研究人类如何识别AI生成假新闻，发现越警惕越难辨真伪。
Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens
- 用网络安全杀伤链框架分析人类识别假信息的思维过程
- 持续看假新闻导致识别准确率下降10.2个百分点
- 当前大模型文本已几乎无法与真人写作区分
生成式AI使大规模定制化虚假信息成为可能，但防御手段仍以被动响应为主。我们基于一项包含504名参与者、2,438次判断的人类实验，研究用户如何通过来源（人工/机器）和真实性（真实/虚假）对新闻片段进行分类。采用经调整的网络安全杀伤链作为干预分类框架，将感知数据映射至认知攻击生命周期的各个阶段。主要发现有三：(1) 存在感知-准确率差距，即怀疑情绪增强并未提升识别能力；(2) 现代大语言模型（LLMs）频繁生成难以与人类写作区分的文本；(3) 认知疲劳呈非对称效应：在持续暴露下，假新闻识别准确率下降10.2个百分点，而AI来源识别能力保持稳定。这些结果为应对AI驱动的虚假信息提供了可主动干预的关键节点。
原文摘要 · Abstract (English)
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.
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