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.