AI-Generated Text Detection: A Comprehensive Review of Active and Passive Approaches

The rapid advancement of large language models (LLMs) has driven the pervasive adoption of AI-generated content (AIGC), while also raising concerns about misinformation, academic misconduct, biased or harmful content, and other risks. Detecting AI-generated text has thus become essential to safeguard the authenticity and reliability of digital information. This survey reviews recent progress in detection methods, categorizing approaches into passive and active categories based on their reliance on intrinsic textual features or embedded signals. Passive detection is further divided into surface linguistic feature-based and language model-based methods, whereas active detection encompasses watermarking-based and semantic retrieval-based approaches. This taxonomy enables systematic comparison of methodological differences in model dependency, applicability, and robustness. A key challenge for AI-generated text detection is that existing detectors are highly vulnerable to adversarial attacks, particularly paraphrasing, which substantially compromises their effectiveness. Addressing this gap highlights the need for future research on enhancing robustness and cross-domain generalization. By synthesizing current advances and limitations, this survey provides a structured reference for the field and outlines pathways toward more reliable and scalable detection solutions.