In recent years, DeepFake is becoming a common threat to our society, due to
the remarkable progress of generative adversarial networks (GAN) in image
synthesis. Unfortunately, existing studies that propose various approaches, in
fighting against DeepFake and determining if the facial image is real or fake,
is still at an early stage. Obviously, the current DeepFake detection method
struggles to catch the rapid progress of GANs, especially in the adversarial
scenarios where attackers can evade the detection intentionally, such as adding
perturbations to fool the DNN-based detectors. While passive detection simply
tells whether the image is fake or real, DeepFake provenance, on the other
hand, provides clues for tracking the sources in DeepFake forensics. Thus, the
tracked fake images could be blocked immediately by administrators and avoid
further spread in social networks.In this paper, we investigate the potentials
of image tagging in serving the DeepFake provenance tracking. Specifically, we
devise a deep learning-based approach, named FakeTagger, with a simple yet
effective encoder and decoder design along with channel coding to embed message
to the facial image, which is to recover the embedded message after various
drastic GAN-based DeepFake transformation with high confidence. Experimental
results demonstrate that our proposed approach could recover the embedded
message with an average accuracy of more than 95% over the four common types of
DeepFakes. Our research finding confirms effective privacy-preserving
techniques for protecting personal photos from being DeepFaked. Thus, effective
proactive defense mechanisms should be developed for fighting against
DeepFakes, instead of simply devising DeepFake detection methods that can be
mostly ineffective in practice.

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