摘要
提出了一种G.729A自适应码本分组基音调制信息隐藏的检测算法.对语音码流的分析发现,通过基音预测进行信息隐藏将改变相邻语音帧中基音周期估计值的共生特性.通过量化这种共生特性,并经过PCA(Principal Component Analysis,主成分分析)降维获得对隐写检测敏感的特征向量.最后基于特征向量和SVM(Support Vector Machine,支持向量机)构建隐写检测器.对不同语音样本数据集的检测表明,当语音长度在2s及以上时,检测正确率均超过96%.此检测算法是一种有效的压缩域信息隐藏检测方法.
A detection algorithm of pitch modulation information hiding in G. 729A low bit-rate speech codec is proposed. The analysis of speech stream showed that pith modulation information biding would change the pitch symbiotic characteristics of adjacent speech frames. We designed a model to quantify these pitch symbiotic characteristics for steganalysis. However, the dimension of quantitative feature vector of pitch symbiotic characteristics was too bigh,so PCA （Principal Component Analysis） was employed to reduce the dimension of the feature vector. Finally, we built a pitch modulation information biding detector based on the dimension reduced feature vector and SVM （Support Vector Macbine） classifier. Experiments on different speech datasets show that the proposed steganalysis algorithm is very effective： the accuracy is more than 96 % when speech length equals to or is greater than 2s. So this paper gives an effective method for compression domain information hiding detection.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2015年第8期1513-1517,共5页
Acta Electronica Sinica
基金
国家自然科学基金(No.61303249)
海南省自然科学基金(No.614236)
海南省重大科技项目课题(No.JDJS2013006)
关键词
基音调制信息隐藏
共生特性
隐写检测
主成分分析
支持向量机
pitch modulation information biding
symbiotic characteristics
steganography detection
principal component analysis （PCA）
support vector machine （SVM）
