In MobilBye: Attacking ADAS with Camera Spoofing, a group of Ben Gurion security researchers describe how they were able to defeat a Renault Captur's "Level 0" autopilot (Level 0 systems advise human drivers but do not directly operate cars) by following them with drones that projected images of fake roadsigns for a 100ms instant -- too short for human perception, but long enough for the autopilot's sensors.
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An adversarial preturbation is a small, human-imperceptible change to a piece of data that flummoxes an otherwise well-behaved machine learning classifier: for example, there's a really accurate ML model that guesses which full-sized image corresponds to a small thumbnail, but if you change just one pixel in the thumbnail, the classifier stops working almost entirely.
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For several years, I've been covering the bizarre phenomenon of "adversarial examples (AKA "adversarial preturbations"), these being often tiny changes to data than can cause machine-learning classifiers to totally misfire: imperceptible squeaks that make speech-to-text systems hallucinate phantom voices; or tiny shifts to a 3D image of a helicopter that makes image-classifiers hallucinate a rifle
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Machine learning models use statistical analysis of historical data to predict future events: whether you are a good candidate for a loan, whether you will violate parole, or whether the thing in the road ahead is a stop sign or a moose.
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Machine learning systems trained for object recognition deploy a bunch of evolved shortcuts to choose which parts of an image are important to their classifiers and which ones can be safely ignored.
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In Partial Information Attacks on Real-world AI, a group of MIT computer science researchers report on their continuing work fooling Google's image-classifier, this time without any knowledge of how the classifier works.
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