I know what you MEME! Understanding and Detecting Harmful Memes with Multimodal Large Language Models

—Memes have become a double-edged sword on social media platforms. On one hand, they facilitate the rapid dissemination of information and enhance communication. On the other hand, memes pose a risk of spreading harmful content under the guise of humor and virality. This duality highlights the need to develop effective moderation tools capable of identifying harmful memes. Current detection methods, however, face significant challenges in identifying harmful memes due to their inherent complexity. This complexity arises from the diverse forms of expression, intricate compositions, sophisticated propaganda techniques, and varied cultural contexts in which memes are created and circulated. These factors make it difficult for existing algorithms to distinguish between harmless and harmful content accurately. To understand and address these challenges, we first conduct a comprehensive study on harmful memes from two novel perspectives: visual arts and propaganda techniques . It aims to assess existing tools for detecting harmful memes and understand the complexities inherent in them. Our findings demonstrate that meme compositions and propaganda techniques can significantly diminish the effectiveness of current harmful meme detection methods. Inspired by our observations and understanding of harmful memes, we propose a novel framework called HM-G UARD for effective detection of harmful memes. HMG UARD utilizes adaptive prompting and chain-of-thought (CoT) reasoning in multimodal large language models (MLLMs). HMG UARD has demonstrated remarkable performance on the public harmful meme dataset, achieving an accuracy of 0.92. Compared to the baseline, HMG UARD represents a substantial improvement, with accuracy exceeding the baselines by 15% to 79.17%