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2020 | OriginalPaper | Chapter

5. Copy-Move Forgery Detection with Similar But Genuine Objects

Authors : Aniket Roy, Rahul Dixit, Ruchira Naskar, Rajat Subhra Chakraborty

Published in: Digital Image Forensics

Publisher: Springer Singapore

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Abstract

Copy-Move Forgery Detection (CMFD) is a classic image forensics problem. However, CMFD with Similar but Genuine Objects (SGO) has received relatively less attention in the current literature. Recently, it has been observed that state-of-the-art CMFD techniques are not quite effective in solving this important yet less explored problem variant. We have addressed this specific problem by using Rotated Local Binary Pattern (RLBP) texture features, followed by feature matching by Generalized 2-Nearest Neighborhood (g2NN) technique, agglomerative hierarchical clustering, and geometric transformation estimation through homography computation among the clusters. Experimental results verify that our CMFD framework not only outperforms the state-of-the-art CMFD schemes for tampered images having similar but genuine objects, but also commensurates with the accuracy of state-of-the-art schemes for other copy-move forgery types. The proposed method has also been found to be robust with respect to several post-processing operations, e.g., compression and filtering.

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previous chapter Copy-Move Forgery Detection Exploiting Statistical Image Features
next chapter Copy-Move Forgery Detection in Transform Domain
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Metadata
Title
Copy-Move Forgery Detection with Similar But Genuine Objects
Authors
Aniket Roy
Rahul Dixit
Ruchira Naskar
Rajat Subhra Chakraborty
Copyright Year
2020
Publisher
Springer Singapore
Book
Digital Image Forensics

Print ISBN: 978-981-10-7643-5

Electronic ISBN: 978-981-10-7644-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-10-7644-2_5