Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities

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, spanning texts, images, videos, and audio. The authenticity of AI-generated content is progressively enhancing, approaching human-level creative standards. 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, categorized under AI-generated disinformation and AI-generated misinformation. From various perspectives, we then introduce FAIGC detection methods, including Deceptive FAIGC Detection, Deepfake Detection, and Hallucination-based FAIGC Detection. Finally, we discuss outstanding challenges and promising areas for future research.