  The remarkable generative capabilities of denoising diffusion models have
raised new concerns regarding the authenticity of the images we see every day
on the Internet. However, the vast majority of existing deepfake detection
models are tested against previous generative approaches (e.g. GAN) and usually
provide only a "fake" or "real" label per image. We believe a more informative
output would be to augment the per-image label with a localization map
indicating which regions of the input have been manipulated. To this end, we
frame this task as a weakly-supervised localization problem and identify three
main categories of methods (based on either explanations, local scores or
attention), which we compare on an equal footing by using the Xception network
as the common backbone architecture. We provide a careful analysis of all the
main factors that parameterize the design space: choice of method, type of
supervision, dataset and generator used in the creation of manipulated images;
our study is enabled by constructing datasets in which only one of the
components is varied. Our results show that weakly-supervised localization is
attainable, with the best performing detection method (based on local scores)
being less sensitive to the looser supervision than to the mismatch in terms of
dataset or generator.
