一种针对LSB匹配隐写的负载定位新算法

针对最不重要比特位(Least significant bit, LSB)匹配隐写算法，本文提出了一种新的负载定位算法。将隐写负载定位看作二分类问题，将载密图像每个像素位置看作待分类样本，通过提取载密图像集中每个像素位置在8个方向上的相邻像素差分平方均值特征，利用支持向量机(Support vector machine,SVM)分类器，将每个像素位置划分到正确的类别——负载位置或非负载位置。本文从理论和实验两方面验证了所提分类特征的有效性。针对LSB匹配隐写，本文方法与最大后验概率(Maximum a posteriori, MAP)载体估计方法做出比较，在低嵌入率条件下，本文方法的定位性能有明显提高。

New Method for Payload Location Aimed at LSB Matching

To locate payloads for the least significant bit matching (LSB-M) steganography, the paper proposes a new method. The problem of payload location for LSB-M can be solved by abstracting the mean square adjacency pixel difference feature of every pixel to classify all the pixels into two parts: payload or non-payload. The feature is proved effective both theoretically and experimentally. Furthermore, the proposed method is compared with the maximum a posteriori estimator for payload location aimed at LSB-M. When the embedding rate is low, the method performs much better than the maximum a posteriori estimator.