This paper evaluates the effectiveness of keypoint methods for content-based protection of digital images. These methods identify a set of "distinctive" regions (termed keypoints) in an image and encode them using descriptors that are robust to expected image transformations. To determine whether particular images were derived from a protected image, the keypoints for both images are generated and their descriptors matched. We describe a comprehensive set of experiments to examine how keypoint methods cope with three real-world challenges: (1) loss of keypoints due to cropping; (2) matching failures caused by approximate nearest-neighbor indexing schemes; (3) degraded descriptors due to significant image distortions. While keypoint methods perform very well in general, this paper identifies cases where the accuracy of such methods degrades.
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