arXiv AI By Alexander Loth, Martin Kappes, Marc-Oliver Pahl

Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems

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The paper "Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems" updates the 2024 survey on generative AI and misinformation, shifting from a literature review to practical countermeasures. It documents changes in the threat landscape, noting that Large Language Models and multimodal systems have improved AI-generated content, thereby worsening information quality. The authors introduce JudgeGPT, a platform for assessing human perception of AI-generated news, and RogueGPT, a controlled stimulus generation engine, forming an experimental pipeline to study detection and mitigation strategies, including LLM-based detection and inoculation approaches.

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arXiv AI
Sep 30

Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?

The paper investigates whether multimodal large language models (MLLMs) can generate and detect realistic multimodal fake news on social media. Using a multi‑agent framework—comprising a story agent, an image agent, and a critic agent—the authors produced over 9,000 paired multimodal news posts across science, health, and entertainment domains. They benchmarked 16 open‑ and closed‑source MLLMs for automated detection and found that most models fall far short of human accuracy, especially in identifying image authenticity, highlighting the need for stronger defenses against social media fake news.

By Jiyao Yang, Yang Liu, Zhenyue Qin, Qingyu Chen, Xiuzhen Zhang