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.Comment: 13 pages, 7 figures, 1 table, submitted to IEEE Transactions on
Information Forensics & Securit