  Deepfakes raised serious concerns on the authenticity of visual contents.
Prior works revealed the possibility to disrupt deepfakes by adding adversarial
perturbations to the source data, but we argue that the threat has not been
eliminated yet. This paper presents MagDR, a mask-guided detection and
reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR
starts with a detection module that defines a few criteria to judge the
abnormality of the output of deepfakes, and then uses it to guide a learnable
reconstruction procedure. Adaptive masks are extracted to capture the change in
local facial regions. In experiments, MagDR defends three main tasks of
deepfakes, and the learned reconstruction pipeline transfers across input data,
showing promising performance in defending both black-box and white-box
attacks.
