• English
    • français
  • Help
  •  | 
  • Contact
  •  | 
  • About
  •  | 
  • Login
  • HAL portal
  •  | 
  • Pages Pro
  • EN
  •  / 
  • FR
View Item 
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Efficient Steganography in JPEG Images by ...
  • BibTeX
  • CSV
  • Excel
  • RIS

Document type :
Article dans une revue scientifique
DOI :
10.1109/TIFS.2021.3111713
Title :
Efficient Steganography in JPEG Images by Minimizing Performance of Optimal Detector
Author(s) :
Cogranne, Rémi [Auteur]
Laboratoire Modélisation et Sûreté des Systèmes [LM2S]
Giboulot, Quentin [Auteur]
Laboratoire Modélisation et Sûreté des Systèmes [LM2S]
Bas, Patrick [Auteur] refId
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Journal title :
IEEE Transactions on Information Forensics and Security
Pages :
1328 - 1343
Publisher :
Institute of Electrical and Electronics Engineers
Publication date :
2021-09-01
ISSN :
1556-6013
English keyword(s) :
Steganography
steganalysis
JPEG images
hypothesis testing
statistical modeling
HAL domain(s) :
Statistiques [stat]/Méthodologie [stat.ME]
Statistiques [stat]/Applications [stat.AP]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Cryptographie et sécurité [cs.CR]
English abstract : [en]
Since the introduction of adaptive steganography, most of the recent research works seek at designing cost functions that are evaluated against steganalysis methods. While those approaches have been successful, they rely ...
Show more >
Since the introduction of adaptive steganography, most of the recent research works seek at designing cost functions that are evaluated against steganalysis methods. While those approaches have been successful, they rely on intuitive principles and ad-hoc costs associated with each pixel or Discrete Cosine Transform (DCT) coefficient. Beyond the empirical assessments, the insights one can get from such approaches are very limited. On the opposite, this paper presents an original method for steganography in JPEG images that exploits a statistical model of the DCT coefficients. Within the framework of hypothesis testing theory, we use a statistical model of covers to derive the analytical expression of the most powerful detector. The objective of the steganographer is to minimize the statistical performance of this “omniscient detector” which represents a “worst-case” scenario for security. This paper shows how this method allows designing effective steganography, in terms of both security and computational complexity, in the two main use cases: when having only one single JPEG image and when the uncompressed image is available, case also known as Side-Informed (SI). A wide range of numerical comparisons shows that the proposed method outperforms the current state-of-the-art especially against the latest and most accurate steganalysis approaches based on Deep Learning.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Utilisation de grandes bases d'images hétérogènes en stéganalyse pour se rapprocher d'un contexte opérationnel
Outils pour la détection de manipulation d'images numériques.
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
Files
Thumbnail
  • https://hal-utt.archives-ouvertes.fr/hal-03342645/document
  • Open access
  • Access the document
Thumbnail
  • https://hal-utt.archives-ouvertes.fr/hal-03342645/document
  • Open access
  • Access the document
Thumbnail
  • https://hal-utt.archives-ouvertes.fr/hal-03342645/document
  • Open access
  • Access the document
Thumbnail
  • https://hal-utt.archives-ouvertes.fr/hal-03342645/document
  • Open access
  • Access the document
Université de Lille

Mentions légales
Université de Lille © 2017