Zero-Shot Warning Generation for Misinformative Multimodal Content

• URL: http://arxiv.org/abs/2502.00752v1
• Date: Sun, 02 Feb 2025 11:18:05 GMT
• Title: Zero-Shot Warning Generation for Misinformative Multimodal Content
• Authors: Giovanni Pio Delvecchio, Huy Hong Nguyen, Isao Echizen,
• Abstract summary: Out-of-context misinformation, where authentic images are paired with false text, is particularly deceptive and easily misleads audiences. We present a model that detects multimodal misinformation through cross-modality consistency checks, requiring minimal training time. We also introduce a dual-purpose zero-shot learning task for generating contextualized warnings, enabling automated debunking and enhancing user comprehension.
• Score: 6.7932860553262415
• License:
• 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.

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