Copy-Move Forgery Detection by an Optimal Keypoint on SIFT (OKSIFT) Method

Copy-Move is a technique widely used in digital image tampering, meaning Copy Move Forgery Detection (CMFD) is still significant research. This paper proposes an optimal keypoint in SIFT (OKSIFT). The OKSIFT method produces images of different sizes and different sigma’s. Then with the help of the Gaussian difference (DoG) method, the maximum and minimum keypoints are calculated. When selecting the optimal keypoints, the absolute value of the second sentence will be used instead of using the Taylor expansion binomial series. First, the keypoints lose their dependence on the blurred regions, and secondly, more keypoints appear at the main edges. In the localization process of the region, considering the cases of multiple copies, method g2NN has been used to compare the keypoints. This method reduces the complexity of keypoint calculations and gives a better answer. Experimental results based on precision, recall, and F1 criteria show that the proposed method, with good robustness, works better than some advanced methods.