You can use Amazon Rekognition to detect content that is inappropriate, unwanted, or offensive. You can use Rekognition moderation APIs in social media, broadcast media, advertising, and e-commerce situations to create a safer user experience, provide brand safety assurances to advertisers, and comply with local and global regulations.
Today, many companies rely entirely on human moderators to review third-party or user-generated content, while others simply react to user complaints to take down offensive or inappropriate images, ads, or videos. However, human moderators alone cannot scale to meet these needs at sufficient quality or speed, which leads to a poor user experience, high costs to achieve scale, or even a loss of brand reputation. By using Rekognition for image and video moderation, human moderators can review a much smaller set of content, typically 1-5% of the total volume, already flagged by machine learning. This enables them to focus on more valuable activities and still achieve comprehensive moderation coverage at a fraction of their existing cost. To set up human workforces and perform human review tasks, you can use Amazon Augmented AI, which is already integrated with Rekognition.
You can enhance the accuracy of the moderation deep learning model with the Custom Moderation feature. With Custom Moderation, you train a custom moderation adapter by uploading your images and annotating these images. The trained adapter can then be provided to the DetectModerationLabels operation to to enhance its performance on your images. See Enhancing accuracy with Custom Moderation for more information.
Labels supported by Rekognition content moderation operations
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To download a list of the moderation labels, click here.
Topics
The following diagram shows shows the order for calling operations, depending on your goals for using the image or video components of Content Moderation:
