INSUBCONTINENT EXCLUSIVE:
Chip maker Intel has been chosen to lead a new initiative led by the United States military research wing, DARPA, aimed at improving
cyber-defenses against deception attacks on machine learning models.
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 in it
That can help those with impaired vision to know what 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 on the road.
But deception attacks, although rare, can meddle with machine learning
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
The research was one of the first examples of manipulating a device machine learning algorithms.
That where DARPA hopes to come into play
The research arm said earlier this year that it 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 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 Information Innovation Office.
We must ensure machine learning is safe and incapable of being
deceived,& said Siegelmann.
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