Feature reduction and payload location with WAM steganalysis
Feature reduction and payload location with WAM steganalysis
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DOI：
10.1117/12.805910
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发表时间：
2009-02
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通讯作者：
Andrew D. Ker;Ivans Lubenko
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文献类型：
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作者：
Andrew D. Ker;Ivans Lubenko
WAM隐写分析是Goljan等人在2006年提出的一种基于特征的分类器，用于检测LSB匹配隐写术，并被证明即使对小负载也很敏感。本文对WAM方法的发展做出了三个贡献。首先，我们在一些封面图像集中对WAM的一些变体进行了基准测试，并且我们能够量化基于WAM特征的不同机器学习算法之间结果差异的重要性。事实证明，与许多竞争对手一样，WAM在某些类型的覆盖中无效，而且很难预测哪些类型的覆盖适合WAM隐写分析。其次，我们证明了只有少数的特征用于WAM隐写分析做几乎所有的工作，因此，一个简化的WAM隐写分析器可以构建在换取一个小的检测能力。最后，我们演示了如何WAM方法可以扩展到提供取证工具，以确定LSB匹配的有效载荷的位置（和潜在的内容），给定一些隐写图像与有效载荷放置在相同的位置。虽然很容易逃避，但如果同一个隐写密钥被错误地重复用于嵌入多个图像，这是一个合理的情况。
WAM steganalysis is a feature-based classifier for detecting LSB matching steganography, presented in 2006 by Goljan et al. and demonstrated to be sensitive even to small payloads. This paper makes three contributions to the development of the WAM method. First, we benchmark some variants of WAM in a number of sets of cover images, and we are able to quantify the significance of differences in results between different machine learning algorithms based on WAM features. It turns out that, like many of its competitors, WAM is not effective in certain types of cover, and furthermore it is hard to predict which types of cover are suitable for WAM steganalysis. Second, we demonstrate that only a few the features used in WAM steganalysis do almost all of the work, so that a simplified WAM steganalyser can be constructed in exchange for a little less detection power. Finally, we demonstrate how the WAM method can be extended to provide forensic tools to identify the location (and potentially content) of LSB matching payload, given a number of stego images with payload placed in the same locations. Although easily evaded, this is a plausible situation if the same stego key is mistakenly re-used for embedding in multiple images.