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Blind image forensics aims to assess image authenticity. One of the most popular families of methods from this category is the detection of copy-move forgeries. In the past years, more than 15 different algorithms have been proposed for copy-move forgery detection. So far, the efficacy of these approaches has barely been examined. In this paper, we: a) present a common pipeline for copy-move forgery detection, b) perform a comparative study on 10 proposed copy-move features and c) introduce a new benchmark database for copy-move forgery detection. Experiments show that the recently proposed Fourier-Mellin features perform outstandingly if no geometric transformations are applied to the copied region. Furthermore, our experiments strongly support the use of kd-trees for the matching of similar blocks instead of lexicographic sorting.