Deepfakes pose an evolving threat to cybersecurity, which calls for the development of automated countermeasures. While considerable forensic research has been devoted to the detection and localisation of deepfakes, solutions for reversing fake to real are yet to be developed. In this study, we introduce cyber vaccination for conferring immunity to deepfakes. Analogous to biological vaccination that injects antigens to induce immunity prior to infection by an actual pathogen, cyber vaccination simulates deepfakes and performs adversarial training to build a defensive immune system. Aiming at building up attack-agnostic immunity with limited computational resources, we propose to simulate various deepfakes with one single overpowered attack: face masking. The proposed immune system consists of a vaccinator for inducing immunity and a neutraliser for recovering facial content. Experimental evaluations demonstrate effective immunity to face replacement, face reenactment and various types of corruptions.

翻译：深度伪造对网络安全构成不断演变的威胁，亟需开发自动化应对措施。尽管大量取证研究致力于深度伪造的检测与定位，但将伪造内容还原为真实内容的解决方案仍有待开发。本研究提出一种"网络疫苗接种"方法，为深度伪造赋予免疫能力。类比生物疫苗通过注射抗原在病原体真正感染前诱导免疫机制，网络疫苗接种通过模拟深度伪造并进行对抗训练，构建防御性免疫系统。针对有限计算资源下实现攻击无关的免疫能力，我们提出仅通过单一强效攻击方法——面部遮挡，即可模拟多种深度伪造。所提出的免疫系统包含用于诱导免疫的"接种器"和用于恢复面部内容的"中和器"。实验评估表明，该方法对面部替换、面部重演及多种图像损坏具有有效免疫能力。