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 ...

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 ...

What should people new to the field know about adversarial machine learning? originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better ...

A neural network looks at a picture of a turtle and sees a rifle. A self-driving car blows past a stop sign because a carefully crafted sticker bamboozled its computer vision. An eyeglass frame ...

Threat actors have several ways to fool or exploit artificial intelligence and machine learning systems and models, but you can defend against their tactics. As more companies roll out artificial ...

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 ...

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 ...