DARPA snags Intel to lead its machine learning security tech
Machine learning is a kind of artificial intelligence that allows systems to improve over time with new data and experiences. One of its most common use cases today is object recognition, such as taking a photo and describing what’s in it. That can help those with impaired vision to know what’s in a photo if they can’t see it, for example, but it also can be used by other computers, such as autonomous vehicles, to identify what’s on the road.
But deception attacks, although rare, can meddle with machine learning algorithms. Subtle changes to real-world objects can, in the case of a self-driving vehicle, have disastrous consequences.
Just a few weeks ago, McAfee researchers tricked a Tesla into accelerating 50 miles per hour above its intended speed by adding a two-inch piece of tape on a speed limit sign. The research was one of the first examples of manipulating a device’s machine learning algorithms.
That’s where DARPA hopes to come into play. The research arm said earlier this year that it’s working on a program known as GARD, or the Guaranteeing AI Robustness against Deception. The existing mitigations against machine learning attacks are typically rule-based and pre-defined, but DARPA hopes it can develop GARD into a system that will have broader defenses to address a number of different kinds of attacks.
Intel said today it’ll serve as the prime contractor for the four-year program alongside Georgia Tech.
Jason Martin, principal engineer at Intel Labs who leads Intel’s GARD team, said the chip maker and Georgia Tech will work together to “enhance object detection and to improve the ability for AI and machine learning to respond to adversarial attacks.”
During the first phase of the program, Intel said its focus is on enhancing its object detection technologies using spatial, temporal and semantic coherence for both still images and video.
DARPA said GARD could be used in a number of settings — such as in biology.
“The kind of broad scenario-based defense we’re looking to generate can be seen, for example, in the immune system, which identifies attacks, wins and remembers the attack to create a more effective response during future engagements,” said Dr. Hava Siegelmann, a program manager in DARPA’s Information Innovation Office.
“We must ensure machine learning is safe and incapable of being deceived,” said Siegelmann.