Digital images can be edited with the help of photo editing tools to improve or enhance the image quality. On the other hand, digital images can also be subject to manipulations which can alter the visual information being conveyed by the image. The forged images can also be used to spread false information through various media platforms and in some cases may be surreptitiously used as false evidence in a court of law. Therefore, it is crucial to test the authenticity of such images and ensure that it does not spread falsified information. One of the most common types of forgery being used today is copy-move forgery in which one part of the image is copied and placed over another part of the same image in order to either conceal certain details or multiply certain features seen in the original image. This work introduces a method of detecting copy-move forgery in digital images using speeded-up robust features (SURF) to extract keypoints from the image and then uses k-nearest neighbor (kNN) training and mapping to yield accurate matches. The SURF algorithm is capable of performing equally or even exceed the more widely accepted SIFT-based counterparts in terms of ensuring distinctive features, reproducibility, and robustness. As a result, this technique ensures a robust detection of copy move forgery while ensuring lower computational costs compared to the SIFT-based techniques used for the same purpose.