This paper presents a novel blind steganalytic scheme of color images on the basis of RGB space to effectively prevent steganography. The proposed scheme includes intra-channel and inter-channel features. Intra-channel features are formed by features of co-occurrence matrices from the difference planes; these features effectively capture the dependency among coefficients in any color channel. Inter-channel features are extracted in second-order difference planes between channels; these features can effectively capture the dependency between channels. During classification

the costs of each sub-classifier are optimized by the genetic algorithm. Several sub-classifiers with optimal costs are selected

and the optimal decisions are synthesized through majority voting. Experimental results show that the prediction error rate of the proposed features is 4%~5% lower than that of SPAM features

whereas the prediction error rate of the selective ensemble classifier is 1%~2% lower than that of the ensemble classifier. The proposed scheme has minimal time complexity and is applicable to low-embedding color RGB images. Furthermore

the performance of the proposed scheme outperforms state-of-the-art steganalytic schemes.