To investigate the presence of hidden information in cover photographic images is very important for image steganalysis at the present time. Steganalysis can be also regarded as a pattern recognition classification problem to decide which class a test image is classified as: the innocent photographic image or the stego-image. In this paper we propose an Randomized Neural Network (RNN), based multi-modality classifier to improve the accuracy of image steganalysis. In this work: multi-modality steganalysis may provide complementary information to discriminate stego-images from innocent images. Experiments results show that our multimodal scheme can effectively promote the accuracy of image steganalysis and achieve performance at high speed. We also achieve a classification accuracy of 93.43% when combining all five modalities of steganalysis model, and only 91.33% when using even the best individual modality of steganalysis model.