CVMMJul 18, 2024

Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?

arXiv:2407.13488v114 citationsh-index: 16Has Code
Originality Incremental advance
AI Analysis

This work addresses the challenge of multimodal misinformation detection for fact-checking, but it is incremental as it highlights issues with current approaches rather than proposing a fundamentally new solution.

The paper tackles the problem of detecting out-of-context misinformation in multimodal content by introducing a simple baseline method (MUSE) that measures image-text similarity, which competes with or surpasses state-of-the-art methods on NewsCLIPpings and VERITE datasets, and when integrated into a transformer model, improves performance by 3.3% and 7.5% respectively.

Out-of-context (OOC) misinformation poses a significant challenge in multimodal fact-checking, where images are paired with texts that misrepresent their original context to support false narratives. Recent research in evidence-based OOC detection has seen a trend towards increasingly complex architectures, incorporating Transformers, foundation models, and large language models. In this study, we introduce a simple yet robust baseline, which assesses MUltimodal SimilaritiEs (MUSE), specifically the similarity between image-text pairs and external image and text evidence. Our results demonstrate that MUSE, when used with conventional classifiers like Decision Tree, Random Forest, and Multilayer Perceptron, can compete with and even surpass the state-of-the-art on the NewsCLIPpings and VERITE datasets. Furthermore, integrating MUSE in our proposed "Attentive Intermediate Transformer Representations" (AITR) significantly improved performance, by 3.3% and 7.5% on NewsCLIPpings and VERITE, respectively. Nevertheless, the success of MUSE, relying on surface-level patterns and shortcuts, without examining factuality and logical inconsistencies, raises critical questions about how we define the task, construct datasets, collect external evidence and overall, how we assess progress in the field. We release our code at: https://github.com/stevejpapad/outcontext-misinfo-progress

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes