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.