  It is well known that the designing or improving embedding cost becomes a key
issue for current steganographic methods. Unlike existing works, we propose a
novel framework to enhance the steganography security via post-processing on
the embedding units (i.e., pixel values and DCT coefficients) of stego
directly. In this paper, we firstly analyze the characteristics of STCs
(Syndrome-Trellis Codes), and then design the rule for post-processing to
ensure the correct extraction of hidden message. Since the steganography
artifacts are typically reflected on image residuals, we try to reduce the
residual distance between cover and the modified stego in order to enhance
steganography security. To this end, we model the post-processing as a
non-linear integer programming, and implement it via heuristic search. In
addition, we carefully determine several important issues in the proposed
post-processing, such as the candidate embedding units to be modified, the
direction and amplitude of post-modification, the adaptive filters for getting
residuals, and the distance measure of residuals. Extensive experimental
results evaluated on both hand-crafted steganalytic features and deep learning
based ones demonstrate that the proposed method can effectively enhance the
security of most modern steganographic methods both in spatial and JPEG
domains.
