Learning and transferring representations for image steganalysis using convolutional neural network

Abstract

The major challenge of machine learning based image steganalysis lies in obtaining powerful feature representations. Recently, Qian et al. have shown that Convolutional Neural Network (CNN) is effective for learning features automatically for steganalysis. In this paper, we follow up this new paradigm in steganalysis, and propose a framework based on transfer learning to help the training of CNN for steganalysis, hence to achieve a better performance. We show that feature representations learned with a pre-trained CNN for detecting a steganographic algorithm with a high payload can be efficiently transferred to improve the learning of features for detecting the same steganographic algorithm with a low pay-load. By detecting representative WOW and S-UNIWARD steganographic algorithms, we demonstrate that the proposed scheme is effective in improving the feature learning in CNN models for steganalysis.

Publication
2016 IEEE International Conference on Image Processing (ICIP)