This paper presents a method to detect the quantization index modulation(QIM) steganography in G.723.1 bit stream.We show that the distribution of each quantization index(codeword) in the quantization index sequence has unbalanced and correlated characteristics.We present the designs of statistical models to extract the quantitative feature vectors of these characteristics.Combining the extracted vectors with the support vector machine,we build the classifier for detecting the QIM steganography in G.723.1 bit stream.The experiment shows that the method has far better performance than the existing blind detection method which extracts the feature vector in an uncompressed domain.The recall and precision of our method are all more than 90% even for a compressed bit stream duration as low as 3.6 s.
QIM(Quantization Index Modulation,量化索引调制)隐写在标量或矢量量化时嵌入机密信息,可在语音压缩编码过程中进行高隐蔽性的信息隐藏,文中试图对该种隐写进行检测.文中发现该种隐写将导致压缩语音流中的音素分布特性发生改变,提出了音素向量空间模型和音素状态转移模型对音素分布特性进行了量化表示.基于所得量化特征并结合SVM(Support Vector Machine,支持向量机)构建了隐写检测器.针对典型的低速率语音编码标准G.729以及G.723.1的实验表明,文中方法性能远优于现有检测方法,实现了对QIM隐写的快速准确检测.
有学者提出了一种在压缩语音编码过程中进行QIM(Quantization Index Modulation)隐写的方法.该方法可用于在G.729A压缩语音流中高隐蔽性地嵌入秘密信息,研究其隐写分析方法很有必要.本文首先分析了QIM隐写对G.729A码流造成的显著性特征变化,发现该种隐写将使码流中LPC滤波器的量化索引(码字)发生转移,并导致码字分布的不均衡性及相关性特性发生改变.本文设计了统计模型,实现了对码字分布特性的量化特征抽取;结合支持向量机,本文构造了用于隐写检测的集成分类器系统.实验结果显示本文方法能够在低于30ms的时间内,获得超过98%的检测准确率,实现了对QIM隐写的快速有效检测.
