Can I read FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking on EtoBox?

FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking by Wang, Run; Juefei-Xu, Felix; Luo, Meng; Liu, Yang; Wang, Lina is a scholarly article available to read on EtoBox.

What is FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking about?

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 r

Author

Wang, Run; Juefei-Xu, Felix; Luo, Meng; Liu, Yang; Wang, Lina

Published

2020

Language

EN