  For the past few years, in the race between image steganography and
steganalysis, deep learning has emerged as a very promising alternative to
steganalyzer approaches based on rich image models combined with ensemble
classifiers. A key knowledge of image steganalyzer, which combines relevant
image features and innovative classification procedures, can be deduced by a
deep learning approach called Convolutional Neural Networks (CNN). These kind
of deep learning networks is so well-suited for classification tasks based on
the detection of variations in 2D shapes that it is the state-of-the-art in
many image recognition problems. In this article, we design a CNN-based
steganalyzer for images obtained by applying steganography with a unique
embedding key. This one is quite different from the previous study of {\em Qian
et al.} and its successor, namely {\em Pibre et al.} The proposed architecture
embeds less convolutions, with much larger filters in the final convolutional
layer, and is more general: it is able to deal with larger images and lower
payloads. For the "same embedding key" scenario, our proposal outperforms all
other steganalyzers, in particular the existing CNN-based ones, and defeats
many state-of-the-art image steganography schemes.
