The idea of believing photographs to be true seems unreliable nowadays due to the availability of advanced image processing software. The proposed method investigates detecting copy-move forgeries. Firstly, the SIFT algorithm is applied to detecting keypoints in test images and keypoints are extracted as SIFT feature vectors. Secondly, in view of the complexity of computation, the PCA algorithm is used to reduce the dimension of SIFT feature vectors. Thirdly, a matching procedure is implemented in feature space of keypoints. Lastly, an agglomerative hierarchical clustering is performed on spatial location of matched keypoints to reduce the mismatched points. In the identification, conditions about the spatial distribution of keypoints are set to distinguish whether a test image is authentic. After the identification, the estimation of geometric transformation is carried out on tampered images through LMedS algorithm. Experiment and analysis show that the method is appropriate for the identification and estimation of copy-move forgery and can achieve a higher accuracy than existing methods with less dimension feature vectors.
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