Rotation Invariant Local Binary Pattern for Blind Detection of Copy-Move Forgery with Affine Transform

Abstract

For copy-move forgery, the copied region may be rotated or flipped to fit the scene better. A blind image forensics approach is proposed for copy-move forgery detection using rotation invariant uniform local binary patterns (\(LBP_{P, R}^{riu2}\)). The image is first filtered and divided into overlapped blocks with fixed size. The features are extracted from each block using \(LBP_{P, R}^{riu2}\). Then, the feature vectors are sorted and block pairs are identified by estimating the Euclidean distances of these feature vectors. Specifically, a shift-vector counter C is exploited to detect and locate tampering region. Experimental results show that the proposed approach can deal with multiple copy-move forgeries, and is robust to JPEG compression, noise, blurring region rotation and flipping.