Deep Learning Hierarchical Representations for Image Steganalysis

Nowadays, the prevailing detectors of steganographic communication in digital images mainly consist of three steps, i.e., residual computation, feature extraction, and binary classification. In this paper, we present an alternative approach to steganalysis of digital images based on convolutional neural network (CNN), which is shown to be able to well replicate and optimize these key steps in a unified framework and learn hierarchical representations directly from raw images. The proposed CNN has a quite different structure from the ones used in conventional computer vision tasks. Rather than a random strategy, the weights in the first layer of the proposed CNN are initialized with the basic high-pass filter…

• NN
  National Natural Science Foundation of China

  Awards: 60970145, 61672546, 61379156
• NS
  Natural Science Foundation of Guangdong Province

  Award: S2012020011114