Attacks capable of removing SIFT keypoints from images have been recently devised with the intention of compromising the correct functioning of SIFT-based copy-move forgery detection. To tackle with these attacks, we propose three novel forensic detectors for the identification of images whose SIFT keypoints have been globally or locally removed. The detectors look for inconsistencies like the absence or anomalous distribution of keypoints within textured image regions. We first validate the methods on state-of-the-art keypoint removal techniques, then we further assess their robustness by devising a counter-forensic attack injecting fake SIFT keypoints in the attempt to cover the traces of removal. We apply the detectors to a practical image forensic scenario of SIFT-based copy-move forgery detection, assuming the presence of a counterfeiter who resorts to keypoint removal and injection to create copy–move forgeries that successfully elude SIFT-based detectors but are in turn exposed by the newly proposed tools.