中文摘要：

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。

英文摘要：

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.