System steganalysis with automatic fingerprint extraction.

System steganalysis with automatic fingerprint extraction.
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DOI:
10.1371/journal.pone.0195737
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发表时间:
2018
期刊:
影响因子:
3.7
通讯作者:
Isasi P
Isasi P
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Cervantes A;Sloan T;Hernandez-Castro J;Isasi P

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本文试图通过提出一种新的方法来解决大数据实际隐写分析的现代挑战,该方法的目标是以完全自动的方式精确地执行。目标是检测隐写过程在这些数据对象中引入的更改,包括与所使用工具相关的签名。我们的方法首先通过分析修改和未修改的数据对象来提取可靠的数据,然后通过创建用于训练的数据上的一般模式来结合这些发现。最后,我们构建了一个用于执行分类的朴素贝叶斯模型,并对使用上述模式提取的属性进行操作。这种技术已被应用于不同的隐写工具,在几种类型的媒体文件中操作。我们能够复制或改进一些或以前发表的结果,但更重要的是,我们还提出了新的隐写分析发现,超过了一些流行的工具,以前没有已知的攻击。
This paper tries to tackle the modern challenge of practical steganalysis over large data by presenting a novel approach whose aim is to perform with perfect accuracy and in a completely automatic manner. The objective is to detect changes introduced by the steganographic process in those data objects, including signatures related to the tools being used. Our approach achieves this by first extracting reliable regularities by analyzing pairs of modified and unmodified data objects; then, combines these findings by creating general patterns present on data used for training. Finally, we construct a Naive Bayes model that is used to perform classification, and operates on attributes extracted using the aforementioned patterns. This technique has been be applied for different steganographic tools that operate in media files of several types. We are able to replicate or improve on a number or previously published results, but more importantly, we in addition present new steganalytic findings over a number of popular tools that had no previous known attacks.
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