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.