A self-adaptive tampering detection algorithm based on image segmentation and feature point matching

Abstract

In order to enhance the efficiency and accuracy of homologous tampering detection, image segmentation algorithms and image feature points are combined. The Simple Linear Iterative Cluster (SLIC) algorithm is employed for image segmentation. However, manually presetting the number of patches is not applicable to all images and can influence subsequent segmentation results. To achieve a more accurate detection of tampered areas, this paper proposes a selfadaptive image tampering detection algorithm. The number of image segments is determined based on image complexity, which allows the image to be segmented into semantically independent patches. Subsequently, the SIFT algorithm is employed to extract feature points for matching. Test results demonstrate that the proposed algorithm accurately localizes tampered regions and reduces algorithmic complexity.