Defending against GAN-based DeepFake Attacks via Transformation-aware Adversarial Faces
DeepFake represents a category of face-swapping attacks that leverage machine learning models such as autoen-coders or generative adversarial networks. Although the concept of the face-swapping is not new, its recent technical advances make fake content (e.g., images, videos) imperceptible to Humans. Various detection techniques for DeepFake attacks have been explored. These methods, however, are passive measures against DeepFakes as they are mitigation strategies after the high-quality fake content is generated. This work aims to take an offensive measure to impede the generation of high-quality fake images or videos. Specifically, we propose to use novel transformation-aware adversarially perturbed faces as a defense against GAN-based DeepFake attacks, which leverages differentiable random image transformations during the generation. We also propose an ensemble-based approach to enhance the defense robustness against GAN-based DeepFake variants under the black-box setting. We show that training a DeepFake model with adversarial faces can lead to a significant degradation in the quality of synthesized faces.
