Side-Informed Steganography for JPEG Images by Modeling Decompressed Images
Jan Butora, Patrick Bas

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…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Digital Media Forensic Detection · Chaos-based Image/Signal Encryption
