Ensemble Classifiers for Steganalysis of Digital Media
Ensemble Classifiers for Steganalysis of Digital Media
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DOI：
10.1109/tifs.2011.2175919
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发表时间：
2012-04-01
影响因子：
6.8
通讯作者：
Holub, Vojtech
中科院分区：
文献类型：
--
作者：
Kodovsky, Jan;Fridrich, Jessica;Holub, Vojtech
目前，最准确的数字媒体隐写分析方法是基于从媒体中提取的特征向量建立监督分类器。机器学习的首选工具似乎是支持向量机（SVM）。在本文中，我们提出了一种替代的、众所周知的机器学习工具——作为随机森林实现的集成分类器——并认为它们非常适合隐写分析。集成分类器在训练样例的数量和特征维度方面比更复杂的支持向量机更有优势。显著降低的训练复杂性为隐写分析者提供了使用丰富（高维）覆盖模型和在更大的训练集上进行训练的可能性——这两个关键因素对于可靠地检测现代隐写算法似乎是必要的。集成分类在这里被描述为一种强大的开发工具，它允许快速构建隐写检测器，并在广泛的嵌入方法中显著提高检测精度。通过三种隐藏JPEG图像中的消息的隐写方法证明了该框架的强大功能。
Today, the most accurate steganalysis methods for digital media are built as supervised classifiers on feature vectors extracted from the media. The tool of choice for the machine learning seems to be the support vector machine (SVM). In this paper, we propose an alternative and well-known machine learning tool-ensemble classifiers implemented as random forests-and argue that they are ideally suited for steganalysis. Ensemble classifiers scale much more favorably w.r.t. the number of training examples and the feature dimensionality with performance comparable to the much more complex SVMs. The significantly lower training complexity opens up the possibility for the steganalyst to work with rich (high-dimensional) cover models and train on larger training sets-two key elements that appear necessary to reliably detect modern steganographic algorithms. Ensemble classification is portrayed here as a powerful developer tool that allows fast construction of steganography detectors with markedly improved detection accuracy across a wide range of embedding methods. The power of the proposed framework is demonstrated on three steganographic methods that hide messages in JPEG images.