Zero-Shot Warning Generation for Misinformative Multimodal Content

TL;DR

This paper introduces a zero-shot model for detecting and explaining out-of-context multimodal misinformation, using minimal training and a lightweight architecture to generate contextualized warnings for debunking.

Contribution

It presents a novel zero-shot detection method for multimodal misinformation and a lightweight model capable of generating explanatory warnings with minimal training.

Findings

Competitive performance with fewer parameters

Effective zero-shot warning generation

Qualitative and human evaluation results

Abstract

The widespread prevalence of misinformation poses significant societal concerns. Out-of-context misinformation, where authentic images are paired with false text, is particularly deceptive and easily misleads audiences. Most existing detection methods primarily evaluate image-text consistency but often lack sufficient explanations, which are essential for effectively debunking misinformation. We present a model that detects multimodal misinformation through cross-modality consistency checks, requiring minimal training time. Additionally, we propose a lightweight model that achieves competitive performance using only one-third of the parameters. We also introduce a dual-purpose zero-shot learning task for generating contextualized warnings, enabling automated debunking and enhancing user comprehension. Qualitative and human evaluations of the generated warnings highlight both the potential and limitations of our approach.