Discriminative multimodal for steganalysis

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.