A team of engineering researchers from the University of Toronto have created an algorithm to dynamically disrupt facial recognition systems.

Led by professor Parham Aarabi and graduate student Avishek Bose, the team used a deep learning technique called "adversarial training", which pits two artificial intelligence algorithms against each other.

Aarabi and Bose designed a set of two neural networks, the first one identifies faces and the other works on disrupting the facial recognition task of the first. The two constantly battle and learn from each other, setting up an ongoing AI arms race.

"The disruptive AI can 'attack' what the neural net for the face detection is looking for," Bose said in an interview with Eureka Alert.

"If the detection AI is looking for the corner of the eyes, for example, it adjusts the corner of the eyes so they're less noticeable. It creates very subtle disturbances in the photo, but to the detector, they're significant enough to fool the system."

The result looks similar to an Instagram filter that can be applied to photos to protect privacy. The algorithm targets very specific pixels in the image, making subtle changes that are almost imperceptible to the human eye.

"The key here was to train the two neural networks against each other, with one creating an increasingly robust facial detection system, and the other creating an ever stronger tool to disable facial detection," added Bose.