activity
20182021
most citedPELICAN: deeP architecturE for the LIght Curve ANalysis

50 citations · 50 across the 3 of their papers we have counts for

collaborators

9 papers

cs.CR2021

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…

cs.CR2020

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…

cs.MM2019

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…

cs.CR2019

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…

cs.CR2019

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…

astro-ph.IM201950 cited

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…