The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news. This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations. In particular, four types of facial manipulation are reviewed: i) entire face synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv) expression swap. For each manipulation group, we provide details regarding manipulation techniques, existing public databases, and key benchmarks for technology evaluation of fake detection methods, including a summary of results from those evaluations. Among all the aspects discussed in the survey, we pay special attention to the latest generation of DeepFakes, highlighting its improvements and challenges for fake detection. In addition to the survey information, we also discuss open issues and future trends that should be considered to advance in the field.

中文翻译：

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DeepFakes and Beyond：人脸操作和假检测的调查

大规模公共数据库的免费访问，以及深度学习技术，特别是生成对抗网络的快速进步，导致了非常逼真的虚假内容的产生，并在这个假新闻时代对社会产生了相应的影响。本次调查全面回顾了处理人脸图像的技术，包括 DeepFake 方法，以及检测此类操作的方法。特别地，回顾了四种类型的面部操作：i) 全脸合成，ii) 身份交换 (DeepFakes)，iii) 属性操作，以及 iv) 表情交换。对于每个操作组，我们提供有关操作技术、现有公共数据库和虚假检测方法技术评估的关键基准的详细信息，包括这些评估的结果摘要。在调查中讨论的所有方面中，我们特别关注最新一代的 DeepFakes，重点介绍了其在假检测方面的改进和挑战。除了调查信息之外，我们还讨论了应该考虑在该领域取得进展的开放问题和未来趋势。