Implement content moderation by using Azure Video Indexer

Content moderation is a crucial aspect of any online platform that deals with user-generated content. It helps maintain the integrity of the platform by identifying and removing inappropriate or offensive content. Microsoft Azure offers a powerful service called Video Indexer, which can be leveraged to implement robust content moderation capabilities in your application.

Getting Started

1. Create a new Video Indexer resource in the Azure portal.
2. Obtain the necessary API keys to access the Video Indexer service.
3. Install the Azure Video Indexer SDK in your development environment. You can use the SDK reference documentation to guide you through the installation process.
4. Import the necessary libraries and set up authentication using your API keys.

python
import os
from azure.cognitiveservices.vision.videoindexer import VideoIndexerClient
from azure.cognitiveservices.vision.videoindexer.models import ContentModerationDetectionFilter
from msrest.authentication import CognitiveServicesCredentials

# Create an instance of the Video Indexer client
credentials = CognitiveServicesCredentials(subscription_key)
client = VideoIndexerClient(endpoint, credentials)

Now you’re ready to implement content moderation using Azure Video Indexer.

Uploading and Analyzing Videos

5. Upload the video or videos that you want to apply content moderation to. You can use the `upload_video` method provided by the SDK.

python
# Upload a video for content moderation
video_url = 'YOUR_VIDEO_URL'
video_options = {
'video_url': video_url,
'language': 'English'
}

6. Once the video is uploaded, you can start the content moderation process. Use the `get_content_moderation` method provided by the SDK to analyze the video and retrieve the moderation results.

python
# Detect content moderation in the video
moderation_options = {
'video_id': video_id,
'filter': ContentModerationDetectionFilter(),
'language': 'English'
}

7. Analyze the moderation reviews to determine whether the content violates any guidelines. Each review contains information about the detected content, including timestamps, labels, and moderation tags. You can filter the reviews based on severity levels to focus on potentially harmful content.

python
# Process the moderation reviews
for review in moderation_reviews:
if review.is_moderator_set:
# Content violation detected
print('Violation detected at timestamp: {}'.format(review.timestamp))
print('Violation labels: {}'.format(review.label))
print('Moderator tags: {}'.format(review.moderator_tags))

Based on the moderation results, you can take appropriate actions such as flagging the content, notifying the user, or removing the content entirely.

By following these steps, you can implement content moderation using Azure Video Indexer in your application. It’s important to note that content moderation is an ongoing process, and constant monitoring and adjustment of moderation rules may be required to ensure the effectiveness of your system.

Azure Video Indexer provides a comprehensive set of features for video analysis and content moderation. You can explore additional capabilities such as speech analytics, object tracking, sentiment analysis, and more to enhance your application’s video processing capabilities.

Remember to refer to the official Azure Video Indexer documentation for additional guidance and to stay up to date with any changes or updates to the service.