Posted On: Nov 16, 2021
Amazon Rekognition content moderation is a deep learning-based feature that can detect inappropriate, unwanted, or offensive images and videos, making it easier to find and remove such content at scale. Amazon Rekognition provides a detailed taxonomy across 35 sub-categories and 10 distinct top-level moderation categories. Starting today, Amazon Rekognition content moderation comes with an improved model for image moderation that significantly reduces false positive rates across all of the moderation categories, particularly ‘explicit nudity’, without reduction in detection rates for truly unsafe content. Lower false positive rates imply lower volumes of flagged images to be reviewed further, leading to a better experience for human moderators and more cost savings.
Today, many companies employ teams of human moderators to review third-party or user generated content, while others simply react to user complaints to take down offensive or inappropriate content. However, human moderators alone cannot scale to meet these needs at sufficient quality or speed, which could lead to a poor user experience, high costs to achieve scale, or even a loss of brand reputation. Amazon Rekognition content moderation enables you to streamline your moderation workflows using machine learning. Using fully managed moderation APIs, you can quickly review millions of images or thousands of videos, and flag only a small subset of assets for further action. This ensures that you get comprehensive but cost-effective moderation coverage for all your content as your business scales, and you can reduce the burden on your workforce from having to look at large volumes of content for potential moderation. Following is a quote from Flipboard on how they are using Amazon Rekognition for image content moderation:
Flipboard is a content recommendation platform that enables publishers, creators, and curators to share stories with readers to help them stay up to date on their passions and interests. On average, Flipboard processes approximately 90M images per day. To maintain a safe and inclusive environment and to confirm that all images comply with platform guidelines at scale, it is crucial to implement a content moderation workflow using machine learning. To build models for this system internally was labor intensive and lacked the accuracy necessary to meet the high quality standards Flipboard users expect. This is where Amazon Rekognition became the right solution for our product. Amazon Rekognition is a highly accurate, easily deployed, and performant content moderation platform that provides a robust moderation taxonomy. Since putting Amazon Rekognition into our workflows we’ve been catching approximately 63K images that violate our standards per day. Moreover, with frequent improvements like the latest content moderation model update, we can be confident that Rekognition will continue to help make Flipboard an even more inclusive and safe environment for our users over time. - Anuj Ahooja, Sr. Engineering Manager, Flipboard
Accuracy improvements for Amazon Rekognition content moderation in images are now available in all supported AWS Regions. To get started, you can try the latest version in the Amazon Rekognition Console. For more information on Amazon Rekognition content moderation, refer to feature documentation.