7 papers
Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
Can humans tell whether a news article was written by a person or a large language model (LLM)? We investigate this question using JudgeGPT, a study platform that independently mea…
CRED-1: An Open Multi-Signal Domain Credibility Dataset for Automated Pre-Bunking of Online Misinformation
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
This article presents CRED-1, an open, reproducible domain-level credibility dataset combining two openly-licensed source lists (OpenSources.co and Iffy.news) with four computed en…
Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
As foundation models (FMs) approach human-level fluency, distinguishing synthetic from organic content has become a key challenge for Trustworthy Web Intelligence. This paper prese…
Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
Generative AI and misinformation research has evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical counterme…
Origin Lens: A Privacy-First Mobile Framework for Cryptographic Image Provenance and AI Detection
Alexander Loth, Dominique Conceicao Rosario, Peter Ebinger +2
The proliferation of generative AI poses challenges for information integrity assurance, requiring systems that connect model governance with end-user verification. We present Orig…
The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance
Alexander Loth, Martin Kappes, Marc-Oliver Pahl
The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents…