Publication

Abstract : Image forensics is one of the most active research domains. As technology is advancing, we can add or take out crucial features from a picture without any trail of tampering. Therefore, its authenticity is called into question especially when images have impressive power. Copy-move is a kind of forgery, where portions of a picture are transformed and inserted into the same picture. Copy-move forgery detection is one such research domain that has put forward various methods to find out copy-move forgery. Many techniques based on image processing and machine learning have been put forward to detect the forgery. Since the duplicated parts are from the same image, many of the features will be similar to the rest of the image making it difficult to detect forgery using the latest methods. In this work, we propose to use SIFT keypoint-based forgery detection with clustering for quickly identifying copy-move forgeries in highly textured regions. As the SIFT keypoints are difficult to detect in smooth regions, we propose to use Hu’s invariant-based block-based forgery detection strategy to detect the missing cases. We show that the joint approach outperforms the method reported by Li et al. (IEEE Trans Inf Forensics Secur 10:507–518, 2015) on the popular copy-move forgery detection dataset MICC-600.