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Leveraging Chat-Based Large Vision Language Models for Multimodal Out-Of-Context Detection

Fatma Shalabi, Hichem Felouat, Huy H. Nguyen, Isao Echizen

TL;DR

This work tackles multimodal out-of-context (OOC) detection, where images and captions are mismatched to mislead readers. It evaluates the zero-shot capability of LVLMs and demonstrates that their OOC detection performance improves markedly after fine-tuning on multimodal OOC data, specifically by training MiniGPT-4 on the NewsCLIPpings dataset. The authors implement a two-stage fine-tuning pipeline that ultimately yields binary Yes/No outcomes on image-caption coherence, achieving at least an 8 percentage-point gain over baselines across dataset splits. The study highlights the potential of task-specific fine-tuning for LVLM-based OOC detection while also noting limitations in interpretability and explanatory reasoning, which motivates future work toward more transparent detection frameworks.

Abstract

Out-of-context (OOC) detection is a challenging task involving identifying images and texts that are irrelevant to the context in which they are presented. Large vision-language models (LVLMs) are effective at various tasks, including image classification and text generation. However, the extent of their proficiency in multimodal OOC detection tasks is unclear. In this paper, we investigate the ability of LVLMs to detect multimodal OOC and show that these models cannot achieve high accuracy on OOC detection tasks without fine-tuning. However, we demonstrate that fine-tuning LVLMs on multimodal OOC datasets can further improve their OOC detection accuracy. To evaluate the performance of LVLMs on OOC detection tasks, we fine-tune MiniGPT-4 on the NewsCLIPpings dataset, a large dataset of multimodal OOC. Our results show that fine-tuning MiniGPT-4 on the NewsCLIPpings dataset significantly improves the OOC detection accuracy in this dataset. This suggests that fine-tuning can significantly improve the performance of LVLMs on OOC detection tasks.

Leveraging Chat-Based Large Vision Language Models for Multimodal Out-Of-Context Detection

TL;DR

This work tackles multimodal out-of-context (OOC) detection, where images and captions are mismatched to mislead readers. It evaluates the zero-shot capability of LVLMs and demonstrates that their OOC detection performance improves markedly after fine-tuning on multimodal OOC data, specifically by training MiniGPT-4 on the NewsCLIPpings dataset. The authors implement a two-stage fine-tuning pipeline that ultimately yields binary Yes/No outcomes on image-caption coherence, achieving at least an 8 percentage-point gain over baselines across dataset splits. The study highlights the potential of task-specific fine-tuning for LVLM-based OOC detection while also noting limitations in interpretability and explanatory reasoning, which motivates future work toward more transparent detection frameworks.

Abstract

Out-of-context (OOC) detection is a challenging task involving identifying images and texts that are irrelevant to the context in which they are presented. Large vision-language models (LVLMs) are effective at various tasks, including image classification and text generation. However, the extent of their proficiency in multimodal OOC detection tasks is unclear. In this paper, we investigate the ability of LVLMs to detect multimodal OOC and show that these models cannot achieve high accuracy on OOC detection tasks without fine-tuning. However, we demonstrate that fine-tuning LVLMs on multimodal OOC datasets can further improve their OOC detection accuracy. To evaluate the performance of LVLMs on OOC detection tasks, we fine-tune MiniGPT-4 on the NewsCLIPpings dataset, a large dataset of multimodal OOC. Our results show that fine-tuning MiniGPT-4 on the NewsCLIPpings dataset significantly improves the OOC detection accuracy in this dataset. This suggests that fine-tuning can significantly improve the performance of LVLMs on OOC detection tasks.
Paper Structure (16 sections, 5 equations, 6 figures, 2 tables)

This paper contains 16 sections, 5 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Vision Language Models
  4. Multimodal Misinformation Detection
  5. Leveraging Pre-trained Vision Language Models in Multimodal OOC Detection
  6. Proposed Approach
  7. Fine-tuning Large Language Models
  8. OOC Problem Formulation
  9. Fine-tuning MiniGPT-4
  10. Data Preparation
  11. Fine-tuning MiniGPT-4 for Multimodal OOC Detection
  12. Experimental and Results
  13. Dataset
  14. Experimental Settings
  15. Results and Discussion
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Key types related to misinformation islam2020deep.
  • Figure 2: Examples from our dataset show that OOC content generation arises from swapping authentic images and captions. In example A, the red caption, taken from a different context, is paired with the image. In example B, the green image is the original visual representation of the caption, while the red image is from a different context.
  • Figure 3: Illustration of our model's ability to detect contextual consistency in an image caption. Green: The image and caption are in context (Match). Red: The image and caption are out of context (Mismatch).
  • Figure 4: The workflow of our approach entails exclusively training the linear projection layer to align visual features with the Vicuna and establish weights consistent with our dataset structure. Throughout the training phase, we directed MiniGPT-4 to generate responses in a binary "Yes" or "No" format to verify the contextual relevance of the image caption relative to the provided image.
  • Figure 5: Our method achieves accuracy gains of $\geq 8\%$ across diverse classification splits of the NewsCLIPpings dataset, compared to NewsCLIPpings and MiniGPT-4 (zero-shot) classifiers.
  • ...and 1 more figures