Linking Intrinsic Difficulty and Regret ...
Document type :
Communication dans un congrès avec actes
DOI :
Title :
Linking Intrinsic Difficulty and Regret to Properties of Multivariate Gaussians in Image Steganalysis
Author(s) :
Mallet, Antoine [Auteur]
Université de Technologie de Troyes [UTT]
Cogranne, Rémi [Auteur]
Université de Technologie de Troyes [UTT]
Bas, Patrick [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Technologie de Troyes [UTT]
Cogranne, Rémi [Auteur]
Université de Technologie de Troyes [UTT]
Bas, Patrick [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Conference title :
12th ACM Workshop on Information Hiding and Multimedia Security (ACM IH&MMSEC'24)
City :
Baiona
Country :
Espagne
Start date of the conference :
2024-06-24
Book title :
Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security (IHMMSec ’24)
Publisher :
ACM
Publication date :
2024
English keyword(s) :
steganalysis
cover-source mismatch
image processing pipeline
fingerprint
Forensics
cover-source mismatch
image processing pipeline
fingerprint
Forensics
HAL domain(s) :
Informatique [cs]/Multimédia [cs.MM]
English abstract : [en]
This paper deals with the Cover-Source Mismatch (CSM) problem faced in operational steganalysis. Based on a multivariate Gaussian model of the distribution of the noise contained in natural images, it provides proxies for ...
Show more >This paper deals with the Cover-Source Mismatch (CSM) problem faced in operational steganalysis. Based on a multivariate Gaussian model of the distribution of the noise contained in natural images, it provides proxies for the two important empirical measures of CSM: intrinsic difficulty and regret. The former can be modeled with the determinant of the covariance matrix of the noise present in an image. The latter can be predicted with a modified Kullback-Leibler divergence between the distribution of the noises of images coming from different cover-sources. We first recall the reasoning behind the multivariate Gaussian model of the noise, and detail how to compute the statistic of the distribution of the noise. Then, our proposed models are compared to empirical data with a specifically designed cover-source generation process. For both quantities, very high correlation coefficients between the model and the observations are obtained. Finally, realistic cover-sources are used to further illustrate the relevance of our model.Show less >
Show more >This paper deals with the Cover-Source Mismatch (CSM) problem faced in operational steganalysis. Based on a multivariate Gaussian model of the distribution of the noise contained in natural images, it provides proxies for the two important empirical measures of CSM: intrinsic difficulty and regret. The former can be modeled with the determinant of the covariance matrix of the noise present in an image. The latter can be predicted with a modified Kullback-Leibler divergence between the distribution of the noises of images coming from different cover-sources. We first recall the reasoning behind the multivariate Gaussian model of the noise, and detail how to compute the statistic of the distribution of the noise. Then, our proposed models are compared to empirical data with a specifically designed cover-source generation process. For both quantities, very high correlation coefficients between the model and the observations are obtained. Finally, realistic cover-sources are used to further illustrate the relevance of our model.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :
Files
- document
- Open access
- Access the document
- main_copy.pdf
- Open access
- Access the document