Towards Comprehensive Detection of Chinese Harmful Memes

Junyu Lu, Bo Xu, Xiaokun Zhang, WangHongbo, Haohao Zhu, Dongyu Zhang, Liang Yang, Hongfei Lin

Keywords: Chinese harmful meme; Fine-grained dataset; Baseline detector

Abstract:

Harmful memes have proliferated on the Chinese Internet, while research on detecting Chinese harmful memes significantly lags behind due to the absence of reliable datasets and effective detectors.
To this end, we present the comprehensive detection of Chinese harmful memes.
We introduce ToxiCN MM, the first Chinese harmful meme dataset, which consists of 12,000 samples with fine-grained annotations for meme types.
Additionally, we propose a baseline detector, Multimodal Knowledge Enhancement (MKE), designed to incorporate contextual information from meme content, thereby enhancing the model's understanding of Chinese memes.
In the evaluation phase, we conduct extensive quantitative experiments and qualitative analyses on multiple baselines, including LLMs and our MKE.
Experimental results indicate that detecting Chinese harmful memes is challenging for existing models, while demonstrating the effectiveness of MKE.

Submission Number: 1421