🤖 AI Summary
This study addresses the growing threat posed by generative large language models (LLMs) in amplifying disinformation within digital ecosystems. To systematically investigate users’ ability to detect AI-generated fake news, the authors develop an end-to-end experimental framework integrating RogueGPT—a novel, controllable disinformation generation engine—and JudgeGPT, an evaluation platform. The framework incorporates multimodal content generation, LLM-assisted detection, cognitive inoculation interventions, and human perception experiments. Findings reveal that while human detection capabilities have improved, a dynamic adversarial interplay persists between generation and detection mechanisms. The proposed strategies demonstrate significant efficacy in mitigating risks associated with AI-generated disinformation, advancing the field beyond theoretical discourse toward empirically grounded countermeasures.
📝 Abstract
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.