Steganalysis of DCT-embedding based adaptive steganography and YASS
Steganalysis of DCT-embedding based adaptive steganography and YASS
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DOI：
10.1145/2037252.2037267
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发表时间：
2011-09
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影响因子：
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通讯作者：
Qingzhong Liu
中科院分区：
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作者：
Qingzhong Liu
最近，设计良好的自适应隐写系统，包括在DCT域中嵌入±1，并优化成本以实现最小失真[8]，给隐写分析器带来了严重的挑战。此外，尽管对另一种隐写方案(YASS)的隐写分析正在积极进行，但通过大的B块参数来检测YASS隐写还没有得到很好的探索。在本文中，我们的目标是检测DCT嵌入中的最新自适应隐写系统，并改进YASS的隐写分析。为了检测基于DCT嵌入的自适应隐写，我们在原始JPEG图像和校准版本之间的DCT系数的绝对阵上设计了差分相邻关节密度的特征。为了区分Yass隐写图和封面，首先识别可能用于嵌入的候选块和不可能用于信息隐藏的非候选块邻居。得到了候选块与非候选块相邻节点密度的差值。采用支持向量机和Logistic回归分类器进行分类。实验结果表明，该方法在检测基于DCT嵌入的自适应隐写时具有很好的应用前景。与基于CC-PEV特征集的隐写分析方法相比，该方法大大提高了检测精度，在检测相对负载较低的隐写图像时优势尤为明显。在对YASS的隐写分析中，我们的方法优于以前著名的隐写分析算法；我们的方法显著地提高了检测精度，特别是对以前没有很好解决的大B块产生的YASS隐写图像的检测。
Recently well-designed adaptive steganographic systems, including ±1 embedding in the DCT domain with optimized costs to achieve the minimal-distortion [8], have posed serious challenges to steganalyzers. Additionally, although the steganalysis of Yet Another Steganographic Scheme (YASS) was actively conducted, the detection of the YASS steganograms by a large B-block parameter has not been well explored. In this paper, we aim to detect the state-of-the-art adaptive steganographic system in DCT-embedding and to improve the steganalysis of YASS. To detect DCT-embedding based adaptive steganography, we design the features of differential neighboring joint density on the absolute array of DCT coefficients between the original JPEG images and the calibrated versions. To discriminate YASS steganograms from covers, the candidate blocks that are possibly used for embedding and the non-candidate block neighbors that are impossibly used for information hiding are identified first. The difference of the neighboring joint density between candidate blocks and the non-candidate neighbors is obtained. Support Vector Machine (SVM) and logistic regression classifiers are employed for classification. Experimental results show that our approach is very promising when detecting DCT-embedding based adaptive steganography. Compared to the steganalysis based on CC-PEV feature set, our method greatly improves the detection accuracy; the advantage is especially noticeable in the detection of the steganograms with low relative payload. In steganalysis of YASS, our approach is superior to a previous well-known steganalysis algorithm; our method remarkably improves the detection accuracy especially in the detection of the YASS steganograms that are produced with a large B-block size, which was not well addressed before.