Copy move forgery is one of the most common types of image forgery, and it is very important to detect this type of forgery. Feature-based forgery detection methods perform better than block-based methods. In this article, a new feature-based approach is suggested in the copy-move forgery detection process. In the suggested approach, first, the features extraction process is done based on SIFT. Second, matching process is based on the g2NN criteria. Finally, removal mismatches are done based on the suggested improved A-RANSAC that stopping criteria is presented based on the number of final matches. The stop time in the basic A-RANSAC method is based on the number of repetitions, which increases the execution time and decreases its speed. This suggested approach, in addition to proper accuracy, increases speed. The simulation results on MICC-F220 datasets affirm the suggested approach advantage in comparison with some other basic methods in terms of precision matching and execution time.
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