Copy-move forgery is one of the most used manipulations for tampering with digital images. The authenticity of the image becomes more crucial when the images are used in important processes. keypoints-based algorithms have been reported to be very effective in revealing copy-move evidence due to their robustness against various attacks. However, these approaches sometimes fail to make good prediction because of different factors such small number of keypoints detected, or wrongly detected keypoints. Matching the correct keypoints and filtering the wrong keypoints are other difficult tasks. One reason behind these issues is the parameters used to configure the key point detection algorithm. In this paper, another CMF (copy-move forgery) detection algorithm is proposed, by applying particle swarm optimization to find the best parameters for the algorithm for all different phases. Furthermore, filtering is achieved through two stages to remove most of the wrong keypoints detected. Additionally, triangulation is used as another technique applied to the algorithm in order to increase the detection area. Experimental results shows that the algorithm has good performance.
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