基于分级聚类与SURF特征约束的图像真伪决策算法

Image true-false decision algorithm based on improved SURF coupled hierarchical clustering

当前篡改图像识别算法在对组合篡改图像进行识别时,主要采用对像素或者块进行逐一匹配的方法来检测,导致识别精度不高、鲁棒性差等不足,为此提出改进的SURF耦合分级聚类的图像信息真伪决策算法。采用积分图像模型计算矩形区域像素强度的总和,通过计算Hessian矩阵提取特征点,构建圆形筛除器对SURF进行改进,对特征点数量进行整定,提高算法效率;引入最优节点优先方法 (best bin first method,BBF)对最近邻进行搜索,通过对特征点的特征描述符进行计算,完成特征点匹配;利用分级聚类方法,对特征点进行集群,创建层次树,完成图像的篡改检测。仿真结果表明,与当前图像篡改识别技术相比,所提算法具有更强的鲁棒性以及更高的检测精度。

The current image forgery detection algorithm for complex image forgery detection,which mainly uses one by one comparison method to process the pixel or block for detection,resulting in the low detection accuracy and poor robustness and so on.The image forgery detection algorithm based on improved SURF coupled hierarchical clustering was proposed.The integral image model was used to calculate the sum of the pixel intensity in the rectangular region,the feature points were extracted by calculating the Hessian matrix,and the circular screen was constructed to improve the SURF,achieving the number of feature points to the whole set and improving the efficiency of the algorithm.The nearest neighbor was searched by introducing the best bin first method,and the feature descriptor of the feature points was calculated to complete feature point matching.Hierarchical clustering method was used to cluster the feature points and a hierarchical tree was created and the image forgery detection was completed.Simulation results show that the proposed algorithm has stronger robustness and higher detection accuracy compared with the current forgery detection methods.