NDLI: Keypoints based enhanced multiple copy-move forgeries detection system using density-based spatial clustering of application with noise clustering algorithm

Content Provider	IET Digital Library
Author	Soni, Badal Das, Pradip K. Thounaojam, Dalton Meitei
Abstract	In this study, the problem of detecting if an image has tampered is inquired; especially, the attention has been paid to the case in which the portion of an image is copied and then pasted onto another region to create a duplication or to hide some important portion of the image. The proposed copy-move forgery detection system is based on the scale-invariant feature transform (SIFT) features extraction and density-based clustering algorithm. The extracted SIFT features are matched using the generalised two nearest neighbours (2NN) procedure. Thereafter, the density-based clustering algorithm is utilised to improve the detection results. The proposed system is tested using MICC-F220, MICC-F2000 and MICC-F8multi datasets. Due to the generalised 2NN matching procedure, the proposed system is able to detect multiple forgeries present in the image. Experimental results show that the performance of the system is quite satisfactory in terms of computational time as well as detection accuracy.
Starting Page	2092
Ending Page	2099
Page Count	8
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 11, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5576
Journal	IET Image Processing
Publisher Date	2018-07-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Counterfeit Goods Data Security Density-based Spatial Clustering Feature Extraction Generalised 2NN Matching Procedure Image Matching Image Recognition Integral Transforms KeyPoint Based Enhanced Multiple Copy-move Forgeries Detection System MICC-F2000 Dataset MICC-F220 Dataset MICC-F8multi Dataset Noise Clustering Algorithm Object Detection Pattern Clustering Scale-invariant Feature Transform Feature Extraction Security of Data SIFT Feature Extraction Transform
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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