Training-Free and Interpretable Hateful Video Detection via Multi-stage Adversarial Reasoning

TL;DR

MARS is a training-free, multi-stage adversarial reasoning framework that enhances hateful video detection by providing reliable, interpretable, and explainable results, outperforming existing methods on real-world datasets.

Contribution

This paper introduces MARS, a novel training-free framework that combines evidence and counter-evidence reasoning for interpretable hateful video detection.

Findings

Achieves up to 10% improvement over other training-free methods.

Outperforms state-of-the-art training-based methods on one dataset.

Provides human-understandable justifications for decisions.

Abstract

Hateful videos pose serious risks by amplifying discrimination, inciting violence, and undermining online safety. Existing training-based hateful video detection methods are constrained by limited training data and lack of interpretability, while directly prompting large vision-language models often struggle to deliver reliable hate detection. To address these challenges, this paper introduces MARS, a training-free Multi-stage Adversarial ReaSoning framework that enables reliable and interpretable hateful content detection. MARS begins with the objective description of video content, establishing a neutral foundation for subsequent analysis. Building on this, it develops evidence-based reasoning that supports potential hateful interpretations, while in parallel incorporating counter-evidence reasoning to capture plausible non-hateful perspectives. Finally, these perspectives are synthesized into a conclusive and explainable decision. Extensive evaluation on two real-world datasets shows that MARS achieves up to 10% improvement under certain backbones and settings compared to other training-free approaches and outperforms state-of-the-art training-based methods on one dataset. In addition, MARS produces human-understandable justifications, thereby supporting compliance oversight and enhancing the transparency of content moderation workflows. The code is available at https://github.com/Multimodal-Intelligence-Lab-MIL/MARS.