Side-Informed Steganography for JPEG Images by Modeling Decompressed Images - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Information Forensics and Security Année : 2023

Side-Informed Steganography for JPEG Images by Modeling Decompressed Images

Résumé

Side-informed steganography has always been among the most secure approaches in the field. However, a majority of existing methods for JPEG images use the side information, here the rounding error, in a heuristic way. For the first time, we show that the usefulness of the rounding error comes from its covariance with the embedding changes. Unfortunately, this covariance between continuous and discrete variables is not analytically available. An estimate of the covariance is proposed, which allows to model steganography as a change in the variance of DCT coefficients. Since steganalysis today is best performed in the spatial domain, we derive a likelihood ratio test to preserve a model of a decompressed JPEG image. The proposed method then bounds the power of this test by minimizing the Kullback-Leibler divergence between the cover and stego distributions. We experimentally demonstrate in two popular datasets that it achieves state-of-the-art performance against deep learning detectors. Moreover, by considering a different pixel variance estimator for images compressed with Quality Factor 100, even greater improvements are obtained.
Fichier principal
Vignette du fichier
SI_LRT_07.pdf (721.02 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04083163 , version 1 (27-04-2023)

Licence

Copyright (Tous droits réservés)

Identifiants

Citer

Jan Butora, Patrick Bas. Side-Informed Steganography for JPEG Images by Modeling Decompressed Images. IEEE Transactions on Information Forensics and Security, In press, ⟨10.1109/TIFS.2023.3268884⟩. ⟨hal-04083163⟩
31 Consultations
61 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More