Abstract: A clustering method to detect the copy-move area based on the results of superpixel segmentation is proposed. Different from the traditional K-means clustering method, the proposed clustering method is to place random seeds in the image and segment the region by using super-pixel segmentation method, and then obtain the regions containing pre-matched feature points. In this algorithm, the number of matched feature points in each region is used as a criterion to determine whether the matched feature points in the region are effective feature points. When the number of matching points reaches a certain threshold, the points in the sub-regions are clustered into one group, so that the clustering results are closer to the image content and in accordance with the actual situation. Experiments show that the proposed method based on super-pixel segmentation is more effective than the traditional SIFT method.

Key words: scale invariant feature transform (SIFT), super pixel segmentation, digital image forensics, copy-move forgery