Stream Guard: Automated Content Moderation for Live Streams and Videos Using AWS Rekognition and Transcribe

Description

Stream Guard is an automated content moderation platform that uses AWS Rekognition for visual analysis and AWS Transcribe for speech-to-text to detect inappropriate content in live streams and uploaded videos. It integrates with multiple streaming platforms, provides customizable content detection, and offers real-time alerts and detailed reports for effective content management.

Practical Use Case and User Story

As a content moderator, I need Stream Guard to monitor live streams on platforms like YouTube, Twitch, or Facebook Live, ensuring compliance with community guidelines in real-time. It should also maintain professionalism in corporate and educational events and automatically scan user-generated content for violations before publishing. This will help maintain a safe and appropriate environment across various streaming and content-sharing platforms.

Tech Stack Involved

Selenium Python: Automates the process of connecting and scraping streams from multiple platforms.

AWS Transcribe: Converts speech from video into text for analyzing offensive language and inappropriate verbal content.

AWS Rekognition: Detects visual anomalies such as nudity, smoking, fire, and other inappropriate visuals.

Custom Labeling: Allows for tailored content moderation rules to suit specific industry needs.

Demo

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