Steganalyzing Texture Images

Abstract

A texture image is of noisy nature in its spatial representation. As a result, the data hidden in texture images, in particular in raw texture images, are hard to detect with current steganalytic methods. We propose an effective universal steganalyzer in this paper, which combines features, i.e., statistical moments of 1-D and 2-D characteristic functions extracted from the spatial representation and the block discrete cosine transform (BDCT) representations (with a set of different block sizes) of a given test image. This novel scheme can greatly improve the capability of attacking steganographic methods applied to texture images. In addition, it is shown that this scheme can be used as an effective universal steganalyzer for both texture and non-texture images.