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":null,"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":23.8000,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Computing Surveys","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3729165","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods.
ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.