In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have become deeply embedded in various aspects of daily life and work. However, these technologies have also led to the emergence of Fake Artificial Intelligence Generated Content (FAIGC), posing new challenges in distinguishing genuine information. It is crucial to recognize that AIGC technology is akin to a double-edged sword; its potent generative capabilities, while beneficial, also pose risks for the creation and dissemination of FAIGC. In this survey, We propose a new taxonomy that provides a more comprehensive breakdown of the space of FAIGC methods today. Next, we explore the modalities and generative technologies of FAIGC. We introduce FAIGC detection methods and summarize the related benchmark from various perspectives. Finally, we discuss outstanding challenges and promising areas for future research.

翻译：近年来，以大型语言模型（LLMs）和扩散模型（DMs）为代表的生成式人工智能模型彻底改变了内容生产方式。这些人工智能生成内容（AIGC）已深度嵌入日常生活与工作的各个方面。然而，这些技术也催生了假人工智能生成内容（FAIGC）的出现，为辨别真实信息带来了新的挑战。必须认识到，AIGC技术犹如一柄双刃剑：其强大的生成能力虽具优势，却也伴随着FAIGC的创造与传播风险。本综述提出了一种新的分类体系，更全面地剖析了当前FAIGC方法的研究空间。继而，我们探讨了FAIGC的模态与生成技术，介绍了FAIGC的检测方法，并从多角度归纳了相关基准。最后，本文讨论了现存的关键挑战与未来研究的前景领域。