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To protect privacy and prevent malicious use of deepfake, current studiespropose methods that interfere with the generation process, such as detectionand destruction approaches. However, these methods suffer from sub-optimalgeneralization performance to unseen models and add undesirable noise to theoriginal image. To address these problems, we propose a new problem formulationfor deepfake prevention: generating a ``scapegoat image'' by modifying thestyle of the original input in a way that is recognizable as an avatar by theuser, but impossible to reconstruct the real face. Even in the case ofmalicious deepfake, the privacy of the users is still protected. To achievethis, we introduce an optimization-based editing method that utilizes GANinversion to discourage deepfake models from generating similar scapegoats. Wevalidate the effectiveness of our proposed method through quantitative and userstudies.

Scapegoat Generation for Privacy Protection from Deepfake