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.

翻译：自2024年综述以来，生成式人工智能与虚假信息研究领域持续演进。本文提出更新的视角，从文献综述转向实际对策研究。我们报告了威胁态势的变化，包括通过大语言模型和多模态系统改进的AI生成内容。本工作的核心是我们的实践贡献：JudgeGPT（评估人类对AI生成新闻感知的平台）和RogueGPT（用于研究的受控刺激生成引擎）。这些工具共同构成了研究人类如何感知和检测AI生成虚假信息的实验流程。我们的研究结果表明，检测能力已有所提升，但生成与检测之间的博弈仍在持续。我们探讨了包括基于LLM的检测、免疫接种方法以及生成式AI的双重用途特性在内的缓解策略。此项工作为应对人工智能对信息质量负面影响的研究提供了贡献。