STEGANOGRAPHY DETECTION USING RBFNN

摘要：

A machine learning approach based on alpha-trimmed mean feature preprocessing is introduced to determine whether secret messages are hidden within JPEG images. This paper also integrates a multi-preprocessing sequence to develop the classification system which contains features generated from an image dataset including steganographic and clean images, feature ranking and selection, feature extraction, and data standardization. Neural networks using radial basis functions train the classifier to accomplish the decision making progress. The analyzed image is labeled as either a steganographic or a clean image. The computer simulations have shown that classification accuracy increases by 40％ when using feature preprocessing within the complete detection system over a system without feature preprocessing. In addition, alpha-trimmed mean (including mean and median) statistics approach results in higher classification accuracy.

关键词： Steganography steganalysis radial basis function neural networks pattern classification alpha-trimmed mean

作者: MEI-CHING CHEN SOS S.AGAIAN C.L.PHILIP CHEN BENJAMIN M.RODRIGUEZ

作者单位: Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Anton Department of Electrical and Computer Engineering, Air Force Institute of Technology, WPAFB, U.S.A.

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3720-3725

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）