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 ...

Most artificial intelligence researchers agree that one of the key concerns of machine learning is adversarial attacks, data manipulation techniques that cause trained models to behave in undesired ...

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.

With machine learning becoming increasingly popular, one thing that has been worrying experts is the security threats the technology will entail. We are still exploring the possibilities: The ...

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 ...

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 ...

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 ...

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 ...