Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Journal articles

Multivariate Side-Informed Gaussian Embedding Minimizing Statistical Detectability

Abstract : Steganography schemes based on a deflection criterion for embedding posses a clear advantage against schemes based on heuristics as they provide a direct link between theoretical detectability and empirical performance. However, this advantage depends on the accuracy of the cover and stego model underlying the embedding scheme. In this work we propose an original steganography scheme based on a realistic model of sensor noise, taking into account the camera model, the ISO setting and the processing pipeline. Exploiting this statistical model allows us to take correlations between DCT coefficients into account. Several types of dependency models are presented, including a very general lattice model which accurately models dependencies introduced by a large class of processing pipelines of interest. We show in particular that the stego signal which minimizes the KL divergence under this model has a covariance proportional to the cover noise covariance. The resulting embedding scheme achieves state-of-the-art performances which go well beyond the current standards in side-informed JPEG steganography.
Complete list of metadata

https://hal-utt.archives-ouvertes.fr/hal-03663628
Contributor : Rémi Cogranne Connect in order to contact the contributor
Submitted on : Tuesday, May 10, 2022 - 12:09:00 PM
Last modification on : Saturday, July 2, 2022 - 3:46:36 AM

File

TIFS_final.pdf
Files produced by the author(s)

Identifiers

Citation

Quentin Giboulot, Patrick Bas, Rémi Cogranne. Multivariate Side-Informed Gaussian Embedding Minimizing Statistical Detectability. IEEE Transactions on Information Forensics and Security, Institute of Electrical and Electronics Engineers, In press, 17, pp.1841 - 1854. ⟨10.1109/TIFS.2022.3173184⟩. ⟨hal-03663628⟩

Share

Metrics

Record views

9

Files downloads

11