This paper takes the task of image steganalysis as a texture classification problem. The impact of steganography to an
image is viewed as the alteration of image texture in a fine scale. Specifically, stochastic textures are more likely to
appear in a stego image than in a cover image from our observation and analysis. By developing a feature extraction
technique previously used in texture classification, we propose a set of universal steganalytic features, which are
extracted from the normalized histograms of the local linear transform coefficients of an image. Extensive experiments
are conducted to make comparison of our proposed feature set with some existing universal steganalytic feature sets on
gray-scale images by using Fisher Linear Discriminant (FLD). Some classical non-adaptive spatial domain
steganographic algorithms, as well as some newly presented adaptive spatial domain steganographic algorithms that have
never been reported to be broken by any universal steganalytic algorithm, are used for benchmarking. We also report the
detection performance on JPEG steganography and JPEG2000 steganography. The comparative experimental results
show that our proposed feature set is very effective on a hybrid image database.
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