Duplication forgery detection using improved DAISY descriptor

Copy-move is one of the simple and effective operations to create digital image forgeries due to the gradually evolved image processing tools. In recent years, SIFT-based approach is widely applied to detect copy-move. Although these methods are proved to have robust performance in this field, when the cloned region is of uniform texture, this kind of methods normally failed to detect such forgeries due to insufficient or even none keypoints located. Thus, in this paper, an effective manner based on adaptive non-maximal suppression and rotation-invariant DAISY descriptor is proposed, and which enables the capability to detect a cloned region even undergone several geometric changes, such as rotation, scaling, JPEG compression, and Gaussian noise. Extensive experimental results are presented to confirm that the technique is effective to identify the altered area.

► Non-maximal suppression and rotation-invariant DAISY descriptor is proposed.
► Proposed method can detect a cloned region even undergone several geometric changes.
► Extensive experimental results confirm that the technique is effective to identify the altered area.