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

TL;DR

This survey reviews the rise of AI-generated content, focusing on the emergence of fake content, and discusses detection methods, challenges, and future research directions in this rapidly evolving field.

Contribution

It introduces a new taxonomy for FAIGC, analyzes detection techniques, and summarizes benchmarks, providing a comprehensive overview of the field.

Findings

A new taxonomy for FAIGC methods

Summary of detection techniques and benchmarks

Discussion of future challenges and research opportunities

Abstract

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.