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Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu

Abstract: 

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.

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Paper Details

Authors:
Frédéric COMBY , Marc CHAUMONT
Submitted On:
12 April 2018 - 11:57am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Yedroudj Mehdi
Paper Code:
IFS-P3.3
Document Year:
2018
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[1] Frédéric COMBY , Marc CHAUMONT, "Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2424. Accessed: Apr. 24, 2019.
@article{2424-18,
url = {http://sigport.org/2424},
author = {Frédéric COMBY ; Marc CHAUMONT },
publisher = {IEEE SigPort},
title = {Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu},
year = {2018} }
TY - EJOUR
T1 - Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu
AU - Frédéric COMBY ; Marc CHAUMONT
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2424
ER -
Frédéric COMBY , Marc CHAUMONT. (2018). Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu. IEEE SigPort. http://sigport.org/2424
Frédéric COMBY , Marc CHAUMONT, 2018. Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu. Available at: http://sigport.org/2424.
Frédéric COMBY , Marc CHAUMONT. (2018). "Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu." Web.
1. Frédéric COMBY , Marc CHAUMONT. Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2424