Authors - Juttiga Rohita, B Teja Sree, Ibrapatnam Anusha, Mohammad Sharmila Begum, Nirjogi Mahathi Abstract - Social media platforms have become increasingly vulnerable to online threats, making safeguarding the internet an increasingly difficult task. Why? This project showcases an artificial intelligence-powered system that can detect and filter out inappropriate text and images in real-time. Machine learning and natural language processing (NLP) are utilized by the system to detect hate speech, toxic terminology such as slang, and explicit imagery while maintaining document integrity. TF-IDF, LSA, and Word Embeddings are utilized in text filtering to improve the understanding of context. In image filtering, deep learning models using convolutional neural networks (CNNs) and pre-trained NSFW classifiers detect and remove explicit content. This balances scale with accuracy and provides a robust, automated content moderation system that improves both safety and compliance on the Internet.
