SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection

TL;DR

SNIFFER is a multimodal large language model designed to detect and explain out-of-context misinformation by assessing image-text consistency and leveraging external knowledge, significantly improving accuracy and interpretability.

Contribution

The paper introduces SNIFFER, a novel two-stage instruction-tuned multimodal model that enhances out-of-context misinformation detection and explanation capabilities.

Findings

SNIFFER surpasses original MLLM by over 40% in detection accuracy.

It outperforms state-of-the-art methods in misinformation detection.

Provides accurate and persuasive explanations validated by evaluations.

Abstract

Misinformation is a prevalent societal issue due to its potential high risks. Out-of-context (OOC) misinformation, where authentic images are repurposed with false text, is one of the easiest and most effective ways to mislead audiences. Current methods focus on assessing image-text consistency but lack convincing explanations for their judgments, which is essential for debunking misinformation. While Multimodal Large Language Models (MLLMs) have rich knowledge and innate capability for visual reasoning and explanation generation, they still lack sophistication in understanding and discovering the subtle crossmodal differences. In this paper, we introduce SNIFFER, a novel multimodal large language model specifically engineered for OOC misinformation detection and explanation. SNIFFER employs two-stage instruction tuning on InstructBLIP. The first stage refines the model's concept alignment of generic objects with news-domain entities and the second stage leverages language-only GPT-4 generated OOC-specific instruction data to fine-tune the model's discriminatory powers. Enhanced by external tools and retrieval, SNIFFER not only detects inconsistencies between text and image but also utilizes external knowledge for contextual verification. Our experiments show that SNIFFER surpasses the original MLLM by over 40% and outperforms state-of-the-art methods in detection accuracy. SNIFFER also provides accurate and persuasive explanations as validated by quantitative and human evaluations.