Steganalysis by Subtractive Pixel Adjacency Matrix
Steganalysis by Subtractive Pixel Adjacency Matrix
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DOI：
10.1109/tifs.2010.2045842
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发表时间：
2010-06-01
影响因子：
6.8
通讯作者：
Fridrich, Jessica
中科院分区：
文献类型：
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作者：
Pevny, Tomas;Bas, Patrick;Fridrich, Jessica
提出了一种通过添加低幅度独立隐写信号来检测嵌入在空域中的隐写方法的方法，最低有效位(LSB)匹配就是其中的一个例子。首先，利用一阶和二阶马尔可夫链对相邻像素之间的差异进行建模。然后将样本转移概率矩阵的子集用作由支持向量机实现的隐写分析器的特征。在四个不同的图像库上进行的实验主要集中在对LSB匹配检测的评估上。与现有技术的比较表明，所提出的特征集在检测LSB匹配方面提供了更高的准确度。尽管特征集是专门为空域隐写分析而开发的，但通过为JPEG图像的10种算法构建隐写分析器，证明了这些特征也可以在变换域中检测隐写。
This paper presents a method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which is least significant bit (LSB) matching. First, arguments are provided for modeling the differences between adjacent pixels using first-order and second-order Markov chains. Subsets of sample transition probability matrices are then used as features for a steganalyzer implemented by support vector machines. The major part of experiments, performed on four diverse image databases, focuses on evaluation of detection of LSB matching. The comparison to prior art reveals that the presented feature set offers superior accuracy in detecting LSB matching. Even though the feature set was developed specifically for spatial domain steganalysis, by constructing steganalyzers for ten algorithms for JPEG images, it is demonstrated that the features detect steganography in the transform domain as well.