Titre :

Effects and Solutions of Cover-Source Mismatch in Image Steganalysis

Auteur(s) :

Giboulot, Quentin [Auteur]
Laboratoire Modélisation et Sûreté des Systèmes [LM2S]
Cogranne, Rémi [Auteur]
Laboratoire Modélisation et Sûreté des Systèmes [LM2S]
Borghys, Dirk [Auteur]
Ecole Royale Militaire / Koninklijke Militaire School [ERM KMS]
Bas, Patrick [Auteur]
Centre National de la Recherche Scientifique [CNRS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Titre de la revue :

Signal Processing: Image Communication
86

Éditeur :

Elsevier

Date de publication :

2020-08

ISSN :

0923-5965

Mot(s)-clé(s) en anglais :

Steganography
Steganalysis
Cover-Source Mismatch
Image processing
Image Heterogeneity

Discipline(s) HAL :

Informatique [cs]/Cryptographie et sécurité [cs.CR]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Applications [stat.AP]

Résumé en anglais : [en]

The Cover-Source Mismatch (CSM) has been long recognized as a major problem in modern steganography and steganalysis. Indeed, while a vast majority of works in steganography and steganalysis had been tailored to a specific ...
Lire la suite >The Cover-Source Mismatch (CSM) has been long recognized as a major problem in modern steganography and steganalysis. Indeed, while a vast majority of works in steganography and steganalysis had been tailored to a specific reference database, namely BOSSbase, recent works show that, because of CSM, the results may greatly differ when changing this dataset. Although the CSM has already been the subject of several publications, these prior works investigated only a few elements in a limited setup. The goal of the current paper is to study the effects of the CSM in a more comprehensive manner and then to examine and compare different strategies for mitigating it. It first defines two different parameters, the source difficulty and the source inconsistency, which are involved in the CSM. Then, using different steganographic schemes and feature sets, it aims at providing a systematic study regarding the various factors that can give birth to CSM for image steganalysis. Finally, two practical ways to mitigate the CSM, using training techniques promoting either diversity of different sources or the specificity of one targeted source which is beforehand identified by training a multi-class classifier, are presented and their performances are compared for different training set sizes.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Projet ANR :

Outils pour la détection de manipulation d'images numériques.
Utilisation de grandes bases d'images hétérogènes en stéganalyse pour se rapprocher d'un contexte opérationnel

Collections :

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

Source :

Harvested from HAL