JPEG Steganalysis Based on Feature Fusion by Principal Component Analysis

Abstract:

Aiming to the problems in the existing JPEG steganalysis schemes, such as high redundancy in features and failure to make good use of the complementarities among them, this study proposed a JPEG steganalysis approach based on feature fusion by the principal component analysis (PCA) and analysis of the complementarities among features. The study fused complementary features and isolated redundant components by PCA, and finally used RBaggSVM classifier for classification. Experimental results show that this scheme effectively improves the detection rate of steganalysis in JPEG images and achieves faster speed of image classification.

• Periodical: Applied Mechanics and Materials (Volumes 263-266)
• Pages: 2933-2938
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.263-266.2933
• Online since: December 2012
• Authors: Feng Ying He, Shang Ping Zhong, Kai Zhi Chen
• Keywords: Complementary Feature, Feature Fusion, PCA-RBaggSVM Algorithm, Principal Component Analysis (PCA), Steganalysis

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