woman with children viewing ipad filtering content

Top 5 things to know about machine learning content moderation

If you are in charge of moderating user generated content, whether video, image or text based, you may be wondering how you can take advantage of machine learning without having to completely rearchitecting your platform. This can be done by plugging into the API-based machine learning content moderation provided by AWS. Here are the top 5 things you should know about it:

  1. Video, image, & audio moderation is done by Rekognition
  2. Machine learning content moderation costs around $0.10 per minute
  3. There are 10 major categories, and 25 secondary categories AWS tracks
  4. You can tailor the content moderation to fit your parameters
  5. You can use the same algorithm for metadata tagging

1. Video, image, & audio moderation is done by Rekognition

In the AWS machine learning space, the workhorse of the content moderation is Rekognition. Rekognition is a deep learning algorithm powered and trained by millions of hours of video, and countless numbers of images which can do robust content moderation out of the box. The system is available on demand (see pricing below) via an API which can be setup by creating an AWS account and providing the right IAM user permissions. If you need help setting this up, please don't hesitate to reach out.

2. Machine learning content moderation costs around $0.10 per minute

While visual machine learning can be complicated to setup, the infrastructure costs on Amazon are often negligible. For example:

  • image analysis generally ranges between $0.001 per image (for the first million images) up to $0.00025 per image if you are processing 35 million images or more.
  • Basic label detection starts at $0.0010 per image, aka.1,000 images for 1 dollar.
  • Video analysis is generally prices per minute per service, with all of the following services billed at $0.10/min: label detection, content moderation, text detection, face detection, celebrity recognition, face search, person pathing

More robust pricing can be found on the Amazon Rekognition pricing page, but even if the calculations get complicated, the price is still incredibly low. As an AWS Reseller, Metal Toad can likely get you better pricing as well. Feel free to reach out and see what kind of pricing we can offer!

3. Amazon tracks are 10 major and 25 secondary categories

The Rekognition API will automatically tag content reviewed with the following major or top-level categories (explicit nudity, violence, etc) and (if applicable) the second-level category:

  • Explicit Nudity
    • Nudity
    • Graphic Male Nudity
    • Graphic Female Nudity
    • Sexual Activity
    • Illustrated Explicit Nudity
    • Adult Toys
  • Suggestive
    • Female Swimwear Or Underwear
    • Male Swimwear Or Underwear
    • Partial Nudity
    • Barechested Male
    • Revealing Clothes
    • Sexual Situations
  • Violence
    • Graphic Violence Or Gore
    • Physical Violence
    • Weapon Violence
    • Weapons
    • Self Injury
  • Visually Disturbing
    • Emaciated Bodies
    • Corpses
    • Hanging
    • Air Crash
    • Explosions And Blasts
  • Rude Gestures
    • Middle Finger
  • Drugs
    • Drug Products
    • Drug Use
    • Pills
    • Drug Paraphernalia
  • Tobacco
    • Tobacco Products
    • Smoking
  • Alcohol
    • Drinking
    • Alcoholic Beverages
  • Gambling
    • Gambling
  • Hate Symbols
    • Nazi Party
    • White Supremacy
    • Extremist

4. You can tailor the content moderation to fit your parameters

As a content moderator you may find that some content you want to remove or flag for human review, while other content you may want to allow through or simply delete without review. All of this can be setup and customized in the application layer, which means AWS machine learning is extremely flexible: it describes the type of content and the time stamp where it occurs, and you decide how you want to deal with it.

This also means that every application of machine learning content moderation must be extended and customized.  If this is something you are exploring feel free to reach out and we can help you scope out the project.

If you need a comprehensive solution that can provide video upload, streaming, and moderation you can read about how to pull it all together using Rekognition + MUX here.

5. You can use the same algorithm for metadata tagging

Amazon Rekognition does much more than content moderation. Although each service requires it's own configuration and incurs additional costs, Rekognition can also do the following "out-of-the-box":

  • Facial recognition - this often the first thing people think of when they think of machines doing image analysis.
  • Celebrity recognition - in addition to identifying faces, the algorithm knows "celebrities" out of the box. Jeff Bezos or Werner Vogels are perfect examples.
  • Sentiment analysis - "that's a face, are the people happy or sad", etc?
    • Person pathing - given a person, the algorithm can track the path that person moves
    • Text detection - the machine can read things like menus, t-shirts, etc.
    • Personal Protective Equipment (PPE) - Rekognition can detect the usage of PPE
    • Custom Labeling - one of the most powerful features of Rekognition, the machine can label images with things it observes like:
      • Activities (running, playing, laughing, etc.)
      • Objects (trees, streets, cars, people, etc.)
      • Scenes (beach, sunset, city, etc.)
      • Concepts (outdoors, fun, etc.)

    More about Amazon Machine Learning

    If you'd like to learn more about Amazon's machine learning services, you can checkout our overview of AWS machine learning or our blog post on the top 5 things to know about deep learning.

    Date posted: April 19, 2022

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    About the Author

    Joaquin Lippincott, CEO

    Joaquin is a 20+ year technology veteran helping to lead businesses in the move to the Cloud. He frequently speaks on panels about the future of tech ranging from IoT and Machine Learning to the latest innovation in the entertainment industry.  He has helped to modernize software for industry leaders like Sony, Daimler, Intel, the Golden Globes, Siemens Wind Power, ABC, NBC, DC Comics, Warner Brothers & the Linux Foundation.

    As the CEO and Founder of Metal Toad, an AWS Advanced Consulting Partner, his primary job is to "get the right people in the room".  This one responsibility is cross-functional and includes both external business development functions as well as internal delegation and leadership development.

    A UCLA alumni, he also serves in the community as a Board Member for the Los Angeles Area Chamber of Commerce, the Beverly Hills Chamber of Commerce, and Stand for Children Oregon - a public education political advocacy group. As an outspoken advocate for entry-level job creation in tech he helped found the non-profit, P4TH, an organization dedicated to increasing the number of entry-level jobs in the tech industry, and is in the process of organizing an Advisory Board for the Bixel Exchange, a Los Angeles non-profit that provides almost 200 tech internships every year.


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