DeepTag: Robust Image Tagging for DeepFake Provenance
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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, to determine if the facial image is real or fake, is still at an early stage. Obviously, the current DeepFake detection method struggles to catchthe rapid progress of GANs, especially in the adversarial scenarios where attackers can evade the detection intentionally, such as adding perturbations to fool 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 investigated the potentials of image tagging in serving the DeepFake provenance. Specifically, we devise a deep learning-based approach, named DeepTag, with a simple yet effective encoder and decoder design to embed message to the facial image, which is to recover the embedded message after various drastic GAN-based DeepFake transformation with high confidence. The embedded message could be employed to represent the identity of facial images, which further contributed to DeepFake detection and provenance. Experimental results demonstrate that our proposed approach could recover the embedded message with an average accuracy of nearly 90 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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