Publications

In Advanced information networking and applications (AINA))

Abstract The main approaches/implementations used to counteract phishing attacks involve the use of crowd-sourced blacklists. However, blacklists come with several drawbacks. In this paper, we present a comprehensive approach for the detection of phishing attacks. Our approach uses our own detection engine which relies on Graph Neural Networks to leverage the hyperlink structure of the websites to analyze. Additionally, we offer a turnkey implementation to the end-users in the form of a Mozilla Firefox plugin.

Automatic vectorization of historical maps: A benchmark

By Yizi Chen, Joseph Chazalon, Edwin Carlinet, Minh Ôn Vũ Ngọc, Clément Mallet, Julien Perret

2024-04-01

In PLOS ONE

Abstract Shape vectorization is a key stage of the digitization of large-scale historical maps, especially city maps that exhibit complex and valuable details. Having access to digitized buildings, building blocks, street networks and other geographic content opens numerous new approaches for historical studies such as change tracking, morphological analysis and density estimations. In the context of the digitization of Paris atlases created in the 19th and early 20th centuries, we have designed a supervised pipeline that reliably extract closed shapes from historical maps.

Unsupervised discovery of interpretable visual concepts

By Caroline Mazini-Rodrigues, Nicolas Boutry, Laurent Najman

2024-01-14

In Information Sciences

Abstract Providing interpretability of deep-learning models to non-experts, while fundamental for a responsible real-world usage, is challenging. Attribution maps from xAI techniques, such as Integrated Gradients, are a typical example of a visualization technique containing a high level of information, but with difficult interpretation. In this paper, we propose two methods, Maximum Activation Groups Extraction (MAGE) and Multiscale Interpretable Visualization (Ms-IV), to explain the model’s decision, enhancing global interpretability. MAGE finds, for a given CNN, combinations of features which, globally, form a semantic meaning, that we call concepts.

Informatique mathématique. Une photographie en 2023

By Élodie Puybareau, Samy Blusseau

2023-12-04

In

Abstract

A CP-based automatic tool for instantiating truncated differential characteristics

By François Delobel, Patrick Derbez, Arthur Gontier, Loïc Rouquette, Christine Solnon

2023-12-01

In Progress in cryptology – INDOCRYPT 2023

Abstract An important criteria to assert the security of a cryptographic primitive is its resistance against differential cryptanalysis. For word-oriented primitives, a common technique to determine the number of rounds required to ensure the immunity against differential distinguishers is to consider truncated differential characteristics and to count the number of active S-boxes. Doing so allows to provide an upper bound on the probability of the best differential characteristic with a reduced computational cost.

Closure and decision properties for higher-dimensional automata

By Amazigh Amrane, Hugo Bazille, Uli Fahrenberg, Krzysztof Ziemiański

2023-12-01

In 20th international colloquium on theoretical aspects of computing (ICTAC’23)

Abstract

An improved spectral extraction method for JWST/NIRSpec fixed slit observations

By Laurence Denneulin, A. Guilbert-Lepoutre, M. Langlois, S. Thé, E. Thiébaut, B. J. Holler, P. Ferruit

2023-11-15

In Astronomy & Astrophysics

Abstract The James Webb Space Telescope is performing beyond our expectations. Its Near Infrared Spectrograph (NIRSpec) provides versatile spectroscopic capabilities in the 0.6-5.3 micrometre wavelength range, where a new window is opening for studying Trans-Neptunian objects in particular. We propose a spectral extraction method for NIRSpec fixed slit observations, with the aim of meeting the superior performance on the instrument with the most advanced data processing. We applied this method on the fixed slit dataset of the guaranteed-time observation program 1231, which targets Plutino 2003 AZ84.

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

By Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost, Hieab Adams, David Atkinson, Frederik Barkhof, Mahlet A. Birhanu, Esther E. Bron, Robin Camarasa, Nish Chaturvedi, Yuan Chen, Zihao Chen, Shuai Chen, Qi Dou, Tavia Evans, Ivan Ezhov, Haojun Gao, Marta Girones Sanguesa, Juan Domingo Gispert, Beatriz Gomez Anson, Alun D. Hughes, M. Arfan Ikram, Silvia Ingala, H. Rolf Jaeger, Florian Kofler, Hugo J. Kuijf, Denis Kutnar, Minho Lee, Bo Li, Luigi Lorenzini, Bjoern Menze, Jose Luis Molinuevo, Yiwei Pan, Élodie Puybareau, Rafael Rehwald, Ruisheng Su, Pengcheng Shi, Lorna Smith, Therese Tillin, Guillaume Tochon, Hélène Urien, Bas H. M. Velden, Isabelle F. Velpen, Benedikt Wiestler, Frank J. Wolters, Pinar Yilmaz, Marius Groot, Meike W. Vernooij, Marleen Bruijne

2023-11-13

In Medical Image Analysis

Abstract Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021.

Bridging human concepts and computer vision for explainable face verification

By Miriam Doh, Caroline Mazini-Rodrigues, Nicolas Boutry, Laurent Najman, Mancas Matei, Hugues Bersini

2023-10-10

In 2nd international workshop on emerging ethical aspects of AI (BEWARE-23)

Abstract With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairness, and accountability of decisions. Although Explainable Artificial Intelligence (XAI) techniques exist to clarify AI decisions, it is equally important to provide interpretability of these decisions to humans. In this paper, we present an approach to combine computer and human vision to increase the explanation’s interpretability of a face verification algorithm.

Refinement of a ligand activity and representation of topological phamacophores in a colored network

By Maroua Lejmi, Damien Geslin, Bertrand Cuissart, Ilef Ben Slima, Nida Meddouri, Ronan Bureau, Alban Lepailleur, Amel Borgi, Jean-Luc Lamotte

2023-10-01

In Proceedings of the 11èmes journées de la société française de chémoinformatique

Abstract Structure-Activity Relationships is a critical aspect of drug design. It enables us to examine ligand interactions and performances towards specific targets, then to design effective drugs for treating diseases or improving existing medical therapies. In this context, we specifically study the activity of ligands towards kinases using the BCR-ABL dataset. The work is dedicated to introduce a refinement method for the activity of molecules. Instead of considering anity as a binary activity, a molecule being either active or inactive, the compounds were partitioned into 4 classes according to their activity: very active, moderately active, slightly active, inactive.

An end-to-end approach for the detection of phishing attacks

Automatic vectorization of historical maps: A benchmark

Unsupervised discovery of interpretable visual concepts

Informatique mathématique. Une photographie en 2023

A CP-based automatic tool for instantiating truncated differential characteristics

Closure and decision properties for higher-dimensional automata

An improved spectral extraction method for JWST/NIRSpec fixed slit observations

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Bridging human concepts and computer vision for explainable face verification

Refinement of a ligand activity and representation of topological phamacophores in a colored network

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