Automating Safer Experiences with Amazon Rekognition Content Moderation

Overview

Online platforms depend on user-generated photos and videos, but every upload can contain explicit, violent, or otherwise disturbing content. Manually reviewing everything doesn’t scale, and letting unsafe content slip through harms both users and brand reputation. Amazon Rekognition Content Moderation uses pre-trained machine learning models to scan images and videos for unsafe content, so you can automate most of the work and pay only for what you analyze.

Why Amazon Rekognition Content Moderation?

Amazon Rekognition Content Moderation is designed to:

• Automate moderation at scale by leveraging ML models exposed via APIs to detect and flag inappropriate or unwanted content.
• Enhance user and brand safety by screening assets ranging from a few to millions against predefined or custom unsafe categories.
• Reduce manual effort and cost by automatically flagging most unsafe content, allowing humans to focus on the remaining edge cases.
• Avoid upfront investment with pricing based on analyzed images and video duration, featuring no licenses or minimums.

What Amazon Rekognition Can Detect?

The service identifies many types of problematic visual content in images and videos, including:

• Explicit and suggestive adult content
• Non-explicit nudity
• Violence and weapons (including graphic scenes)
• Drugs and tobacco
• Alcohol use and beverages
• Gambling
• Hate symbols
• Visually disturbing or shocking material

Each detection returns labels with confidence scores. For videos, results also include timestamps so you know when unsafe content appears.

How the Moderation APIs Work?

Amazon Rekognition provides separate flows for images and videos, with synchronous and asynchronous options.

Images:

• DetectModerationLabels (sync) – Analyzes a JPEG or PNG image using raw bytes or an Amazon S3 reference.
• StartMediaAnalysisJob / GetMediaAnalysisJob (async) – Run large-scale image moderation jobs.

Videos:

• StartContentModeration – Starts a job to analyze a stored video for unsafe content.
• GetContentModeration – Retrieves labels, confidence scores, and per-frame or segment timestamps.

Taxonomy, Content Types, and Confidence

A key feature is the three-level taxonomy for labels:

• L1 – Broad top-level categories (for example, explicit content or non-explicit nudity).
• L2 – More specific subcategories.
• L3 – Very fine-grained labels for detailed situations.

Each label includes a TaxonomyLevel field. Most customers focus on L1 and L2 for policy decisions, using L3 only when they need extra precision.

The APIs can also distinguish between animated content (games, cartoons, anime, comics) and illustrated content (drawings, paintings, sketches), allowing you to apply different rules to stylized versus real-world imagery.

Every label includes a confidence score. If you don’t specify MinConfidence, only labels with at least 50% confidence are returned; lowering this value increases recall but may raise false positives, while increasing it tightens moderation at the risk of missing borderline cases.

Human Review and Custom Moderation

Amazon Rekognition is designed for human-in-the-loop moderation, not full replacement of reviewers. In a typical workflow:

• The model automatically filters out most unsafe content.
• Human moderators review only a small percentage of items focused on ambiguous or high-impact cases.

You can integrate Amazon Augmented AI (Amazon A2I) to route low-confidence or sensitive items for manual review.

For domain-specific policies, Rekognition supports Custom Moderation: you upload and label your own images, train a custom moderation adapter, and reference it from DetectModerationLabels. Adapters progress through lifecycle states such as TRAINING_IN_PROGRESS, TRAINING_COMPLETED, DEPRECATED, and EXPIRED.

Common Use Cases

Typical scenarios for Amazon Rekognition Content Moderation include:

• Social media and communities – Filter photos, short videos, and chat media on social, content-sharing, gaming, and dating platforms.
• Gaming platforms – Moderate avatars, screenshots, and profile images, and reduce harassment-driven churn.
• E-commerce – Block illegal or inappropriate images in product listings and reviews to maintain trust and compliance.
• Advertising and brand safety – Verify creatives to ensure brands avoid unsafe associations and comply with internal and regulatory standards.
• Media and entertainment – Protect audiences from disturbing content and maintain a healthy community on streaming and media sites.

Amazon Rekognition can also contribute to age-gated experiences by combining Face Liveness checks with age estimation to help ensure only suitable users access restricted content.

Responsible Use and Limitations

AWS is clear that Amazon Rekognition is not an authority on what is or isn’t offensive and is not an exhaustive filter of unsafe content. It also does not decide whether content is illegal, including highly sensitive material. Instead, it provides labels and confidence scores, which you must map to your own policies. You remain responsible for defining acceptable content, setting thresholds for blocking or review, and keeping humans in the loop for edge cases and appeals.

Conclusion

Amazon Rekognition Content Moderation offers a ready-to-use, ML-powered toolkit for detecting risky content in images and videos.

When integrated thoughtfully into your workflows, with clear policies, logging, and human oversight, it can dramatically reduce manual review workloads, enhance user safety, and protect your brand while maintaining full control over what constitutes “safe” for your platform.

Drop a query if you have any questions regarding Amazon Rekognition Content Moderation and we will get back to you quickly.

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