开发一种有效的隐写分析框架,用于发现数字媒体中隐藏的数据

V. Leask, R. Cogranne, D. Borghys, Helena Bruyninckx
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引用次数: 1

摘要

本文介绍了地平线2020项目的总体目标,其总体目标是缩小数据隐藏领域的学术工作与操作需求之间的差距。虽然数字数据隐藏是一个相对较新的研究领域,但我们在这个项目中的动机源于学术界和“现实生活”场景的操作需求之间日益扩大的差距,即为了发现秘密隐藏的数据的存在而进行对象检查。除了对discover结构的监督外,我们的论文还提出了一项实证研究,研究了针对给定数据隐藏方案(所谓的Stego-Source Mismatch)专门训练检测方法的影响,作为未探索问题的一个例子,该问题在discover项目目标的操作上下文中提出了重要且大多被忽视的后果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UNCOVER: Development of an efficient steganalysis framework for uncovering hidden data in digital media
This paper presents the general goals of Horizon 2020 project UNCOVER, whose overall purpose is to close the gap between academic work and operational needs in the fields of data-hiding. While digital data-hiding is a relatively new area of research, our motivation in this project has been rooted in the growing gap between the academic community and the operational needs of a ”real-life” scenario of object inspection in order to UNCOVER the presence of data secretly hidden. As well as an oversight into the structure of UNCOVER, our paper presents an empirical study on the impact of specifically training a detection method for a given data-hiding scheme, the so-called Stego-Source Mismatch, as an example of unexplored issues that raises important and mostly ignored consequences within the operational context the UNCOVER project targets.
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