HealthTree Cure Hub: A Patient-Derived, Patient-Driven Clinical Cancer Information Platform Used to Overcome Hurdles and Accelerate Research in Multiple Myeloma Adversarial images represent a ...

In recent years, the media have been paying increasing attention to adversarial examples, input data such as images and audio that have been modified to manipulate the behavior of machine learning ...

Deep learning has come a long way since the days when it could only recognize handwritten characters on checks and envelopes. Today, deep neural networks have become a key component of many computer ...

An autonomous train is barreling down the tracks, its cameras constantly scanning for signs that indicate things like how fast it should be going. It sees one that appears to require the train to ...

点击上方“Deephub Imba”,关注公众号,好文章不错过 !精心构造的输入样本能让机器学习模型产生错误判断，这些样本与正常数据的差异微小到人眼无法察觉，却能让模型以极高置信度输出错误预测。这类特殊构造的输入在学术界被称为对抗样本(adversarial ...

Adversarial attacks are an increasingly worrisome threat to the performance of artificial intelligence applications. If an attacker can introduce nearly invisible alterations to image, video, speech, ...

You’re probably familiar with deepfakes, the digitally altered “synthetic media” that’s capable of fooling people into seeing or hearing things that never actually happened. Adversarial examples are ...

The algorithms that computers use to determine what objects are–a cat, a dog, or a toaster, for instance–have a vulnerability. This vulnerability is called an adversarial example. It’s an image or ...

is a senior reporter who has covered AI, robotics, and more for eight years at The Verge. Computer vision has improved massively in recent years, but it’s still capable of making serious errors. So ...

Machine learning systems and innovative deep learning mechanisms that assure prospects of the bright and glittering future are in fact exceedingly vulnerable to cyberattacks. Like any technology, ...

We’ve touched previously on the concept of adversarial examples—the class of tiny changes that, when fed into a deep-learning model, cause it to misbehave. In March, we covered UC Berkeley professor ...