A Novel Copy-Move Forgery Detection Algorithm via Feature Label Matching and Hierarchical Segmentation Filtering
作者：
Highlights：
• The improved structure of SIFT can extract as many as possible interesting and effective points in the homogeneous region(s), that enables the ability of CMFD in homogenous regions simultaneously under large-scaling attacks.
• The proposed FLM algorithm makes use of a newly designed feature label and OLC for significantly reduced computation. As a result, matching efficiency is significantly improved.
• The proposed HSF algorithm can effectively filter out suspicious outliers. Furthermore, the fusion of the three-hierarchical filtering segmentations can indicate forgery regions precisely.
摘要
•The improved structure of SIFT can extract as many as possible interesting and effective points in the homogeneous region(s), that enables the ability of CMFD in homogenous regions simultaneously under large-scaling attacks.•The proposed FLM algorithm makes use of a newly designed feature label and OLC for significantly reduced computation. As a result, matching efficiency is significantly improved.•The proposed HSF algorithm can effectively filter out suspicious outliers. Furthermore, the fusion of the three-hierarchical filtering segmentations can indicate forgery regions precisely.
论文关键词：Copy-move forgery detection (CMFD),Improved SIFT structure,Feature Label Matching,Hierarchical Segmentation Filtering
论文评审过程：Received 27 July 2021, Revised 25 September 2021, Accepted 29 September 2021, Available online 11 October 2021, Version of Record 11 October 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102783
