Amazon Rekognition Content Moderation

Automate and streamline content moderation workflows with machine learning

Amazon Rekognition Content Moderation automates and streamlines your image and video moderation workflows using machine learning (ML), without requiring ML experience. Process millions of images and videos efficiently while detecting inappropriate or unwanted, with fully managed APIs and customizable moderation rules to keep users safe and the business compliant. Pay only for what you use, without minimum fees, licenses, or upfront commitments.

What's Amazon Rekognition Content Moderation? (1:18)


Improves user and brand safety

Review a few or millions of images and videos against various predefined or business-specific unsafe categories. Proactively ensure that your users and brand sponsors are not exposed to unwanted or inappropriate content.

Automate moderation

Enable human reviewers to follow up on smaller subsets of content, and protect them from harmful content exposure by automatically flagging up to 95% of unsafe content. Integrate human review without building new tools and infrastructure with Amazon Augmented AI (Amazon A2I).

Increase reliability and reduce costs

Create cost-reliable, scalable, and repeatable cloud-based content moderation workflows without upfront commitments or expensive licenses. Pay based on the number of images or the duration of videos processed.


Detect and label explicit images and videos

Detect explicit adult or suggestive content, violence, drugs, tobacco, alcohol, hate symbols, gambling, and disturbing content in images and videos. Mark each detected label and video timestamp with confidence scores. Use a hierarchical taxonomy to create granular business rules for different geographies, target audiences, time of day, and more.

Customize audio and text moderation

Detect, read, and check text against your own list of prohibited words or phrases with Amazon Rekognition Text Detection. Convert speech in videos to text with Amazon Transcribe and check it for use of profanities or hate speech. Extend text analysis with the natural language processing (NLP) capabilities in Amazon Comprehend.

Verify user age

Deter underage users from accessing restricted content with Amazon Rekognition Face Liveness. For example, online gaming or dating customers can use Face Liveness and age estimation from Amazon Rekognition Facial Analysis to verify a user’s age before granting access. 

Streamline content moderation operations

Access automation and artificial intelligence (AI) capabilities to implement a reliable content moderation solution without requiring ML expertise. Create safe online environments, protect your brand, and minimize moderation costs. For more information, see Streamline Content Moderation Operations.

Train and deploy custom models

Easily train and deploy your own moderation models with a few clicks or API calls. Quickly create and operationalize new models with Amazon Rekognition Custom Labels to address real-time scenarios, such as removing offensive messages from online stores or blurring logos on a live broadcast.

Enhance predictions with human reviews

Enhance predictions further with strategic human intervention. Integrate Amazon A2I with Amazon Rekognition moderation APIs to help your teams or a third-party vendor make final judgments whenever low-confidence predictions require human intervention.

Use cases

Social media

Protect users from exposure to inappropriate content on content sharing platforms. Proactively moderate large volumes of user uploads to keep users and communities safe from inappropriate content on social media platforms and services including photo and video sharing, online gaming, video streaming, and online dating apps.


Prevent offensive or controversial imagery in gaming forums, live gaming, and video streaming services. Additionally, moderate user-generated content such as profiles and avatars, keep gamers engaged and active, and prevent harassment and bully-causing user churn.


Enhance customer trust by keeping illegal or inappropriate image and video content associated with third-party product listings and reviews off your digital shelves, fostering a secure and transparent shopping experience. Safeguard the platform's reputation and compliance, as customers are increasingly turning to product reviews to make informed decisions in their shopping journey.


Protect brands against unwanted associations to meet compliance. Also achieve brand objectives that lead to revenue growth, such as brand elevation and likability.

Media and entertainment

Protect audiences from exposure to potentially unsafe image and video content, safeguarding user well-being, protecting intellectual property, and maintaining a positive community.


Moderate the contributions from students and educators to help build a safe, inclusive, and fulfilling learning experience.


Read more about our 40+ Amazon Rekognition customer stories.

