Unveiling Covert Toxicity in Multimodal Data via Toxicity Association Graphs: A Graph-Based Metric and Interpretable Detection Framework

TL;DR

This work tackles covert toxicity in multimodal data by introducing Toxicity Association Graphs (TAGs) to model cross-modal semantic associations and derive an interpretable detection pathway. It contributes the first quantitative metric, Multimodal Toxicity Covertness (MTC), to measure concealment in toxic multimodal content, and a Covert Toxic Dataset (CTD) designed to stress high-covertness cases. The TA-CTD framework uses TAGs to detect toxicity with explicit reasoning paths, leading to substantial performance gains over vanilla detectors across overt and covert regimes and enabling auditable explanations. Overall, the approach advances explainable multimodal toxicity detection by combining graph-based reasoning, a principled covertness metric, and a purpose-built benchmark to evaluate subtle toxic signals.

Abstract

Detecting toxicity in multimodal data remains a significant challenge, as harmful meanings often lurk beneath seemingly benign individual modalities: only emerging when modalities are combined and semantic associations are activated. To address this, we propose a novel detection framework based on Toxicity Association Graphs (TAGs), which systematically model semantic associations between innocuous entities and latent toxic implications. Leveraging TAGs, we introduce the first quantifiable metric for hidden toxicity, the Multimodal Toxicity Covertness (MTC), which measures the degree of concealment in toxic multimodal expressions. By integrating our detection framework with the MTC metric, our approach enables precise identification of covert toxicity while preserving full interpretability of the decision-making process, significantly enhancing transparency in multimodal toxicity detection. To validate our method, we construct the Covert Toxic Dataset, the first benchmark specifically designed to capture high-covertness toxic multimodal instances. This dataset encodes nuanced cross-modal associations and serves as a rigorous testbed for evaluating both the proposed metric and detection framework. Extensive experiments demonstrate that our approach outperforms existing methods across both low- and high-covertness toxicity regimes, while delivering clear, interpretable, and auditable detection outcomes. Together, our contributions advance the state of the art in explainable multimodal toxicity detection and lay the foundation for future context-aware and interpretable approaches. Content Warning: This paper contains examples of toxic multimodal content that may be offensive or disturbing to some readers. Reader discretion is advised.

Figures (7)

• Figure 1: Image-text examples with increasing covertness levels:(a) both modalities are toxic, (b) only one modality is toxic, (c) both modalities are non-toxic.
• Figure 2: Workflow of TA-CTD and computation of Multimodal Toxicity Covertness score.
• Figure 3: The overview of our multi-agent-based data generation pipeline.
• Figure 4: Toxic taxonomy of CTD.
• Figure 5: MTC score Distribution across datasets.

Theorems & Definitions (1)

• Definition 1: Toxicity Association Graphs