基于主成分分析进行特征融合的JPEG隐写分析

摘要

To solve problems in the existing JPEG steganalysis schemes, such as high redundancy in features and failure to make good use of the complementarity among them, this study proposes a JPEG steganalysis approach based on feature fusion by the principal component analysis (PCA) and analysis of the complementarity among features. The study fuses complementary features to reflect the statistical differences between cover and stego signals in the round, isolates redundant components by PCA, and finally achieves the goal of improving accuracy. Experimental results show that in various datasets and embedding rates, this scheme provides more accuracy than the main JPEG steganalysis schemes against steganographic methods of high concealment (e.g. F5, MME and PQ) and greatly reduces the time cost of the existing fusion methods on feature level.%为了解决现有JPEG隐写分析方法特征冗余度高和未能充分利用特征间互补关系的问题,提出了一种基于主成分分析(principal component analysis,简称PCA)进行特征融合的JPEG隐写分析方法,并分析所选特征之间的互补性.通过融合将互补特征结合在一起,更全面地反映载体和隐写信号间的统计差异,并用PCA分离出冗余成分,最终达到进一步提升准确率的目的,实验结果表明,在不同数据集和嵌入率情况下,该方法分析高隐蔽性隐写(如F5,MME和PQ)的准确率高于主要PEG分析方法,在耗时上较现有特征层融合降维方法大为缩短.