  Image steganalysis is a special binary classification problem that aims to
classify natural cover images and suspected stego images which are the results
of embedding very weak secret message signals into covers. How to effectively
suppress cover image content and thus make the classification of cover images
and stego images easier is the key of this task. Recent researches show that
Convolutional Neural Networks (CNN) are very effective to detect steganography
by learning discriminative features between cover images and their stegos.
Several deep CNN models have been proposed via incorporating domain knowledge
of image steganography/steganalysis into the design of the network and achieve
state of the art performance on standard database. Following such direction, we
propose a novel model called Cover Image Suppression Network (CIS-Net), which
improves the performance of spatial image steganalysis by suppressing cover
image content as much as possible in model learning. Two novel layers, the
Single-value Truncation Layer (STL) and Sub-linear Pooling Layer (SPL), are
proposed in this work. Specifically, STL truncates input values into a same
threshold when they are out of a predefined interval. Theoretically, we have
proved that STL can reduce the variance of input feature map without
deteriorating useful information. For SPL, it utilizes sub-linear power
function to suppress large valued elements introduced by cover image contents
and aggregates weak embedded signals via average pooling. Extensive experiments
demonstrate the proposed network equipped with STL and SPL achieves better
performance than rich model classifiers and existing CNN models on challenging
steganographic algorithms.
