It is observed that the co-occurrence matrix, one kind of textural features proposed by Haralick et al., has played a very critical role in steganalysis. On the other hand, the data hidden in the image texture area has been known difficult to detect for years, and the modern steganographic schemes tend to embed data into complicated texture area where the statistical modeling becomes difficult. Based on these observations, we propose to learn and utilize the textural features from the rich literature in the field of texture classification for further development of the modern steganalysis. As a demonstration, a group of textural features, including the local binary patterns, Markov neighborhoods and cliques, and Laws' masks, have been selected to form a new set of 22,153 features, which are used with the FLD-based ensemble classifier to steganalyze the HUGO on BOSSbase 0.92. At the embedding rate of 0.4 bpp (bit per pixel) an average detection accuracy of 83.92% has been achieved. It is expected that this new approach can enhance our capability in steganalysis.