Titre :

Using Set Covering to Generate Databases for Holistic Steganalysis

Auteur(s) :

Abecidan, Rony [Auteur]
Centrale Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Itier, Vincent [Auteur]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Boulanger, Jérémie [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Bas, Patrick [Auteur]
Centrale Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Pevný, Tomáš [Auteur]
Czech Technical University in Prague [CTU]

Titre de la manifestation scientifique :

IEEE International Workshop on Information Forensics and Security (WIFS 2022)

Ville :

Shanghai

Pays :

Chine

Date de début de la manifestation scientifique :

2022-12-13

Discipline(s) HAL :

Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseau de neurones [cs.NE]
Informatique [cs]/Multimédia [cs.MM]
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]

Résumé en anglais : [en]

Within an operational framework, covers used by a steganographer are likely to come from different sensors and different processing pipelines than the ones used by researchers for training their steganalysis models. Thus, ...
Lire la suite >Within an operational framework, covers used by a steganographer are likely to come from different sensors and different processing pipelines than the ones used by researchers for training their steganalysis models. Thus, a performance gap is unavoidable when it comes to out-of-distributions covers, an extremely frequent scenario called Cover Source Mismatch (CSM). Here, we explore a grid of processing pipelines to study the origins of CSM, to better understand it, and to better tackle it. A set-covering greedy algorithm is used to select representative pipelines minimizing the maximum regret between the representative and the pipelines within the set. Our main contribution is a methodology for generating relevant bases able to tackle operational CSM. Experimental validation highlights that, for a given number of training samples, our set covering selection is a better strategy than selecting random pipelines or using all the available pipelines. Our analysis also shows that parameters as denoising, sharpening, and downsampling are very important to foster diversity. Finally, different benchmarks for classical and wild databases show the good generalization property of the extracted databases. Additional resources are available at github.com/RonyAbecidan/HolisticSteganalysisWithSetCovering.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL