Out-of-Context Misinformation Detection via Variational Domain-Invariant Learning with Test-Time Training
Xi Yang, Han Zhang, Zhijian Lin, Yibiao Hu, Hong Han
Abstract
Out-of-context misinformation (OOC) is a low-cost form of misinformation in news reports, which refers to place authentic images into out-of-context or fabricated image-text pairings. This problem has attracted significant attention from researchers in recent years. Current methods focus on assessing image-text consistency or generating explanations. However, these approaches assume that the training and test data are drawn from the same distribution. When encountering novel news domains, models tend to perform poorly due to the lack of prior knowledge. To address this challenge, we propose Variational Domain-Invariant Learning with Test-Time Training (VDT) framework to enhance the domain adaptation capability for OOC misinformation detection. Domain-Invariant Variational Align module is employed to jointly encodes source and target domain data to learn a separable distributional space and domain-invariant features. For preserving semantic integrity, we utilize domain consistency constraint module to reconstruct the source and target domain latent distribution. During testing phase, we adopt the test-time training strategy and confidence-variance filtering module to dynamically updating the VAE encoder and classifier, facilitating the model
BibTeX
@inproceedings{aaai2026_outofcontextmisi,
title = {Out-of-Context Misinformation Detection via Variational Domain-Invariant Learning with Test-Time Training},
author = {Xi Yang and Han Zhang and Zhijian Lin and Yibiao Hu and Hong Han},
booktitle = {AAAI 2026},
year = {2026}
}