Abstract
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 in this https URL.
Abstract (translated)
除了深度伪造技术的进步，相应的检测技术也需要持续进化来规范深度伪造技术的潜在滥用，例如隐私侵犯和网络钓鱼攻击。这项调查全面回顾了深度伪造技术和检测的最新发展，总结和分析了这一快速发展的领域的现状。我们首先统一了任务定义，全面介绍了数据集和指标，并讨论了生成和检测技术框架的发展。接着，我们讨论了几个相关子领域的开发，重点研究了四个主流的深度伪造领域：流行人脸交换、人脸复原、谈话式人脸生成和面部属性编辑以及外国检测。随后，我们针对每个领域全面评估了代表方法，全面评估了顶级会议/期刊上最近发表的作品。最后，我们分析了讨论领域所面临的挑战和未来的研究方向。我们密切关注着这个链接：https://url.com/。
URL
https://arxiv.org/abs/2403.17881
