Weakly-supervised deepfake localization in diffusion-generated images

Stefan Smeu, Elisabeta Oneata, Dan Oneata

Abstract:

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.