AI Video File Moderation
Stream’s video moderation engine, powered by AWS Rekognition, provides real-time content analysis of video uploads through advanced AI processing. Unlike image moderation, video analysis occurs frame-by-frame asynchronously to ensure thorough content review without impacting application performance.
Key Features:
- Frame-by-frame content analysis with precise timestamps
- Detect inappropriate content, violence, and explicit material
- Analyze video frames for policy violations
- Moderate video content at scale
- Receive frame-by-frame analysis with timestamps
- Get confidence scores for detected violations
Supported Categories
Our video moderation engine analyzes frames to detect:
Explicit Content:
- Pornographic material
- Explicit sexual acts
Non-Explicit Nudity:
- Exposed intimate body parts
- Intimate physical contact
Swimwear/Underwear:
- People in revealing swimwear
- Underwear detection
Violence & Visually Disturbing:
- Fighting and physical aggression
- Weapons
- Gore and blood
- Disturbing imagery
Substances:
- Drug-related content and paraphernalia
- Tobacco products
- Alcoholic beverages
Inappropriate Content:
- Rude gestures
- Offensive body language
- Gambling-related content
- Hate symbols and extremist imagery
How It Works
When a video is uploaded:
- The video is accepted immediately to maintain low latency.
- Analysis begins asynchronously in the background.
- AI models evaluate the video against all configured categories.
- If confidence thresholds are met, configured actions are applied.
- The message or post is updated based on moderation results.
- Flagged videos appear in the Media Queue for moderator review.
- Receive frame-by-frame analysis with timestamps.
Best Practices
For optimal video moderation:
- Configure confidence scores based on your tolerance for false positives
- Use the “Flag” action for borderline cases that need human review
- Use “Block” for clearly inappropriate content
- Monitor the Media Queue regularly to validate automated decisions
- Review frame-by-frame analysis with timestamps
- Adjust thresholds based on observed accuracy
- Consider your audience and community standards when configuring rules
On this page: