Universal Stego Post-processing for Enhancing Image Steganography

TL;DR

This paper introduces a novel post-processing framework that directly modifies stego images to reduce artifacts and improve security, applicable to various steganographic methods in spatial and JPEG domains.

Contribution

It proposes a new post-processing approach based on non-linear integer programming to enhance steganography security by minimizing residual differences.

Findings

Effectively improves security against steganalytic detection

Applicable to both spatial and JPEG domain steganography

Enhances security of modern steganographic methods

Abstract

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.