  The ability to detect manipulated visual content is becoming increasingly
important in many application fields, given the rapid advances in image
synthesis methods. Of particular concern is the possibility of modifying the
content of medical images, altering the resulting diagnoses. Despite its
relevance, this issue has received limited attention from the research
community. One reason is the lack of large and curated datasets to use for
development and benchmarking purposes. Here, we investigate this issue and
propose M3Dsynth, a large dataset of manipulated Computed Tomography (CT) lung
images. We create manipulated images by injecting or removing lung cancer
nodules in real CT scans, using three different methods based on Generative
Adversarial Networks (GAN) or Diffusion Models (DM), for a total of 8,577
manipulated samples. Experiments show that these images easily fool automated
diagnostic tools. We also tested several state-of-the-art forensic detectors
and demonstrated that, once trained on the proposed dataset, they are able to
accurately detect and localize manipulated synthetic content, even when
training and test sets are not aligned, showing good generalization ability.
Dataset and code are publicly available at
https://grip-unina.github.io/M3Dsynth/.
