FakeTagger：通过来源跟踪对DeepFake传播的强大保护
FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking
摘要：
近年来，由于生成对抗网络（GAN）在图像合成方面的显着进步，DeepFake正在成为我们社会的共同威胁。不幸的是，现有的研究提出了各种方法，以对抗DeepFake并确定面部图像是真是假，仍然处于早期阶段。显然，目前的DeepFake检测方法很难捕捉到GANs的快速发展，特别是在攻击者可以故意逃避检测的对抗场景中，例如添加扰动来欺骗基于DNN的检测器。虽然被动检测只是告诉图像是假的还是真的，但另一方面，DeepFake出处为DeepFake取证中的来源跟踪提供了线索。因此，管理员可以立即阻止跟踪的假图像，并避免在社交网络中进一步传播。在本文中，我们研究了图像标记在服务于DeepFake来源跟踪方面的潜力。具体来说，我们设计了一种基于深度学习的方法，名为FakeTagger，具有简单而有效的编码器和解码器设计以及信道编码，以将消息嵌入到面部图像中，这是在各种激烈的基于GAN的DeepFake变换之后以高置信度恢复嵌入的消息。嵌入的消息可以用来表示面部图像的身份，这进一步有助于DeepFake检测和出处。实验结果表明，我们提出的方法可以在四种常见类型的DeepFakes上恢复嵌入的消息，平均准确率超过95%。我们的研究发现证实了有效的隐私保护技术，可以保护个人照片免受DeepFaked的影响。
英文摘要：
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. 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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
