Open AccessEnhanced copy–paste forgery detection in digital images using scale‐invariant feature transform
Author(s) -
Selvaraj Priyadharsini,
Karuppiah Muneeswaran
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0842
Subject(s) - scale invariant feature transform , artificial intelligence , pixel , computer science , pattern recognition (psychology) , computer vision , cluster analysis , digital image , invariant (physics) , hierarchical clustering , feature (linguistics) , transformation (genetics) , image (mathematics) , mathematics , image processing , linguistics , philosophy , mathematical physics , biochemistry , chemistry , gene
Image forgery detection and localisation is one of the principal problems in digital forensics. Copy–paste forgery in digital images is a type of forgery in which an image region is copied and pasted at another location within the same image. In this work, the authors propose a methodology to detect and localise copy‐pasted regions in images based on scale‐invariant feature transform (SIFT). Existing copy‐paste forgery detection in images using SIFT and clustering techniques such as hierarchical agglomerative and density‐based spatial clustering of applications with noise resulted many false pixel detections. They have introduced sensitivity‐based clustering along with SIFT features to identify copy–pasted pixels and disregard the false pixels. Experimental evaluation on public image datasets MICC‐F220, MICC‐F2000 and MICC‐F8 multi shows that the proposed method is showing improved performance in detecting and localising copy‐paste forgeries in images than the existing works. Also the proposed work detects multiple copy–pasted regions in the images and is robust to attacks such as geometrical transformation of copied regions such as scaling and rotation.