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.

众所周知，设计或改善嵌入成本成为当前隐写方法的关键问题。与现有作品不同，我们提出了一种新颖的框架，通过对隐身的嵌入单元（即像素值和DCT系数）直接进行后处理来增强隐身术的安全性。在本文中，我们首先分析了STC（Syndrome-Trellis码）的特征，然后设计了后处理规则，以确保正确提取隐藏消息。由于隐写术伪影通常会反映在图像残差上，因此我们尝试减小封面和改良的隐身术之间的残留距离，以增强隐写术的安全性。为此，我们将后处理建模为非线性整数规划，并通过启发式搜索将其实现。此外，我们仔细确定了拟议的后处理中的几个重要问题，例如要修改的候选嵌入单元，后修改的方向和幅度，获取残差的自适应滤波器以及残差的距离度量。在手工隐写分析功能和基于深度学习的功能上进行的大量实验结果表明，该方法可以有效地增强大多数现代隐写方法在空间和JPEG域中的安全性。