针对现有图像盲取证方法在多重镜像篡改检测效果较差的问题,提出一种基于近似最近邻(ANN)搜索的图像篡改检测方法。提取图像的BRISK(Binary Robust Invariant Scalable Keypoints)特征描述子,获得图像的二值特征向量。利用PatchMatch计算特征间的偏移量并借助传导策略优化搜索相似图像块,实现篡改区域的初步检测。利用最小均方线性模型计算拟合误差移除误匹配点,精确定位篡改区域。在CASIA V2.0图像数据集和哥伦比亚大学图像数据集上进行实验,实验结果表明,该算法能够准确且高效地检测经复杂几何形变的篡改区域,特别是对多重镜像篡改检测的准确率更高。

Owing to the poor performance of the existing blind image forensics method in multiple mirror tamper, we propose an image tampering detection algorithm based on approximate nearest neighbor (ANN) search in this study. The binary robust invariant scalable keypoints (BRISK) feature descriptor is extracted to obtain a binary feature vector of an image. The PatchMatch is used to calculate the offset and optimize the search for similar image blocks through conduction strategy, which can achieve the preliminary detection results of tampering region. The least mean square linear model is used to calculate the fitting error, which can eliminate the mismatched points and accurately locate the tampering area. Experiments are performed on CASIA V2.0 and Columbia University datasets, and the results show that the proposed algorithm can accurately and efficiently detect the tampering region with complex geometric deformations, proving to be more accurate in multiple-mirror tampering.