When Harmful Content Gets Camouflaged: Unveiling Perception Failure of LVLMs with CamHarmTI

TL;DR

This paper introduces CamHarmTI, a benchmark revealing that current LVLMs struggle to detect camouflaged harmful content in text-image posts, highlighting perceptual gaps compared to humans and suggesting avenues for model improvement.

Contribution

The paper presents CamHarmTI, a new benchmark for evaluating LVLM perception of camouflaged harmful content, and demonstrates how fine-tuning improves model sensitivity and understanding.

Findings

Humans recognize camouflaged harmful content with over 95.75% accuracy.

Current LVLMs perform poorly, with ChatGPT-4o achieving only 2.10% accuracy.

Fine-tuning increases model accuracy by 55.94% and enhances early-layer sensitivity.

Abstract

Large vision-language models (LVLMs) are increasingly used for tasks where detecting multimodal harmful content is crucial, such as online content moderation. However, real-world harmful content is often camouflaged, relying on nuanced text-image interplay, such as memes or images with embedded malicious text, to evade detection. This raises a key question: \textbf{can LVLMs perceive such camouflaged harmful content as sensitively as humans do?} In this paper, we introduce CamHarmTI, a benchmark for evaluating LVLM ability to perceive and interpret camouflaged harmful content within text-image compositions. CamHarmTI consists of over 4,500 samples across three types of image-text posts. Experiments on 100 human users and 12 mainstream LVLMs reveal a clear perceptual gap: humans easily recognize such content (e.g., over 95.75\% accuracy), whereas current LVLMs often fail (e.g., ChatGPT-4o achieves only 2.10\% accuracy). Moreover, fine-tuning experiments demonstrate that \bench serves as an effective resource for improving model perception, increasing accuracy by 55.94\% for Qwen2.5VL-7B. Attention analysis and layer-wise probing further reveal that fine-tuning enhances sensitivity primarily in the early layers of the vision encoder, promoting a more integrated scene understanding. These findings highlight the inherent perceptual limitations in LVLMs and offer insight into more human-aligned visual reasoning systems.