ACM Computing Surveys最新文献

A Generic Taxonomy for Steganography Methods

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-09 DOI: 10.1145/3729165

Steffen Wendzel, Luca Caviglione, Wojciech Mazurczyk, Aleksandra Mileva, Jana Dittmann, Christian Krätzer, Kevin Lamshöft, Claus Vielhauer, Laura Hartmann, Jörg Keller, Tom Neubert, Sebastian Zillien

{"title":"A Generic Taxonomy for Steganography Methods","authors":"Steffen Wendzel, Luca Caviglione, Wojciech Mazurczyk, Aleksandra Mileva, Jana Dittmann, Christian Krätzer, Kevin Lamshöft, Claus Vielhauer, Laura Hartmann, Jörg Keller, Tom Neubert, Sebastian Zillien","doi":"10.1145/3729165","DOIUrl":"https://doi.org/10.1145/3729165","url":null,"abstract":"A unified understanding of terms is essential for every scientific discipline: steganography is no exception. Being divided into several domains (e.g., network and text steganography), it is crucial to provide a unified terminology as well as a taxonomy that is not limited to few applications or areas. A prime attempt towards a unified understanding of terms was conducted in 2015 with the introduction of a pattern-based taxonomy for network steganography. In 2021, the first work towards a pattern-based taxonomy for all domains of steganography was proposed. However, this initial attempt still faced several shortcomings, e.g., remaining inconsistencies and a lack of patterns for several steganography domains. As the consortium who published the previous studies on steganography patterns, we present the first comprehensive pattern-based taxonomy tailored to fit all known domains of steganography, including smaller and emerging areas, such as filesystem, IoT/CPS, and AI/ML steganography. To make our contribution more effective and promote the use of the taxonomy to advance research, we also provide a unified description method joint with a thorough tutorial on its utilization.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"183 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805846","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

Proof Scores: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-09 DOI: 10.1145/3729166

Adrián Riesco, Kazuhiro Ogata, Masaki Nakamura, Daniel Gaina, Duong Dinh Tran, Kokichi Futatsugi

引用次数: 0

AI-Generated Content (AIGC) for Various Data Modalities: A Survey

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728633

Lin Geng Foo, Hossein Rahmani, Jun Liu

{"title":"AI-Generated Content (AIGC) for Various Data Modalities: A Survey","authors":"Lin Geng Foo, Hossein Rahmani, Jun Liu","doi":"10.1145/3728633","DOIUrl":"https://doi.org/10.1145/3728633","url":null,"abstract":"AI-generated content (AIGC) methods aim to produce text, images, videos, 3D assets, and other media using AI algorithms. Due to its wide range of applications and the potential of recent works, AIGC developments – especially in Machine Learning (ML) and Deep Learning (DL) – have been attracting significant attention, and this survey focuses on comprehensively reviewing such advancements in ML/DL. AIGC methods have been developed for various data modalities, such as image, video, text, 3D shape, 3D scene, 3D human avatar, 3D motion, and audio – each presenting unique characteristics and challenges. Furthermore, there have been significant developments in cross-modality AIGC methods, where generative methods receive conditioning input in one modality and produce outputs in another. Examples include going from various modalities to image, video, 3D, and audio. This paper provides a comprehensive review of AIGC methods across different data modalities, including both single-modality and cross-modality methods, highlighting the various challenges, representative works, and recent technical directions in each setting. We also survey the representative datasets throughout the modalities, and present comparative results for various modalities. Moreover, we discuss the typical applications of AIGC methods in various domains, challenges, and future research directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"227 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143797962","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

Visual Question Answering: A Survey of Methods, Datasets, Evaluation, and Challenges

