  For about 10 years, detecting the presence of a secret message hidden in an
image was performed with an Ensemble Classifier trained with Rich features. In
recent years, studies such as Xu et al. have indicated that well-designed
convolutional Neural Networks (CNN) can achieve comparable performance to the
two-step machine learning approaches.
  In this paper, we propose a CNN that outperforms the state-ofthe-art in terms
of error probability. The proposition is in the continuity of what has been
recently proposed and it is a clever fusion of important bricks used in various
papers. Among the essential parts of the CNN, one can cite the use of a
pre-processing filterbank and a Truncation activation function, five
convolutional layers with a Batch Normalization associated with a Scale Layer,
as well as the use of a sufficiently sized fully connected section. An
augmented database has also been used to improve the training of the CNN.
  Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding
algorithms and its performances were compared with those of three other
methods: an Ensemble Classifier plus a Rich Model, and two other CNN
steganalyzers.
