Steganalysis Using Wavelet Transform

Abstract

Techniques and applications for information hiding have become increasingly more sophisticated and widespread. With high-resolution digital images as carriers, detecting the presence of hidden messages has also become considerably more difficult. It is sometimes possible, nevertheless, to detect (but not necessarily decipher) the presence of embedded messages. The basic approach taken here works by finding predictable higher-order statistics of "natural" images within a multi-scale decomposition, and then showing that embedded messages alter these statistics.The decomposition of images using basis functions that are localized in spatial position, orientation, and scale (such as wavelets) has proven extremely useful in a range of applications. One reason for this is that such decompositions exhibit statistical regularities that can be exploited.The proposed algorithm consist of three stages: Image feature extraction (IFE) stage, training stage, and testing stage. In IFE the image decomposes to four level wavelet. Set of statistics (mean, skewness, and kurtosis for each subband) is collected from this decomposition. Thesecond set of statistics collected is based on the errors in an optimal linear predictor of coefficient magnitude. In this predictor, the subband coefficients are correlated to their spatial, orientation and scale neighbors.The steganalysis technique was tested on samples of images processed with most commercial steganographic software products.