机器生成文本：威胁模型与检测方法的综合综述
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
AI总结：
本综述系统分析当代NLG系统威胁模型，全面回顾机器生成文本检测方法，强调检测系统需具备公平性、鲁棒性与问责制以应对滥用。
摘要：
机器生成的文本越来越难以与人类撰写的文本区分。强大的开源模型可免费获取，而普及生成模型访问的用户友好工具正在激增。ChatGPT 在本综述第一版发布后不久推出，集中体现了这些趋势。最先进自然语言生成（NLG）系统的巨大潜力因众多滥用途径而受到制约。检测机器生成文本是减少 NLG 模型滥用的关键对策，但面临重大技术挑战和众多开放问题。我们提供的综述包括：1）对当代 NLG 系统构成的威胁模型的广泛分析，以及 2）迄今为止最完整的机器生成文本检测方法回顾。本综述将机器生成文本置于其网络安全和社会背景中，并为未来工作提供有力指导，以应对最关键的威胁模型，并确保检测系统本身通过公平性、鲁棒性和问责制展现可信度。
英文摘要：
Machine generated text is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. The great potential of state-of-the-art natural language generation (NLG) systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
