Harmfully Manipulated Images Matter in Multimodal Misinformation Detection

TL;DR

This paper introduces HAMI-M3D, a novel multimodal misinformation detection method that leverages manipulation and intention features, using weakly supervised learning to improve detection accuracy of harmful manipulated images.

Contribution

It proposes a new approach that incorporates manipulation and intention features with weak supervision, enhancing multimodal misinformation detection performance.

Findings

HAMI-M3D consistently improves baseline performance across datasets.

Manipulation and intention features are effective for misinformation detection.

Weakly supervised signals help overcome label scarcity in detection tasks.

Abstract

Nowadays, misinformation is widely spreading over various social media platforms and causes extremely negative impacts on society. To combat this issue, automatically identifying misinformation, especially those containing multimodal content, has attracted growing attention from the academic and industrial communities, and induced an active research topic named Multimodal Misinformation Detection (MMD). Typically, existing MMD methods capture the semantic correlation and inconsistency between multiple modalities, but neglect some potential clues in multimodal content. Recent studies suggest that manipulated traces of the images in articles are non-trivial clues for detecting misinformation. Meanwhile, we find that the underlying intentions behind the manipulation, e.g., harmful and harmless, also matter in MMD. Accordingly, in this work, we propose to detect misinformation by learning manipulation features that indicate whether the image has been manipulated, as well as intention features regarding the harmful and harmless intentions of the manipulation. Unfortunately, the manipulation and intention labels that make these features discriminative are unknown. To overcome the problem, we propose two weakly supervised signals as alternatives by introducing additional datasets on image manipulation detection and formulating two classification tasks as positive and unlabeled learning problems. Based on these ideas, we propose a novel MMD method, namely Harmfully Manipulated Images Matter in MMD (HAMI-M3D). Extensive experiments across three benchmark datasets can demonstrate that HAMI-M3D can consistently improve the performance of any MMD baselines.