Skip to Content
Artificial intelligence

Facebook’s “radioactive data” tracks the images used to train an AI

An image of a pile of photographs
An image of a pile of photographsJon Tyson | Unsplash

The news: A team from Facebook AI Research has developed a way to track exactly which images in a data set were used to train a machine-learning model. By making imperceptible tweaks to images, creating a kind of watermark, they were able to make tiny corresponding changes to the way an image classifier trained on those images works, without impairing its overall accuracy. This let them later match models up with the images that were used to train them. 

Why it matters: Facebook calls the technique “radioactive data” because it is analogous to the use of radioactive markers in medicine, which show up in the body under x-ray. Highlighting what data has been used to train an AI makes models more transparent, flagging potential sources of bias—such as a model trained on an unrepresentative set of images—or revealing when a data set was used without permission or for inappropriate purposes. 

Make no mistake: A big challenge was to change the images without breaking the resulting model. Tiny tweaks to an AI’s input can sometimes lead to it making stupid mistakes, such as identifying a turtle as a gun or a sloth as a racecar. Facebook made sure to design its watermarks so that this did not happen. The team tested its technique on ImageNet, a widely used data set of more than 14 million images, and found that they could detect use of radioactive data with high confidence in a particular model even when only 1% of the images had been marked. 

Deep Dive

Artificial intelligence

This new data poisoning tool lets artists fight back against generative AI

The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models. 

Rogue superintelligence and merging with machines: Inside the mind of OpenAI’s chief scientist

An exclusive conversation with Ilya Sutskever on his fears for the future of AI and why they’ve made him change the focus of his life’s work.

Unpacking the hype around OpenAI’s rumored new Q* model

If OpenAI's new model can solve grade-school math, it could pave the way for more powerful systems.

Generative AI deployment: Strategies for smooth scaling

Our global poll examines key decision points for putting AI to use in the enterprise.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.