arXiv AI
Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens
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arXiv AI
3d ago
Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems
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.
arXiv Computation and Language
1d ago
Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild
The paper introduces JudgeGPT and RogueGPT, a dual‑axis framework that separates authenticity from attribution to study how people detect hallucinations and disinformation from foundation models. Using 918 evaluations on five models, including GPT‑4 and Llama‑2, the authors apply Structural Causal Models to test causal hypotheses about detection accuracy. Results show political orientation has little effect, while familiarity with fake news mediates detection, and a "fluency trap" in GPT‑4 outputs makes them hard to distinguish from human text, implying pre‑bunking should focus on cognitive source monitoring.
arXiv Machine Learning
Jun 9
What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks
arXiv:2606. 09700v1 Announce Type: cross Abstract: Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content.
arXiv AI
Jun 11
"That's AI Slop, You Bot!" Studying Accusations, Evidence, and Credibility in Online Discourse Towards LLM-Generated Comments
arXiv:2606. 12073v1 Announce Type: cross Abstract: Generative AI has made fluent prose cheap to produce, breaking the old promise to readers that good writing meant real thinking.
arXiv AI
Sep 10
CogniDir: Combating Cognitive Malicious Comments via Adaptive Distributional Learning for Robust Fake News Detection
CogniDir is an adaptive distributional learning framework designed to improve fake news detection against new psychologically grounded malicious comments generated by Large Language Models. It reframes robust detection as a dynamic data mixture optimization problem, using cognitive psychology to formalize adversarial paradigms and an information‑theoretic score to guide adaptive sampling of training data. Experiments on three benchmarks show that CogniDir achieves state‑of‑the‑art robustness, boosting F1 scores by up to 17.9% over existing baselines under heterogeneous AI‑generated attacks.
arXiv Computation and Language
Oct 26
Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers
arXiv:2606.21037v3 Announce Type: replace-cross Abstract: The empirical foundation of cyber deception relies on human-centered hypotheses, but the rapid emergence of autonomous, AI-enabled attackers...