Resilient Data Augmentation Approaches to Multimodal Verification in the News Domain

Abstract

With the advent of generative adversarial networks and misinformation in social media, there has been increased interest in multimodal verification. Image-text verification typically involves determining whether a caption and an image correspond with each other. Building on multimodal embedding techniques, we show that data augmentation via two distinct approaches improves results: entity linking and cross-domain local similarity scaling. We refer to the approaches as resilient because we show state-of-the-art results against manipulations specifically designed to thwart the exact multimodal embeddings we are using as the basis for all of our features.