Improving SURF Based Copy-Move Forgery Detection Using Super Resolution

In recent years, keypoints-based features, such as speeded up robust features (SURF), capture the attention of researchers in the area of image copy-move forgery detection. They are fast to compute and are quite robust to most inter-processing and post-processing operations. However, they will fail to detect the forgery if not enough keypoints are detected, and that is the case when the forgery size is small. A small size forgery is possible if the object is far away from the camera, or if the image is scaled down as a post-processing step. In this paper, our focus is on detecting forgeries of small size using an improved SURF based copy-move forgery detection (CMFD) method. Our method improves keypoints detection by preprocessing the image using a single image super resolution (SISR) algorithm. The proposed approach has been evaluated and compared against original SURF through a comprehensive set of experiments using a dataset of small size forgeries. Experimental results indicate that our method outperforms SURF especially when the forgery size is small.