3770916","url":null,"abstract":"Deep generative models have demonstrated impressive performance in various computer vision applications, including image synthesis, video generation, and medical analysis. Despite their significant advancements, these models may be used for malicious purposes, such as misinformation, deception, and copyright violation. In this paper, we provide a systematic and timely review of research efforts on defenses against AI-generated visual media, covering detection, disruption, and authentication. We review existing methods and summarize the mainstream defense-related tasks within a unified passive and proactive framework. Moreover, we survey the derivative tasks concerning the trustworthiness of defenses, such as their robustness and fairness. For each defense strategy, we formulate its general pipeline and propose a multidimensional taxonomy applicable across defense tasks, based on methodological strategies. Additionally, we summarize the commonly used evaluation datasets, criteria, and metrics. Finally, by analyzing the reviewed studies, we provide insights into current research challenges and suggest possible directions for future research.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"110 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145215778","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

A Systematic Review of the Use of Augmented Reality in Pedestrian Navigation 增强现实技术在行人导航中的应用综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-04 DOI: 10.1145/3770917

Yu Zhao, Holly Gagnon, Jeanine Stefanucci, Sarah Creem-Regehr, Bobby Bodenheimer

引用次数: 0

Workload Shifting Techniques: From Digital Inebriation to Sobriety 工作量转移技术：从数字醉酒到清醒

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-10-03 DOI: 10.1145/3769301

Nicolas Tirel, Philippe Roose, Sergio Ilarri, Adel Noureddine, Olivier Le Goaër

引用次数: 0

A Review on Self-Supervised Learning in Time Series Anomaly Detection: Recent Advances and Open Challenges 自监督学习在时间序列异常检测中的研究进展与挑战