Detecting Mismatched Text-Image Pairs in Misinformation Using Neural-Symbolic-Enhanced Large Multimodal Model

Core Concepts

The proposed model can efficiently detect out-of-context misinformation by extracting and evaluating the consistency between textual and visual information using a neural-symbolic framework.

Abstract

The paper proposes an interpretable cross-modal misinformation detection model that can jointly output predictions and supporting "evidence" based on a neural-symbolic approach.
Key highlights:

The model first parses the text into an abstract meaning representation (AMR) graph and extracts a set of elementary statements (queries) from the graph.
These queries are then forwarded into a large pre-trained vision-language model to determine whether they are supported by the vision input or not.
A query ranker is designed to filter out the most reliable and important queries to make the final prediction.
The queries that support the final prediction are output as evidence, making the model's decisions interpretable.
Experiments show the proposed model outperforms state-of-the-art baselines in accuracy while providing interpretable evidence.
The model addresses the limitations of existing neural-symbolic multi-modal models by only converting the text modality to semantic graphs and leveraging the knowledge in large pre-trained models to capture complicated semantics without requiring scene graph annotations.

Stats

"Increasing exploits of multimedia misinformation (e.g., news with edited images and/or machine-generated text) are threatening the credibility and reliability of online information."
"Text-image de-contextualization (Jaiswal et al., 2017) is one of their tools to evade being detected. The misinformation campaigns can recruit experienced human writers to imitate the wording of true news and use unedited but mismatched images."
"The latent space usually lacks interpretability. In other words, we have no idea about the physical meaning of each dimension in the latent space."

Quotes

"To address this challenge, in this paper we explore how to achieve interpretable cross-modal de-contextualization detection that simultaneously identifies the mismatched pairs and the cross-modal contradictions, which is helpful for fact-check websites to document clarifications."
"To address the missing-detail issue and the complicated semantic challenge, we propose a novel multi-modal neural-symbolic learning framework."

Key Insights Distilled From

Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model

by Yizhou Zhang... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2304.07633.pdf

Deeper Inquiries

How can the proposed model be extended to detect more sophisticated forms of misinformation beyond text-image mismatches, such as deepfakes or coordinated disinformation campaigns?

The proposed model can be extended to detect more sophisticated forms of misinformation by incorporating additional modalities and features. For detecting deepfakes, the model can be enhanced to analyze audio components in conjunction with visual and textual information. By integrating audio analysis techniques, such as voice recognition and speech pattern analysis, the model can identify discrepancies between the audio track and the accompanying content, flagging potential deepfake instances.
To address coordinated disinformation campaigns, the model can be augmented with network analysis capabilities to detect patterns of coordinated behavior across multiple accounts or platforms. By analyzing the dissemination patterns of misinformation and identifying clusters of accounts sharing similar content, the model can uncover orchestrated campaigns aimed at spreading false information.
Furthermore, the model can leverage natural language processing techniques to analyze the linguistic style and sentiment of the content, helping to identify propaganda or biased narratives. By incorporating sentiment analysis and linguistic pattern recognition, the model can detect subtle cues indicative of manipulative or misleading information.

What are the potential limitations of relying on large pre-trained models for the query answering component, and how could these be addressed?

Relying solely on large pre-trained models for the query answering component may pose several limitations. One key limitation is the potential bias present in the pre-trained models, which can lead to biased or inaccurate responses to queries. To address this, fine-tuning the pre-trained models on domain-specific data related to misinformation detection can help mitigate bias and improve the accuracy of query responses.
Another limitation is the lack of transparency and interpretability in the decision-making process of large pre-trained models. To enhance interpretability, techniques such as attention mechanisms and explainable AI methods can be employed to provide insights into how the model arrives at its answers. By visualizing the attention weights or decision-making process, users can better understand the rationale behind the model's responses.
Additionally, the computational resources required to deploy and maintain large pre-trained models can be a limitation. To address this, model compression techniques, such as knowledge distillation or pruning, can be applied to reduce the model size and computational overhead while maintaining performance. By optimizing the model architecture and parameters, the efficiency of the query answering component can be improved.

How could the interpretable evidence generated by the model be further leveraged to provide actionable insights for fact-checking and content moderation efforts?

The interpretable evidence generated by the model can be leveraged to provide actionable insights for fact-checking and content moderation efforts in several ways:

Automated Flagging: The model-generated evidence can be used to automatically flag potentially misleading content for further review by fact-checkers. By highlighting specific inconsistencies or factual errors, the evidence can expedite the identification of misinformation.

Evidence Documentation: The model can generate detailed reports documenting the evidence supporting its predictions. These reports can serve as a reference for fact-checkers, enabling them to verify the accuracy of the model's findings and streamline the fact-checking process.

User Alerts: The interpretable evidence can be used to provide users with alerts or notifications about potentially deceptive content. By presenting users with transparent evidence of misinformation, platforms can empower users to make informed decisions about the content they engage with.

Content Removal: In cases where the evidence clearly indicates the presence of misinformation, content moderation teams can use this information to prioritize the removal of harmful or deceptive content. The interpretable evidence can guide content moderation efforts and help maintain the integrity of online information ecosystems.