Content Adaptive Steganography by Minimizing Statistical Detectability
Abstract of Adaptive Steganography by Minimizing Statistical
Content Adaptive Steganography by Minimizing Statistical Detectability,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 least significant bit matching but, at the same time, complex enough to capture the non-stationary character of natural images.
Conclusions
Content Adaptive Steganography by Minimizing Statistical Detectability,Model based steganography has been around for almost fifteen years since the introduction of OutGuess. What makes our approach different is the dimensionality of the parameter space, which allows us to capture the non-stationary character of images, as well as the fact that we do not attempt to preserve the model but rather minimize the impact of embedding. We model the image noise residual as a sequence of independent quantized Gaussian variables with varying variances. By working with the residual, besides the acquisition noise we managed to include in the model the content-dependent modeling error, which has a strong effect on steganalysis.
