Since digital images are one of the most important carriers of information, their authenticity is quite important. There are miscellaneous forgery techniques for manipulating digital images, and one of those is copy-move forgery. Many forgery detection techniques have been developed for detection of copy-move forgery so far. However, the main lack of these techniques is that although they can successfully detect the copied and pasted regions on a copy-move forgery image, they are not able to determine which of the detected regions is the source region and which of them is the destination region. In this study, a novel and standalone technique has been proposed for source-destination discrimination on copy-move forgery images. The proposed technique is based on machine learning and uses Support Vector Machine. Our technique can be regarded as an appendage for the classical copy-move forgery detection algorithms, which cannot make source-destination discrimination. To the best of our knowledge, the proposed technique is the first standalone technique which makes source-destination discrimination on copy-move forgeries, in the literature, and it is the only successful source-destination discrimination technique in the literature.