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Copy move forgery detection with similar but genuine objects

Citation Author(s):
Aniket Roy, Akhil Konda, Rajat Subhra Chakraborty
Submitted by:
Aniket Roy
Last updated:
13 September 2017 - 6:14pm
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Aniket Roy
Paper Code:
2808
Categories:

Abstract

Copy-Move Forgery Detection (CMFD) is a well-studied
image forensics problem. However, CMFD with Similar but
Genuine Objects (SGO) has received relatively less attention.
Recently, it has been found that current state-of-the-art
CFMD techniques are mostly inadequate in satisfactorily
solving this important problem variant. In this paper, we have
addressed this issue by using Rotated Local Binary Pattern
(RLBP) based rotation-invariant texture features, followed
by Generalized Two Nearest Neighbourhood (g2NN) based
feature matching, hierarchical clustering and geometric transformation
estimation. Experimental results show that our
technique outperforms the state-of-the-art CFMD techniques
for forged images having similar but genuine objects, and
matches the accuracy of state-of-the-art techniques for other
copy-move forgery types. Our method is also robust with
respect to filtering and compression based post-processing.

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