The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance

TL;DR

This paper explores expert perceptions of GenAI disinformation threats, emphasizing the importance of reproducible provenance methods and standardized benchmarks to effectively counter synthetic media manipulation.

Contribution

It introduces the need for reproducible provenance standards and benchmarks in GenAI disinformation research, highlighting their role in improving detection and mitigation strategies.

Findings

Deepfake videos evoke immediate shock but are less systemic.

Large-scale text generation risks epistemic fragmentation.

Experts favor provenance standards over detection tools.

Abstract

The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents findings from the first wave of a longitudinal expert perception survey (N=21) involving AI researchers, policymakers, and disinformation specialists. It examines the perceived severity of multimodal threats -- text, image, audio, and video -- and evaluates current mitigation strategies. Results indicate that while deepfake video presents immediate "shock" value, large-scale text generation poses a systemic risk of "epistemic fragmentation" and "synthetic consensus," particularly in the political domain. The survey reveals skepticism about technical detection tools, with experts favoring provenance standards and regulatory frameworks despite implementation barriers. GenAI disinformation research requires reproducible methods. The current challenge is measurement: without standardized benchmarks and reproducibility checklists, tracking or countering synthetic media remains difficult. We propose treating information integrity as an infrastructure with rigor in data provenance and methodological reproducibility.