🤖 AI Summary
This study addresses the challenge that generative AI enables the scalable production of customized disinformation, while existing defenses remain largely reactive and lack a systematic understanding of the cognitive attack lifecycle. By adapting the cybersecurity kill chain model to the domain of cognitive security, this work conducts an empirical experiment with 504 participants to systematically analyze human perception and detection mechanisms regarding AI-generated disinformation. The research reveals a "perception-accuracy gap" and asymmetric cognitive fatigue effects, demonstrating that sustained exposure significantly reduces fake news detection rates by 10.2%. These findings precisely identify intervention points for proactive defense, providing critical theoretical and empirical foundations for constructing forward-looking, systematic cognitive security defense frameworks.
📝 Abstract
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.