Abstract
Hateful memes are an escalating issue in the digital landscape, demanding innovative solutions for their effective detection and classification. These memes often employ subtlety, sarcasm, and symbolism, presenting formidable challenges for automated detection systems. Moreover, the linguistic and cultural diversity of the internet, transcending geographical and language boundaries, further complicates the task. This research paper presents a comprehensive approach to hateful meme detection, utilizing a Dual Stream Transformer Model, real-world knowledge integration, characteristic detection, and cultural reference understanding. We emphasize the importance of ethics and responsible usage in deploying such technology, underscoring its potential for positive societal impact
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