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In this paper, we present a medical image integrity verification system to detect and approximate local malevolent image alterations (e.g., removal or addition of lesions) as well as identifying the nature of a global processing an image may have undergone (e.g., lossy compression, filtering, etc.). The proposed integrity analysis process is based on nonsignificant region watermarking with signatures extracted from different pixel blocks of interest, which are compared with the recomputed ones at the verification stage. A set of three signatures is proposed. The first two devoted to detection and modification location are cryptographic hashes and checksums, while the last one is issued from the image moment theory. In this paper, we first show how geometric moments can be used to approximate any local modification by its nearest generalized 2-D Gaussian. We then demonstrate how ratios between original and recomputed geometric moments can be used as image features in a classifier-based strategy in order to determine the nature of a global image processing. Experimental results considering both local and global modifications in MRI and retina images illustrate the overall performances of our approach. With a pixel block signature of about 200 bit long, it is possible to detect, to roughly localize, and to get an idea about the image tamper.