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.

通过使用基于解释、局部分数或注意机制的三种方法，并利用 Xception 网络作为共同的骨干架构，我们将任务定为弱监督定位问题，并在方法选择、监督类型、数据集和生成器等方面进行了仔细分析；我们的研究通过构建数据集，其中只有一个组件发生变化，证明了弱监督定位是可行的；而基于局部分数的最佳检测方法对于较松的监督比对于数据集或生成器的不匹配更不敏感。