摘要：仇恨表情包是一种日益增长的多模态在线伤害形式，其中的敌意意图通常需通过联合解读图像、文本、文化指涉和隐含目标来识别。尽管高资源语言中的仇恨表情包检测已有进展，阿拉伯语仍缺乏充分研究，现有的表情包资源主要聚焦于宣传内容或粗粒度的有害内容标签。我们提出 AHA-Memes（Arabic HAteful Memes），据我们所知，这是首个具备细粒度、多标签标注的大规模阿拉伯语仇恨表情包基准。该数据集包含 5K 条人工标注的表情包，采用能够捕捉仇恨类型（即攻击策略）的分类体系。我们还提供约 66K 条银标注表情包，以支持未来研究。我们对纯文本、纯图像和后期融合多模态模型进行了基准评测，同时评测了少样本上下文学习（ICL），以及开放权重和闭源权重的视觉语言模型（VLMs）在零样本和微调设定下的表现。结果建立了有力的基线，并凸显了在具备文化语境的阿拉伯语仇恨表情包检测中面临的关键挑战。我们发布数据集、标注指南和评测脚本，以支持未来研究。警告：本文包含可能令读者感到不适的示例。

Abstract:Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implicit targets. While hateful meme detection has advanced in high-resource languages, Arabic remains underexplored, with existing meme resources focusing mainly on propaganda or coarse harmful-content labels. We introduce AHA-Memes (Arabic HAteful Memes), which is, to our knowledge, the first large-scale Arabic hateful meme benchmark with fine-grained, multi-label annotations. The dataset includes 5K manually annotated memes using a taxonomy that captures hate types, i.e., attack strategies. We further provide ~66K silver-labeled memes to support future studies. We benchmark text-only, image-only, and late-fusion multimodal models, as well as few-shot in-context learning (ICL) and open- and closed-weight Vision-Language Models (VLMs) under zero-shot and fine-tuning settings. Our results establish strong baselines and highlight key challenges in culturally grounded Arabic hateful meme detection. We release the dataset, annotation guidelines, and evaluation scripts to support future research. WARNING: This paper contains examples that may be disturbing to readers.