Juefei-Xu Felix, Wang Run, Huang Yihao, Guo Qing, Ma Lei, Liu Yang
Alibaba Group, Sunnyvale, CA USA.
Key Laboratory of Aerospace Information Security and Trust Computing, School of Cyber Science and Engineering, Wuhan University, Wuhan, China.
Int J Comput Vis. 2022;130(7):1678-1734. doi: 10.1007/s11263-022-01606-8. Epub 2022 May 4.
The creation or manipulation of facial appearance through deep generative approaches, known as , have achieved significant progress and promoted a wide range of benign and malicious applications, e.g., visual effect assistance in movie and misinformation generation by faking famous persons. The evil side of this new technique poses another popular study, i.e., aiming to identify the fake faces from the real ones. With the rapid development of the DeepFake-related studies in the community, both sides (i.e., DeepFake generation and detection) have formed the relationship of battleground, pushing the improvements of each other and inspiring new directions, e.g., the evasion of DeepFake detection. Nevertheless, the overview of such battleground and the new direction is unclear and neglected by recent surveys due to the rapid increase of related publications, limiting the in-depth understanding of the tendency and future works. To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 318 research papers carefully surveyed. We present the taxonomy of various DeepFake generation methods and the categorization of various DeepFake detection methods, and more importantly, we showcase the battleground between the two parties with detailed interactions between the adversaries (DeepFake generation) and the defenders (DeepFake detection). The battleground allows fresh perspective into the latest landscape of the DeepFake research and can provide valuable analysis towards the research challenges and opportunities as well as research trends and future directions. We also elaborately design interactive diagrams (http://www.xujuefei.com/dfsurvey) to allow researchers to explore their own interests on popular DeepFake generators or detectors.
通过深度生成方法来创建或操纵面部外观，即所谓的深度伪造，已经取得了重大进展，并推动了广泛的良性和恶意应用，例如电影中的视觉效果辅助以及通过伪造名人来制造错误信息。这项新技术的邪恶一面引发了另一项热门研究，即旨在从真实面孔中识别出伪造面孔。随着社区中与深度伪造相关研究的迅速发展，双方（即深度伪造生成和检测）形成了一种竞争关系，相互推动改进并激发新的方向，例如深度伪造检测的规避。然而，由于相关出版物数量的迅速增加，最近的调查并未明确阐述这种竞争关系和新方向，这限制了对该趋势和未来工作的深入理解。为了填补这一空白，在本文中，我们对深度伪造生成、深度伪造检测以及深度伪造检测规避这一主题的研究工作进行了全面概述和详细分析，仔细调研了318多篇研究论文。我们展示了各种深度伪造生成方法的分类以及各种深度伪造检测方法的归类，更重要的是，我们展示了双方之间的竞争关系，以及对手（深度伪造生成）和防御者（深度伪造检测）之间的详细互动。这种竞争关系为深入了解深度伪造研究的最新情况提供了新视角，并能对研究挑战与机遇、研究趋势及未来方向进行有价值的分析。我们还精心设计了交互式图表（http://www.xujuefei.com/dfsurvey），以便研究人员能够探索他们对热门深度伪造生成器或检测器的兴趣。
