Authors
Vahid Sedighi, Rémi Cogranne, Jessica Fridrich,
Title
Content-Adaptive Steganography by Minimizing Statistical Detectability
In
IEEE transactions on Information Forensics and Security
Volume
11
Issue
2
Pages
221 – 234
Publisher
IEEE
Year
2016
Publisher's URL
http://dx.doi.org/10.1109/TIFS.2015.2486744
Indexed by
Abstract
Most current steganographic schemes embed the secret payload by minimizing a heuristically defined distortion. Similarly, their security is evaluated empirically using classifiers equipped with rich image models. In this paper, we pursue an alternative approach based on a locally-estimated multivariate Gaussian cover image model that is sufficiently simple to derive a closed-form expression for the power of the most powerful detector of content-adaptive LSB matching but, at the same time, complex enough to capture the non-stationary character of natural images. We show that when the cover model estimator is properly chosen, state-of-the-art performance can be obtained. The closed-form expression for detectability within the chosen model is used to obtain new fundamental insight regarding the performance limits of empirical steganalysis detectors built as classifiers. In particular, we consider a novel detectability-limited sender and estimate the secure payload of individual images.
Affiliations
Offprint