Active Spread-Spectrum Steganalysis for Hidden Data Extraction

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.