针对LSB 匹配的嵌入机制，提出了一种基于直方图局部平滑度的隐写分析方法. 通过邻域差分像素值选取图像的平坦区域，提取包括灰度直方图平滑度、共生矩阵、差分直方图、差分共生矩阵的局部平滑度和峰值等在内的15 维特征，并引入二次嵌入过程消除图像内容差异带来的影响，结合Fisher 线性分类器实现隐写检测. 实验
结果表明，文中算法对于LSB匹配的检测具有较高的准确度，对于NRCS和UCID图像库均表现了良好的检测性能.

 Spatial LSB matching steganography makes the histogram of the embedded images smoother.Based on this, a new steganalytic method that can exploit smoothness changes is proposed. Smooth areas of a given image are chosen based on the value of pixel differences. A total of 15 features include smoothness of image histogram and local co-occurrence matrix, local smoothness and extreme value of pixel difference histograms and co-occurrence differences matrixes, etc., are extracted. The twice embedded process is used to eliminate the influence of different image contents. The Fisher linear discriminator is applied for classification. Results of experiments on the NRCS and UCID databases show that the proposed method has good performance in detecting LSB matching steganography from grayscale images.