发表机构
Frankfurt University of Applied Sciences; IMT Atlantique（法兰克福应用科学大学; IMT大西洋高等矿业电信学校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过504人参与的实验，借助调整后的网络杀伤链分析人类对AI生成虚假信息的感知，发现感知准确率差距、LLM生成文本难区分及不对称认知疲劳效应，为主动防御AI虚假信息提供了干预方向。
AI 中文摘要
生成式AI可大规模生成定制化虚假信息，但防御措施大多仍处于被动状态。我们开展了一项包含504名参与者、2438次判断的人类受试者研究，让用户对新闻片段的来源（人类 vs. 机器）和真实性（真实 vs. 虚假）进行分类。我们采用调整后的网络杀伤链作为干预分类框架，将感知数据映射到认知攻击生命周期的各个阶段，得出三项关键发现：（1）存在感知准确率差距，即增强的怀疑并未提升检测能力；（2）现代大型语言模型（LLM）常生成与人类无法区分的文本；（3）存在不对称认知疲劳效应，即持续暴露下虚假新闻检测准确率下降10.2个百分点，而AI生成内容的检测保持稳定。这些发现为主动防御AI驱动的虚假信息确定了候选干预点。
英文摘要
Generative AI enables customized misinformation at scale, yet defenses remain largely reactive. We present empirical findings from a human-subject study (n=504 participants, n=2,438 judgments) in which users classified news fragments by origin (human vs. machine) and veracity (real vs. fake). We organize results using an adapted cybersecurity kill chain as a taxonomy for intervention, mapping perception data onto stages of a cognitive attack lifecycle. Three key findings emerge: (1) a perception-accuracy gap where heightened suspicion does not improve detection; (2) modern LLMs frequently produce human-indistinguishable text; and (3) an asymmetric cognitive fatigue effect where fake-news detection degrades by 10.2 percentage points under sustained exposure while AI-origin detection remains stable. These findings identify candidate intervention points for proactive defense against AI-driven disinformation.
CommentsCamera-ready version. 10 pages, 3 figures, 2 tables
Journal refINFORMATIK 2026, LNI P-384, pp. 321-330
