A training-free pipeline using image and text retrieval plus two-stage Gemini and GPT-4o mini reasoning reaches 90.0% accuracy on NewsCLIPpings out-of-context detection, but code, prompts, and error bars are missing.
Learning Domain-Invariant Features for Out-of-Context News Detection
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abstract
Out-of-context news is a common type of misinformation on online media platforms. This involves posting a caption, alongside a mismatched news image. Existing out-of-context news detection models only consider the scenario where pre-labeled data is available for each domain, failing to address the out-of-context news detection on unlabeled domains (e.g. news topics or agencies). In this work, we therefore focus on domain adaptive out-of-context news detection. In order to effectively adapt the detection model to unlabeled news topics or agencies, we propose ConDA-TTA (Contrastive Domain Adaptation with Test-Time Adaptation) which applies contrastive learning and maximum mean discrepancy (MMD) to learn domain-invariant features. In addition, we leverage test-time target domain statistics to further assist domain adaptation. Experimental results show that our approach outperforms baselines in most domain adaptation settings on two public datasets, by as much as 2.93% in F1 and 2.08% in accuracy.
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E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs
A training-free pipeline using image and text retrieval plus two-stage Gemini and GPT-4o mini reasoning reaches 90.0% accuracy on NewsCLIPpings out-of-context detection, but code, prompts, and error bars are missing.