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728635

Byeong Su Kim, Jieun Kim, Deokwoo Lee, Beakcheol Jang

{"title":"Visual Question Answering: A Survey of Methods, Datasets, Evaluation, and Challenges","authors":"Byeong Su Kim, Jieun Kim, Deokwoo Lee, Beakcheol Jang","doi":"10.1145/3728635","DOIUrl":"https://doi.org/10.1145/3728635","url":null,"abstract":"Visual question answering (VQA) is a dynamic field of research that aims to generate textual answers from given visual and question information. It is a multimodal field that has garnered significant interest from the computer vision and natural language processing communities. Furthermore, recent advances in these fields have yielded numerous achievements in VQA research. In VQA research, achieving balanced learning that avoids bias towards either visual or question information is crucial. The primary challenge in VQA lies in eliminating noise, while utilizing valuable and accurate information from different modalities. Various research methodologies have been developed to address these issues. In this study, we classify these research methods into three categories: Joint Embedding, Attention Mechanism, and Model-agnostic methods. We analyze the advantages, disadvantages, and limitations of each approach. In addition, we trace the evolution of datasets in VQA research, categorizing them into three types: Real Image, Synthetic Image, and Unbiased datasets. This study also provides an overview of evaluation metrics based on future research directions. Finally, we discuss future research and application directions for VQA research. We anticipate that this survey will offer useful perspectives and essential information to researchers and practitioners seeking to address visual questions effectively.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"31 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805574","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

Similarity of Neural Network Models: A Survey of Functional and Representational Measures

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728458

Max Klabunde, Tobias Schumacher, Markus Strohmaier, Florian Lemmerich

引用次数: 0

Hybrids of Reinforcement Learning and Evolutionary Computation in Finance: A Survey 强化学习和进化计算在金融领域的混合应用：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728634

Sandarbh Yadav, Vadlamani Ravi, Shivaram Kalyanakrishnan

引用次数: 0

Carbon-Efficient Software Design and Development: A Systematic Literature Review

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728638

Ornela Danushi, Stefano Forti, Jacopo Soldani

引用次数: 0

Ante-Hoc Methods for Interpretable Deep Models: A Survey 可解释深度模型的前置方法：调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728637

Antonio Di Marino, Vincenzo Bevilacqua, Angelo Ciaramella, Ivanoe De Falco, Giovanna Sannino

引用次数: 0

Efficient Compressing and Tuning Methods for Large Language Models: A Systematic Literature Review

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-08 DOI: 10.1145/3728636

Gun Il Kim, Sunga Hwang, Beakcheol Jang

{"title":"Efficient Compressing and Tuning Methods for Large Language Models: A Systematic Literature Review","authors":"Gun Il Kim, Sunga Hwang, Beakcheol Jang","doi":"10.1145/3728636","DOIUrl":"https://doi.org/10.1145/3728636","url":null,"abstract":"Efficient compression and tuning techniques have become indispensable in addressing the increasing computational and memory demands of large language models (LLMs). While these models have demonstrated exceptional performance across a wide range of natural language processing tasks, their growing size and resource requirements pose significant challenges to accessibility and sustainability. This survey systematically reviews state-of-the-art methods in model compression, including compression techniques such as knowledge distillation, low-rank approximation, parameter pruning, and quantization, as well as tuning techniques such as parameter-efficient fine-tuning and inference optimization. Compression techniques, though well-established in traditional deep learning, require updated methodologies tailored to the scale and dynamics of LLMs. Simultaneously, parameter-efficient fine-tuning, exemplified by techniques like Low-Rank Adaptation (LoRA) and query tuning, emerges as a promising solution for adapting models with minimal resource overhead. This study provides a detailed taxonomy of these methods, examining their practical applications, strengths, and limitations. Critical gaps are identified in scalability, and the integration of compression and tuning strategies, signaling the need for unified frameworks and hybrid approaches to maximize efficiency and performance. By addressing these challenges, this survey aims to guide researchers toward sustainable, efficient, and accessible LLM development, ensuring their broader applicability across diverse domains while mitigating resource constraints.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"4 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805570","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

Diffusion-Based Visual Art Creation: A Survey and New Perspectives

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2025-04-05 DOI: 10.1145/3728459

Bingyuan Wang, Qifeng Chen, Zeyu Wang

引用次数: 0