关键词: 隐写分析, 非线性混合核函数, 特征映射, Nyströ, m近似

Abstract:

To reduce detection error of steganographic image by combining feature projection of high-dimensional rich model with classifier, this paper proposes a steganalysis method combining feature segmentation and feature projection algorithm based on hybrid kernel. The high-dimensional features are decomposed into several feature blocks, each feature block is projected, the projected feature blocks are spelled into new features. A new non-linear hybrid kernel function is proposed instead the single kernel function for feature projection, to overcome the phenomenon of large sample size and multidimensional data. The projected features are classified by the Fisher Linear Discriminant（FLD） ensemble classifier. Experimental results show that this method further reduces the detection error of steganographic images, and reduces the running memory requirements.

Key words: steganalysis, nonlinear hybrid kernel function, feature mapping, Nyström approximation