CoStar Group

CoStar is a leader in commercial real estate information, analytics, technology, and news, with one of the most comprehensive data platforms on the market, processing more than 150,000 images that are uploaded to its platform daily.

“For CoStar, it is imperative that images uploaded to our platform comply with the terms of our end user agreement and do not contain inappropriate content, so that we can ensure an inclusive, safe, and data-driven user community. Amazon Rekognition's Content Moderation API enabled us to easily build a solution to automatically analyze all uploaded images, allowing us to efficiently deliver high-value products to our customers. Amazon Rekognition offers a suite pre-trained computer vision APIs, which along with content moderation, text detection, and object detection, help us further improve our product offerings by making the images we receive more discoverable and our community more inclusive. Amazon Rekognition allows us to move quickly and add AI smarts to our systems with its pre-trained models, helping us stay focused on delivering unique solutions to the real estate sector.” 

Mark Osborn, Principal Software Engineer, CoStar Group

Read success story 

Dream11 allows users to post videos and pictures and share images in group chats. The company uses Amazon Rekognition to automate the media analysis of thousands of assets each day as part of its content moderation process to protect and deliver engaging experiences to its 100 million users.

“Every decision we make is backed by data and technology, considering various metrics to continually add ‘wow factors’ that help retain customers. AWS promotes a user-first culture, with intuitive cloud-native services that help us launch things fast without any dependencies. The various AWS technology offerings help us develop our prototypes and make them live very quickly, even at a massive scale. This gives us a competitive edge in the market, where speed is essential.” 

Praveen Jain, Vice President of Engineering, Dream11

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Omlet (Mobisocial)

MobiSocial is a leading mobile software company, focused on building social networking and gaming apps. The company develops Omlet Arcade, a global community where tens of millions of mobile gaming live-streamers and esports players gather to share gameplay and meet new friends.

“In order to ensure that our gaming community is a safe environment to socialize and share entertaining content, we used machine learning to identify content that does not comply with our community standards. We created a workflow, leveraging Amazon Rekognition, to flag uploaded image and video content that contains non-compliant content. Amazon Rekognition’s Content Moderation API helps us achieve the accuracy and scale to manage a community of millions of gaming creators worldwide. Since implementing Amazon Rekognition, we've been able to reduce the amount of content manually reviewed by our operations team by 95%, while freeing up engineering resources to focus on our core business. We are looking forward to the latest Rekognition Content Moderation model update, which will improve accuracy and add new classes for moderation.”

Zehong, Senior Architect, MobiSocial


SmugMug operates two very large online photo platforms, SmugMug and Flickr, enabling more than 100M members to safely store, search, share, and sell tens of billions of photos. Flickr is the world's largest photographer-focused community, empowering photographers around the world to find their inspiration, connect with each other, and share their passion with the world.

"As a large, global platform, unwanted content is extremely risky to the health of our community and can alienate photographers. We use Amazon Rekognition's content moderation feature to find and properly flag unwanted content, enabling a safe and welcoming experience for our community. At Flickr's huge scale, doing this without Amazon Rekognition is nearly impossible. Now, thanks to content moderation with Amazon Rekognition, our platform can automatically discover and highlight amazing photography that more closely matches our members' expectations, enabling our mission to inspire, connect, and share."

Don MacAskill, Cofounder, CEO & Chief Geek, SmugMug 


ZOZO Inc. owns and operates ZOZOTOWN, Japan's largest fashion ecommerce, and WEAR, a SNS for sharing stylings and outfits, and more various services for fashion lovers.

"A large number of images are posted on WEAR from our users every day, and it was necessary to check every image to ensure that it complied with the service guidelines. We built a solution based on Amazon Rekognition Content Moderation API that automatically inspects to analyze the content that users post and store in Amazon S3. Amazon Rekognition has helped us cut down the manual content review process by up to 40% by automatically analyzing images. We were also able to reduce escalating reviews to supervisors that would have slowed down content operations when a person could not determine if an image was appropriate or not."

Yu Shigetani, Engineer, Brand Solution Development Division, ZOZO Inc.

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