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's adaptation to the target domain distribution. Extensive experiments conducted on the benchmark dataset NewsCLIPpings demonstrate that our method outperforms state-of-the-art baselines under most domain adaptation settings.

Abstract Deep learning‐based‐image denoising methods have recently achieved excellent performance by learning non‐linear mapping in the spatial domain. However, these methods fail to address the noise without specific distribution because they only use features of the spatial domain. Meanwhile, existing methods that utilize features in the frequency domain fail to combine the detailed information of both domains properly for effective reconstruction and demonstrate poor generalization. Therefore, a novel adaptive fusion dual‐domain network (AFDN) is introduced for single image restoration. Different from deep learning‐based methods, which operate on the spatial or dual‐domains in a certain order, the proposed AFDN combine the spatial‐domain image and corresponding frequency‐domain image as the input and use the interlacing dual‐domain module with flexible adaptability to learn the relationship between spatial and frequency domains. In experimental results, the AFDN is compared with several state‐of‐the‐art restoration methods. Quantitative results showed that the AFDN achieves enhanced effects and high index values. The code of this paper will be released at https://github.com/jzw0707/AFDN