Harmful Visual Content Manipulation Matters in Misinformation Detection Under Multimedia Scenarios

2026-03-22

AI Summary

This work addresses a critical limitation in existing multimodal misinformation detection approaches, which often overlook whether visual content has been manipulated and the intent behind such manipulation—be it harmful or benign. To bridge this gap, the study introduces, for the first time, the notion of manipulative intent as an intrinsic attribute in this task. It proposes a weakly supervised learning framework that leverages auxiliary signals from image manipulation detection datasets to model both manipulation characteristics and their underlying intent through Positive-Unlabeled (PU) learning. By doing so, the method effectively mitigates the scarcity of labeled data in real-world scenarios and achieves consistent and significant performance improvements across four widely used multimodal misinformation detection benchmarks.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Nowadays, the widespread dissemination of misinformation across numerous social media platforms has led to severe negative effects on society. To address this challenge, the automatic detection of misinformation, particularly under multimedia scenarios, has gained significant attention from both academic and industrial communities, leading to the emergence of a research task known as Multimodal Misinformation Detection (MMD). Typically, current MMD approaches focus on capturing the semantic relationships and inconsistency between various modalities but often overlook certain critical indicators within multimodal content. Recent research has shown that manipulated features within visual content in social media articles serve as valuable clues for MMD. Meanwhile, we argue that the potential intentions behind the manipulation, e.g., harmful and harmless, also matter in MMD. Therefore, in this study, we aim to identify such multimodal misinformation by capturing two types of features: manipulation features, which represent if visual content has been manipulated, and intention features, which assess the nature of these manipulations, distinguishing between harmful and harmless intentions. Unfortunately, the manipulation and intention labels that supervise these features to be discriminative are unknown. To address this, we introduce two weakly supervised indicators as substitutes by incorporating supplementary datasets focused on image manipulation detection and framing two different classification tasks as positive and unlabeled learning issues. With this framework, we introduce an innovative MMD approach, titled Harmful Visual Content Manipulation Matters in MMD (HAVC-M4 D). Comprehensive experiments conducted on four prevalent MMD datasets indicate that HAVC-M4 D significantly and consistently enhances the performance of existing MMD methods.

Problem

• Multimodal Misinformation Detection
• Visual Content Manipulation
• Harmful Intentions
• Weakly Supervised Learning
• Misinformation Detection

Innovation

• multimodal misinformation detection
• visual content manipulation
• harmful intent classification
• weakly supervised learning
• positive and unlabeled learning