Copy move forgery detection with similar but genuine objects

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.