Unsupervised steganalysis over social networks based on multi-reference sub-image sets

This work proposes a new unsupervised steganalysis scheme which mainly tackles the challenge in identifying individual JPEG image as stego or cover. The proposed scheme does not need a large number of samples to train classification model, and thus it is significantly different from the existing supervised steganalysis schemes. The proposed scheme employs calibration technology to construct multiple reference images from one suspicious image. These reference images are considered as the imitation of cover. Furthermore, randomized sampling is performed to construct sub-image sets from suspicious image and reference images, respectively. By calculating the maximum mean discrepancy between any two sub-image sets, an efficient measure is provided to give the optimal decision on this suspicious image. Experimental results show that the proposed scheme is effective and efficient in identifying individual image, and outperforms the state-of-the-art steganalysis scheme. © 2017, Springer Science+Business Media New York.