Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Yedrouj-Net: An efficient CNN for spatial steganalysis   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.

16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg