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Spread of fake news using out-of-context images and captions has becomewidespread in this era of information overload. Since fake news can belong todifferent domains like politics, sports, etc. with their uniquecharacteristics, inference on a test image-caption pair is contingent on howwell the model has been trained on similar data. Since training individualmodels for each domain is not practical, we propose a novel framework termedDPOD (Domain-specific Prompt tuning using Out-of-domain data), which canexploit out-of-domain data during training to improve fake news detection ofall desired domains simultaneously. First, to compute generalizable features,we modify the Vision-Language Model, CLIP to extract features that helps toalign the representations of the images and corresponding captions of both thein-domain and out-of-domain data in a label-aware manner. Further, we propose adomain-specific prompt learning technique which leverages training samples ofall the available domains based on the extent they can be useful to the desireddomain. Extensive experiments on the large-scale NewsCLIPpings and VERITEbenchmarks demonstrate that DPOD achieves state of-the-art performance for thischallenging task. Code: https://github.com/scviab/DPOD.

Can Out-of-Domain data help to Learn Domain-Specific Prompts for Multimodal Misinformation Detection?