Side-Informed Steganography for JPEG Images by Modeling Decompressed Images

TL;DR

This paper introduces a novel JPEG steganography method that models the covariance between rounding errors and embedding changes, leading to state-of-the-art detection resistance in decompressed images.

Contribution

It presents the first analytical modeling of rounding error covariance in JPEG steganography, improving embedding security and detection resistance.

Findings

Achieves state-of-the-art performance against deep learning detectors.

Demonstrates improved results with a new pixel variance estimator at Quality Factor 100.

Models steganography as variance changes in DCT coefficients.

Abstract

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.