Improved Block-based Technique using SURF and FAST Keypoints Matching for Copy-Move Attack Detection

Due to the advancement of image manipulation tool or techniques, the copy-move attack detection from digital images has become the challenging and active research area. This paper proposes an improved block-based technique for copy-move attack detection using Speeded Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) keypoint matching. In the first phase of this technique, the image is divided into non-overlapping blocks and SURF descriptors are extracted from each block. These descriptors are matched using 2NN procedure and match blocks are identified. In the second phase, large blocks are constituted by concatenating the neighboring blocks of each matching block. Thereafter, from each large block FAST features points are extracted and matched using 2NN. Finally, the affine transform is applied to remove the outliers if any. The proposed technique is tested using MICC-F220 and MICC-F2000 standard datasets and it yields better performance in comparison with state of the art techniques.