Color image-spliced localization based on quaternion principal component analysis and quaternion skewness

摘要

Splicing is a common tampering operation where the splicing area and the original image have different noise levels. In this paper, novel color spliced image localization based on quaternion principal component analysis (QPCA) and quaternion skewness is proposed. Firstly, the proposed algorithm uses QPCA to estimate the block-wise noise level. Then K-means algorithm is used to distinguish the splicing region from the original image. Next, in order to increase inconsistencies of the noise level in image blocks from different images, quaternion skewness is employed to select the overlapping patches of image blocks. In addition, morphological methods are applied to the fine classification results to reduce the false detection rate and miss detection rate along the boundary blocks between the original area and spliced area. The experimental results demonstrate the superiority of the proposed method to state-of-art schemes. (C) 2019 Elsevier Ltd. All rights reserved.