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. However, under scaling and rotation PCA and DCT features are the most robust choices. Furthermore, our experiments show a strong support for choosing kd-trees for the matching of similar blocks instead of lexicographic sorting.
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