Moderate your Amazon IVS live stream using Amazon Rekognition

Amazon IVS is a managed live streaming solution that simplifies the setup and management of interactive video experiences. The need for effective content moderation in live streaming has become more crucial. Amazon Rekognition Content Moderation automates image and video moderation workflows. This solution integrates with Amazon IVS and provides options for automated moderation and human review. Thumbnail images from the live stream are sent to the Amazon Rekognition Image Moderation API for analysis. The solution includes a rules engine and a monitoring and management dashboard for human moderators.

 Moderate your Amazon IVS live stream using Amazon Rekognition

Streamline Your Content Moderation with Amazon IVS and Amazon Rekognition

As live streaming becomes increasingly popular, the need for effective content moderation is more important than ever. User-generated content (UGC) presents complex challenges for safety, and relying solely on human moderators can be time-consuming, prone to errors, and difficult to scale. That’s where automated moderation solutions come in.

Amazon Rekognition Content Moderation, a capability of Amazon Rekognition, offers an automated and streamlined approach to image and video moderation. With this solution, you can moderate live stream visual content in near-real-time, combining the power of machine learning with human review.

Moderate Live Stream Visual Content

The most common approach to UGC live stream visual moderation involves sampling images from the stream and utilizing image moderation to receive near-real-time results. Platforms can establish different rules to match their policies effectively, combining human and automatic review. Certain streams will be stopped automatically, but human moderators will also assess whether a stream violates platform policies and should be deactivated.

By using the Amazon Rekognition Image API, you can easily implement this moderation solution in your AWS account. It allows you to define moderation rules, customize sample frequency, and optimize cost by avoiding processing redundant images.

Record Amazon IVS Stream Content to Amazon S3

Amazon IVS offers native solutions for recording stream content to an Amazon Simple Storage Service (Amazon S3) bucket and generating thumbnails. You can customize the image quality and frequency of thumbnails. By creating a recording configuration and linking it to an Amazon IVS channel, the channel’s live streams are automatically recorded to the specified S3 bucket.

Amazon Rekognition Moderation APIs

The solution utilizes the Amazon Rekognition DetectModerationLabel API to moderate Amazon IVS thumbnails in near-real-time. This API provides pre-trained models to analyze a wide range of inappropriate or offensive content, such as violence, nudity, and hate symbols.

Here’s an example of how to call the API using the Python Boto3 library:

import boto3

# Initialize the Amazon Rekognition client object
rekognition = boto3.client('rekognition')

# Call the Rekognition Image moderation API
response = rekognition.detect_moderation_labels(
 Image={'S3Object': {'Bucket': data_bucket, 'Name': s3_key}}
)

The response will include moderation labels with confidence scores, allowing you to take appropriate actions based on the results.

Solution Overview

This solution integrates with Amazon IVS by reading thumbnail images from an S3 bucket and sending them to the Amazon Rekognition Image Moderation API. It provides options for automatically stopping streams and human-in-the-loop review. You can configure rules for the system to automatically halt streams based on conditions and manage violation alerts through a user-friendly web portal.

The solution uses a microservices architecture, consisting of a rules engine and a monitoring and management dashboard. The rules engine evaluates images against moderation rules and alerts human moderators when violations occur. The monitoring and management dashboard allows moderators to monitor live streams, dismiss warnings, and take necessary actions.

With this solution, you can achieve near-real-time moderation, optimize cost, and ensure compliance with platform policies. It’s a practical and efficient way to streamline your content moderation operations.

For more information and detailed instructions on deploying this solution, refer to the GitHub repository.

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