Paper Title
Explicitnet: An Extension for Not Safe for Work Content Moderation Using Multi-Model Deep Learning

Abstract
The increase in growth and the exposure of Not Safe For Work (NSFW) content on the internet is proving to be a big issue, especially for kids and teenagers. The mental and emotional well-being of users may be negatively impacted by this content, which may also be displeasing or unprofessional to view in various situations or contexts. This research paper proposes a chrome extension that makes use of multi-model deep learning techniques to flag and moderate NSFW content on the internet. Through this extension the user's browser can be configured to blur or prevent the loading of NSFW images and videos which makes it simpler to utilize the internet in public or professional environments and helps shield users from exposure to hazardous and sensitive content. The extension is trained on a wide range of both NSFW and non-NSFW content, which includes many topics including but not limited to vulgarity, violence and gore. Using this information, the extension can identify and regulate NSFW content with precision by learning the characteristics of such content. Additionally, the extension is also designed to be lightweight and efficient, so that it does not impact the user's browsing experience. Keywords - Multimodal Deep Learning, Content Moderation , Not Safe For Work (NSFW), Extension.