The rapid proliferation of AI-Generated Content (AIGC), spanning text, images, video, and audio, has created a dual-edged sword of unprecedented creativity and significant societal risks, including misinformation and disinformation. This survey provides a comprehensive and structured overview of the current landscape of AIGC detection technologies. We begin by chronicling the evolution of generative models, from foundational GANs to state-of-the-art diffusion and transformer-based architectures. We then systematically review detection methodologies across all modalities, organizing them into a novel taxonomy of External Detection and Internal Detection. For each modality, we trace the technical progression from early feature-based methods to advanced deep learning, while also covering critical tasks like model attribution and tampered region localization. Furthermore, we survey the ecosystem of publicly available detection tools and practical applications. Finally, we distill the primary challenges facing the field—including generalization, robustness, interpretability, and the lack of universal benchmarks—and conclude by outlining key future directions, such as the development of holistic AI Safety Agents, dynamic evaluation standards, and AI-driven governance frameworks. This survey aims to provide researchers and practitioners with a clear, in-depth understanding of the state of the art and critical frontiers in the ongoing endeavor to ensure a safe and trustworthy AIGC ecosystem.
Xu et al. (2025) studied this question.