Open Access   Article

Clustering Based Feature Extraction for Image Forgery Detection

Pooja Devi1 , Suman Deswal2

1 Dept. C.S.E, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, India.
2 Dept. C.S.E, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, India.

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 22-27, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.2227

Online published on Jul 31, 2018

Copyright © Pooja Devi, Suman Deswal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: Pooja Devi, Suman Deswal, “Clustering Based Feature Extraction for Image Forgery Detection”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.22-27, 2018.

MLA Style Citation: Pooja Devi, Suman Deswal "Clustering Based Feature Extraction for Image Forgery Detection." International Journal of Computer Sciences and Engineering 6.7 (2018): 22-27.

APA Style Citation: Pooja Devi, Suman Deswal, (2018). Clustering Based Feature Extraction for Image Forgery Detection. International Journal of Computer Sciences and Engineering, 6(7), 22-27.

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Abstract

Today manipulation of digital images has become easy due to powerful computers, advanced photo-editing software packages and high resolution capturing devices. Verifying the integrity of photo without any kind of special watermark or any prior knowledge is a critical issue. Photograph tampering techniques like copy-paste, which is very easy and effective to use, can extend image forging. The original content of the picture is copied to the desired locations. The increasing image modification software can easily manipulate the digital photo without leaving any visible clue. It’s important to study these issues because tempered photographs can cause social chaos, criminal and non-public consequences. It’s very important and additionally tough to discover the digital photograph forgeries. The main focus of this paper is to detect picture replica circulate forgery which is depended on SIFT (scale invariant feature transform) descriptors, which are invariant to rotation, scaling etc. Clustering algorithm is used for clustering of key points in images. Results show that, in comparison of existing methods MROGH, SURF-PHA provides consistent precision, recall and F1 score about 98.86%, 99.40%, and 99.13% respectively for the provided dataset. Experimental results indicate that this method is a robust method in detecting the copy-move forgery quickly and withstands certain transformations.

Key-Words / Index Term

Copy-move Image Forgery, Forgery Detection, Feature Extraction, key-points, SIFT, Clustering

References

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