摘要
针对JEPG图像隐写检测问题,提出了一种基于降维共生特征和单类分类器的通用隐写分析方法。采用共生矩阵挖掘图像DCT块内、块间以及图像小波层内、层间相邻系数的相关性特征,并对特征进行校准和LPP降维处理,利用SVDD分类器进行训练和分类。实验结果表明:该方法相比传统二类隐写分析方法,具有更强的泛化能力,检测率相比几种单类隐写分析方法有明显提高;而且,LPP降维相比PCA降维对提高算法的分类精度具有更好的效果。
A universal approach based on dimensionality-reduced co-occurrence features and oneclass classifier is proposed for steganalysis of JPEG images. The co-occurrence matrix is used to capture both the intra-block and inter-block correlation features among neighboring DCT coefficients as well as the intra-scale and inter-scale correlation features among neighboring DWT coefficients.Then the calibrated features are progressed by LPP dimensionality reduction techniques and a SVDD classifier is utilized to train and classify them. Experimental results show that the method performs better at detecting capability comparing to the traditional two-class steganalysis schemes and its detection rate is significantly higher than several novel single-class steganalysis schemes at present.Furthermore,LPP is much better than PCA for improving the algorithm＇s classification accuracy.
出处
《火力与指挥控制》
CSCD
北大核心
2015年第12期13-17,共5页
Fire Control & Command Control
基金
国家自然科学基金(61074191)
海军工程大学自然科学基金资助项目(HJGSK2014G120)
关键词
隐写分析
共生矩阵
支持向量数据描述
局部保持投影
steganalysis
co-occurrence matrix
Support Vector Data Description（SVDD）
Linearity Preserving Projection（LPP）
