  Recent progress in generative models has made it easier for a wide audience
to edit and create image content, raising concerns about the proliferation of
deepfakes, especially in healthcare. Despite the availability of numerous
techniques for detecting manipulated images captured by conventional cameras,
their applicability to medical images is limited. This limitation stems from
the distinctive forensic characteristics of medical images, a result of their
imaging process.
  In this work we propose a novel anomaly detector for medical imagery based on
diffusion models. Normally, diffusion models are used to generate images.
However, we show how a similar process can be used to detect synthetic content
by making a model reverse the diffusion on a suspected image. We evaluate our
method on the task of detecting fake tumors injected and removed from CT and
MRI scans. Our method significantly outperforms other state of the art
unsupervised detectors with an increased AUC of 0.9 from 0.79 for injection and
of 0.96 from 0.91 for removal on average.
