  To protect privacy and prevent malicious use of deepfake, current studies
propose methods that interfere with the generation process, such as detection
and destruction approaches. However, these methods suffer from sub-optimal
generalization performance to unseen models and add undesirable noise to the
original image. To address these problems, we propose a new problem formulation
for deepfake prevention: generating a ``scapegoat image'' by modifying the
style of the original input in a way that is recognizable as an avatar by the
user, but impossible to reconstruct the real face. Even in the case of
malicious deepfake, the privacy of the users is still protected. To achieve
this, we introduce an optimization-based editing method that utilizes GAN
inversion to discourage deepfake models from generating similar scapegoats. We
validate the effectiveness of our proposed method through quantitative and user
studies.
