Active Spread-Spectrum Steganalysis for Hidden Data Extraction
Ming Li, Michel Kulhandjian, Dimitris A. Pados, Stella N. Batalama, Michael J. Medley · 2011
Abstract : This paper considers the problem of blind active spreadspectrum (SS) steganalysis defined as the extraction of hidden data with no prior information. We first develop a multisignature iterative generalized least-squares (M-IGLS) core procedure to seek unknown messages hidden in image hosts via multi-signature direct-sequence spread-spectrum embedding. Neither the original host nor the embedding signatures are assumed available. Then, cross-correlation enhanced MIGLS (CC-M-IGLS), a procedure described herein in detail that is based on statistical analysis of repeated independent M-IGSL processing of the host, is seen to offer most effective hidden message recovery. In fact, experimental studies show that the proposed CC-M-IGLS active SS steganalysis algorithm can achieve probability of error close to what may be attained with known embedding signatures and host autocorrelation matrix.