Amazon Rekognition Content Moderation
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.
Improves user and brand safety
Review a few or millions of images and videos against a wide variety of pre-defined or business-specific unsafe categories. Proactively ensure that your users and brand sponsors are not exposed to unwanted or inappropriate content.
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 (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.
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 offers 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 A2I with Rekognition moderation APIs to enable your teams or a third-party vendor to make final judgements whenever low confidence predictions require human intervention.
Reviewing user generated content
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 matchmaking.
Media and e-commerce compliance
Use Amazon Rekognition and Amazon Transcribe to identify potentially unsafe images, video, text, and audio content, or ensure that third-party listings do not violate the safety policies of your platform.
Identify and filter out unwanted brand associations using rich and hierarchically organized metadata from Amazon Rekognition Content Moderation.
Read more about our 40+ Amazon Rekognition customer stories.
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, Co-founder - CEO & Chief Geek
ZOZO, Inc. owns and operates ZOZOTOWN, Japan's largest fashion e-commerce website, and WEAR, a social network that provides digital services for fashion lovers to safely share styles and outfits.
"A large number of images are posted on WEAR from users every day, and it was necessary to check every image to ensure that it complied with the service guidelines. We built a system that automatically inspects images using Amazon Rekognition's Content Moderation API to analyze images users posted and stored in Amazon S3. Amazon Rekognition has cut down the review process by up to 40% by automatically recognizing images. We were also able to reduce communications, such as escalation of matters to supervisors that would have occurred when the reviewing person could not determine if an image was appropriate or not."
Yu Shigetani, Engineer, Brand Solution Development Division - ZOZO, Inc.
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