Security News > 2020 > February > To Combat Rogue AI, Facebook Pitches 'Radioactive Data'

Neural networks are a type of machine learning that involves using a large set of training data to devise rules that can be used to identify future patterns.

To detect if training sets have used Facebook images, a team of the company's researchers has proposed building a system that can be used to find out.

"We have developed a new technique to mark the images in a data set so that researchers can determine whether a particular machine learning model has been trained using those images," say Facebook researchers Alexandre Sablayrolles, Matthijs Douze and Hervé Jégou in a blog post.

"Radioactive data differs from previous approaches that aim at 'poisoning' training sets in an imperceptible way such that trained models will generalize poorly," they write.

Based on tests conducted with ImageNet - a large, visual database designed for use in visual object recognition software research - the researchers say that even when their radioactive data only comprised 1 percent of the data used to train a specific neural net, they could still verify that it had been used, thanks to the neural network itself devoting some of its capacity to keep track of their "Radioactive tracers."

News URL

https://www.inforisktoday.com/blogs/to-combat-rogue-ai-facebook-pitches-radioactive-data-p-2862