  Deep learning based image steganalysis has attracted increasing attentions in
recent years. Several Convolutional Neural Network (CNN) models have been
proposed and achieved state-of-the-art performances on detecting steganography.
In this paper, we explore an important technique in deep learning, the batch
normalization, for the task of image steganalysis. Different from natural image
classification, steganalysis is to discriminate cover images and stego images
which are the result of adding weak stego signals into covers. This
characteristic makes a cover image is more statistically similar to its stego
than other cover images, requiring steganalytic methods to use paired learning
to extract effective features for image steganalysis. Our theoretical analysis
shows that a CNN model with multiple normalization layers is hard to be
generalized to new data in the test set when it is well trained with paired
learning. To hand this difficulty, we propose a novel normalization technique
called Shared Normalization (SN) in this paper. Unlike the batch normalization
layer utilizing the mini-batch mean and standard deviation to normalize each
input batch, SN shares same statistics for all training and test batches. Based
on the proposed SN layer, we further propose a novel neural network model for
image steganalysis. Extensive experiments demonstrate that the proposed network
with SN layers is stable and can detect the state of the art steganography with
better performances than previous methods.
