Geometric transformation invariant block based copy-move forgery detection using fast and efficient hybrid local features

Abstract Copy-move forgery in images is a popular tampering method, in which a portion of an image is copied and pasted on some other location of the same image. This paper proposes an enhancement of block based copy-move forgery detection using hybrid local features extraction. In this system, the image is divided into non-overlapping blocks and SURF features are computed from each block. SURF features are matched using 2NN procedure and large blocks are formed by considering the eight neighboring blocks of each SURF features match block. Maximally Stable Extremal Regions are detected from each large region and the extracted SURF descriptors from these regions are compared for matching. Finally, affine transform is applied to remove the outliers. The proposed system is experimented using MICC-F220, MICC-F2000 and MICC-F600 benchmark datasets and it yields better performance in comparison with state of the art techniques.

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