In addition to the advancements in deepfake generation, corresponding
detection technologies need to continuously evolve to regulate the potential
misuse of deepfakes, such as for privacy invasion and phishing attacks. This
survey comprehensively reviews the latest developments in deepfake generation
and detection, summarizing and analyzing the current state of the art in this
rapidly evolving field. We first unify task definitions, comprehensively
introduce datasets and metrics, and discuss the development of generation and
detection technology frameworks. Then, we discuss the development of several
related sub-fields and focus on researching four mainstream deepfake fields:
popular face swap, face reenactment, talking face generation, and facial
attribute editing, as well as foreign detection. Subsequently, we
comprehensively benchmark representative methods on popular datasets for each
field, fully evaluating the latest and influential works published in top
conferences/journals. Finally, we analyze the challenges and future research
directions of the discussed fields. We closely follow the latest developments
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对深度伪造生成和检测的最新发展进行了综合回顾和分析，并讨论了该领域的当前状态，主要集中在深度伪造生成和检测技术框架的发展，以及人脸交换、脸部再现、说话人脸生成和面部属性编辑等主流深度伪造领域的研究，并评估了各领域中的代表性方法和最新的研究成果，最后分析了该领域的挑战与未来研究方向。