To human observers, the following two images are identical. But researchers at Google showed in 2015 that a popular object detection algorithm classified the left image as “panda” and the right one as ...

Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a ...

The vulnerabilities of machine learning models open the door for deceit, giving malicious operators the opportunity to interfere with the calculations or decision making of machine learning systems.

Machine learning, for all its benevolent potential to detect cancers and create collision-proof self-driving cars, also threatens to upend our notions of what's visible and hidden. It can, for ...

Artificial intelligence won’t revolutionize anything if hackers can mess with it. That’s the warning from Dawn Song, a professor at UC Berkeley who specializes in studying the security risks involved ...

Much of the anti-adversarial research has been on the potential for minute, largely undetectable alterations to images (researchers generally refer to these as “noise perturbations”) that cause AI’s ...

Imagine the following scenarios: An explosive device, an enemy fighter jet and a group of rebels are misidentified as a cardboard box, an eagle or a sheep herd. A lethal autonomous weapons system ...

Machine learning is becoming more important to cybersecurity every day. As I've written before, it's a powerful weapon against the large-scale automation favored by today's threat actors, but the ...