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.
By Alexander Loth, Martin Kappes, Marc-Oliver Pahl
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.
By Alexander Loth, Martin Kappes, Marc-Oliver Pahl
arXiv:2606. 09700v1 Announce Type: cross Abstract: Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content.
By Qin Yang, Lu Malloy, Joshua Lee, Xiaohan Chang, Meisam Mohammady, Doowon Kim, Yuan Hong
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.
By Jason Miklian, John E. Katsos
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.
By Zhao Tong, Chunlin Gong, Yimeng Gu, Haichao Shi, Qiang Liu, Shu Wu, Xingcheng Xu, Xiao-Yu Zhang
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...
By Kerri Prinos, Lilianne Brush, Cameron Denton
arXiv:2609.20838v1 Announce Type: new
Abstract: In this study, we examine how modern LLMs generate and detect fake news under controlled settings across four manipulation scenarios. These are open-en...
By Zeynep \"Ozdemir, Murat Osmano\u{g}lu, Sevgi Yi\u{g}it-Sert, \"Omer \"Ozg\"ur Tanr{\i}\"over, Y{\i}lmaz Ar
arXiv:2609.39662v1 Announce Type: new
Abstract: Vision-language models (VLMs) are increasingly used for AI-generated image (AIGI) detection, providing natural-language explanations for authenticity j...
By Eunmin Lee, Jungwoo Kim, Jong-Seok Lee
arXiv:2501. 14728v2 Announce Type: replace-cross Abstract: While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raises growing concerns about online information security.
By Zehong Yan, Peng Qi, Wynne Hsu, Mong Li Lee
arXiv:2608.21841v1 Announce Type: new
Abstract: Conversational AI increasingly shapes consequential decisions, yet users have limited support for recognizing and resisting manipulation. We present AI...
By Rachel Poonsiriwong (Pub), Chayapatr (Pub), Archiwaranguprok, Constanze Albrecht, Monchai Lertsutthiwong, Pattie Maes, Pat Pataranutaporn
The article reports on a survey of 21 experts—including AI researchers, policymakers, and disinformation specialists—about the risks posed by Generative Artificial Intelligence (GenAI) to information integrity. It finds that while deepfake videos are immediately shocking, large‑scale text generation threatens to create systemic epistemic fragmentation and synthetic consensus, especially in politics. Experts express doubt about current detection tools and instead advocate for provenance standards and regulatory frameworks, emphasizing the need for reproducible research methods and standardized benchmarks to effectively counter synthetic media.
By Alexander Loth, Martin Kappes, Marc-Oliver Pahl
arXiv:2610.15449v2 Announce Type: new
Abstract: Despite significant advances in language understanding, Large Language Models (LLMs) can still miss meaning hidden in plain sight, failing to identify...
By Yisong Xiao, Benlei Cui, Haiwen Hong, Shiji Zhao, Zonghao Ying, Yaoyuan Zhang, Longtao Huang, Aishan Liu, Hui Xue, Ting Ma, Xianglong Liu