构建工具链评估人类对AI假信息的识别能力，揭示生成与检测的持续博弈。
Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems
- 开发JudgeGPT与RogueGPT，形成研究假信息感知的实验流程
- 发现人类识别能力提升，但生成与检测对抗仍在持续
- 适合关注信息真实性、模型安全与防御策略的研究者
生成式AI与虚假信息研究自2024年综述以来已取得进展。本文从文献回顾转向实践对策，报告了威胁环境的新变化：大语言模型（LLMs）和多模态系统使生成内容质量显著提升。本工作核心贡献为两个实用工具：JudgeGPT——用于评估人类对AI生成新闻的感知能力；RogueGPT——用于生成受控刺激内容的研究引擎。二者共同构成研究人类如何感知与识别AI生成虚假信息的实验流水线。研究发现，尽管检测能力有所提高，生成与检测之间的竞赛仍持续进行。文中讨论了基于大语言模型的检测方法、免疫策略以及生成式AI的双重用途问题。本工作推动了应对人工智能对信息质量负面影响的研究。
原文摘要 · Abstract (English)
Generative AI and misinformation research has evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical countermeasures. We report on changes in the threat landscape, including improved AI-generated content through Large Language Models (LLMs) and multimodal systems. Central to this work are our practical contributions: JudgeGPT, a platform for evaluating human perception of AI-generated news, and RogueGPT, a controlled stimulus generation engine for research. Together, these tools form an experimental pipeline for studying how humans perceive and detect AI-generated misinformation. Our findings show that detection capabilities have improved, but the competition between generation and detection continues. We discuss mitigation strategies including LLM-based detection, inoculation approaches, and the dual-use nature of generative AI. This work contributes to research addressing the adverse impacts of AI on information quality.
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