Copy move attack, a special type of image forgery, is performed by copying a part of the image and pasting anywhere else in the same image. Besides block-based methods, keypoint-based methods like Scale Invariant Feature Transform (SIFT) are improved for detection of copy move attacks. In this method, firstly image keypoints are extracted and a 128 dimensional feature vector named as SIFT descriptor is generated for each keypoint. Then, these keypoints are matched using Euclidean distance among their descriptors. Although this method is good at detection of copy move attacks, it has drawback. Computational complexity is huge and increases with the size of the image. To overcome this drawback, we propose to use k-means++ method for clustering the SIFT descriptors. Thus, each keypoint is matched with keypoints only in its cluster instead of all other keypoints. This proposed hybrid method allows us to decrease the time complexity of the SIFT method considerably.