Practical methods for minimizing embedding impact in steganography 论文

摘要

In this paper, we propose a general framework and practical coding methods for constructing steganographic schemes that minimize the statistical impact of embedding. By associating a cost of an embedding change with every element of the cover, we first derive bounds on the minimum theoretically achievable embedding impact and then propose a framework to achieve it in practice. The method is based on syndrome codes with low-density generator matrices (LDGM). The problem of optimally encoding a message (e.g., with the smallest embedding impact) requires a binary quantizer that performs near the rate-distortion bound. We implement this quantizer using LDGM codes with a survey propagation message-passing algorithm. Since LDGM codes are guaranteed to achieve the rate-distortion bound, the proposed methods are guaranteed to achieve the minimal embedding impact (maximal embedding efficiency). We provide detailed technical description of the method for practitioners and demonstrate its performance on matrix embedding.