50 citations · 78 across the 7 of their papers we have counts for
12 papers
Astronomical image time series classification using CONVolutional attENTION (ConvEntion)
Anass Bairouk, Marc Chaumont, Dominique Fouchez +3
Aims. The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or un…
A study on the invariance in security whatever the dimension of images for the steganalysis by deep-learning
Kévin Planolles, Marc Chaumont, Frédéric Comby
In this paper, we study the performance invariance of convolutional neural networks when confronted with variable image sizes in the context of a more "wild steganalysis". First, w…
LSSD: a Controlled Large JPEG Image Database for Deep-Learning-based Steganalysis "into the Wild"
Hugo Ruiz, Mehdi Yedroudj, Marc Chaumont +2
For many years, the image databases used in steganalysis have been relatively small, i.e. about ten thousand images. This limits the diversity of images and thus prevents large-sca…
Analysis of the Scalability of a Deep-Learning Network for Steganography "Into the Wild"
Hugo Ruiz, Marc Chaumont, Mehdi Yedroudj +3
Since the emergence of deep learning and its adoption in steganalysis fields, most of the reference articles kept using small to medium size CNN, and learn them on relatively small…
Steganography using a 3 player game
Mehdi Yedroudj, Frédéric Comby, Marc Chaumont
Image steganography aims to securely embed secret information into cover images. Until now, adaptive embedding algorithms such as S-UNIWARD or Mi-POD, are among the most secure and…
Pooled Steganalysis in JPEG: how to deal with the spreading strategy?
Ahmad Zakaria, Marc Chaumont, Gérard Subsol
In image pooled steganalysis, a steganalyst, Eve, aims to detect if a set of images sent by a steganographer, Alice, to a receiver, Bob, contains a hidden message. We can reasonabl…
Deep Learning in steganography and steganalysis from 2015 to 2018
Marc Chaumont
For almost 10 years, the detection of a hidden message in an image has been mainly carried out by the computation of Rich Models (RM), followed by classification using an Ensemble…
PELICAN: deeP architecturE for the LIght Curve ANalysis
Johanna Pasquet, Jérôme Pasquet, Marc Chaumont +1
We developed a deeP architecturE for the LIght Curve ANalysis (PELICAN) for the characterization and the classification of light curves. It takes light curves as input, without any…
A CNN adapted to time series for the classification of Supernovae
Anthony Brunel, Johanna Pasquet, Jérôme Pasquet +4
Cosmologists are facing the problem of the analysis of a huge quantity of data when observing the sky. The methods used in cosmology are, for the most of them, relying on astrophys…
Yedrouj-Net: An efficient CNN for spatial steganalysis
Mehdi Yedroudj, Frederic Comby, Marc Chaumont
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 suc…
How to augment a small learning set for improving the performances of a CNN-based steganalyzer?
Mehdi Yedroudj, Marc Chaumont, Frédéric Comby
Deep learning and convolutional neural networks (CNN) have been intensively used in many image processing topics during last years. As far as steganalysis is concerned, the use of…
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover source-mismatch
Lionel Pibre, Pasquet Jérôme, Dino Ienco +1
Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image re…