NDLI: Enhanced copy–paste forgery detection in digital images using scale-invariant feature transform

Content Provider	IET Digital Library
Author	Selvaraj, Priyadharsini Karuppiah, Muneeswaran
Abstract	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.
Starting Page	462
Ending Page	471
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 3, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0842
Journal	IET Image Processing
Publisher Date	2019-10-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Clustering Technique Computer Vision And Image Processing Technique Copied Regions Copy Protection Copy–pasted Pixels Data Security Density-based Spatial Clustering Digital Forensics Digital Image Enhanced Copy–paste Forgery Detection False Pixel Detections Feature Extraction Hierarchical Agglomerative Clustering Image Forensics Image Forgery Detection Image Forgery Localisation Image Recognition Image Region Integral Transforms MICC-F2000 Dataset MICC-F8 Multidataset Multiple Copy–pasted Region Detection Object Detection Pattern Clustering Public Image Datasets MICC-F Scale-invariant Feature Transform Sensitivity-based Clustering SIFT Feature Transform
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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