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The ability to detect manipulated visual content is becoming increasinglyimportant in many application fields, given the rapid advances in imagesynthesis methods. Of particular concern is the possibility of modifying thecontent of medical images, altering the resulting diagnoses. Despite itsrelevance, this issue has received limited attention from the researchcommunity. One reason is the lack of large and curated datasets to use fordevelopment and benchmarking purposes. Here, we investigate this issue andpropose M3Dsynth, a large dataset of manipulated Computed Tomography (CT) lungimages. We create manipulated images by injecting or removing lung cancernodules in real CT scans, using three different methods based on GenerativeAdversarial Networks (GAN) or Diffusion Models (DM), for a total of 8,577manipulated samples. Experiments show that these images easily fool automateddiagnostic tools. We also tested several state-of-the-art forensic detectorsand demonstrated that, once trained on the proposed dataset, they are able toaccurately detect and localize manipulated synthetic content, including whentraining and test sets are not aligned, showing good generalization ability.Dataset and code will be publicly available athttps://grip-unina.github.io/M3Dsynth/.

M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations