图像隐写术方法及其检测策略概览

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Meike Helena Kombrink, Zeno Jean Marius Hubert Geradts, Marcel Worring
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引用次数: 0

摘要

隐写术是一门隐藏(或掩盖)通信的艺术和科学。在数字隐写术中,对图像、视频、音频和文本文件的比特进行调整,以表示要隐藏的信息。本文介绍了目前在图像中隐藏信息的方法,以及旨在检测是否存在隐写术的隐写分析方法。通过查阅 456 篇参考文献,本文讨论了可用于隐写术的不同方法及其研究较少的对应方法。目前在研究中,旧的隐写术方法比新的方法使用得更广泛,尽管这些方法显示出更大的潜力。新方法确实存在缺陷,因此需要进行更多的研究,使这些方法切实可行。对于隐写分析来说,最大的挑战之一就是通用性。通常情况下,一种方案只能检测到一种特定隐藏方法的存在。需要开展更多的研究,将现有的方案结合起来,并/或创建新的通用方案。为了让读者能够比较不同论文之间的结果,我们概述了所有隐写分析方法的性能指标,并对性能进行了比较。比较采用 "拓扑排序 "图,比较了所有论文(如论文本身所述)对不同隐写方案的检测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image steganography approaches and their detection strategies: a survey
Steganography is the art and science of hidden (or covered) communication. In digital steganography, the bits of images, videos, audio and text files are tweaked to represent the information to hide. This paper covers the current methods for hiding information in images, alongside steganalysis methods which aim to detect the presence of steganography. By reviewing 456 references, this paper discusses the different approaches that can be taken toward steganography and its much less widely studied counterpart. Currently in research older steganography approaches are more widely used than newer methods even though these show greater potential. New methods do have flaws, therefore more research is needed to make these practically applicable. For steganalysis one of the greatest challenges is the generalisability. Often one scheme can detect the presence of one specific hiding method. More research is needed to combine current schemes and/or create new generalisable schemes. To allow readers to compare results between different papers in our work performance indications of all steganalysis methods are outlined and a comparison of performance is included. This comparison is given using ’topological sorting’ graphs, which compares detection results from all papers (as stated in the papers themselves) on different steganographic schemes.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
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.
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