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From Meme to Threat: On the Hateful Meme Understanding and Induced Hateful Content Generation in Open-Source Vision Language Models

Name: From Meme to Threat: On the Hateful Meme Understanding and Induced Hateful Content Generation in Open-Source Vision Language Models
Creator: Ma, Yihan

descriptionPublicationkeyboard_double_arrow_right Project deliverable 01 Jan 2025Publisher:Zenodo

Authors: Ma, Yihan;

doi: 10.5281/zenodo.14752660 , 10.5281/zenodo.14752805 , 10.5281/zenodo.14720651 , 10.5281/zenodo.14720650

From Meme to Threat: On the Hateful Meme Understanding and Induced Hateful Content Generation in Open-Source Vision Language Models

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Abstract

This repository contains the source code, datasets, generated content, and scripts for the paper "From Meme to Threat: On the Hateful Meme Understanding and Induced Hateful Content Generation in Open-Source Vision Language Models." In this paper, we present an in-depth evaluation of VLMs' ability to interpret hateful memes by curating a dataset of 39 hateful memes and over 12,000 responses from seven representative VLMs using carefully designed prompts. We also assess how malicious users could exploit VLMs and hateful memes to generate hateful content systematically.

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