| 摘要: |
| 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JPEG 分析方法,在耗时上较现有特征层融合降维方法大为缩短. |
| 关键词: 特征融合 主成分分析 特征降维 隐写分析 |
| DOI：10.3724/SP.J.1001.2012.04107 |
| 分类号: |
| 基金项目:国家自然科学基金(61170281); 北京市自然科学基金(4112063); 中国科学院战略性先导专项课题(XDA06030601);中国科学院信息工程研究所创新课题(Y1Z0041101,Y1Z0051101) |
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| JPEG Steganalysis Based on Feature Fusion by Principal Component Analysis |
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HUANG Wei1, ZHAO Xian-Feng1, FENG Deng-Guo1, SHENG Ren-Nong2
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1.State Key Laboratory of Information Security, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China;2.Beijing Institute of Electronic Technology and Application, Beijing 100091, China
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| Abstract: |
| 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. |
| Key words: feature fusion PCA (principal component analysis) feature reduction steganalysis |
