The patch only fools a specific algorithm, but researchers are working on more flexible solutions The patch only fools a specific algorithm, but researchers are working on more flexible solutions is a ...

Louise Matsakis covers cybersecurity, internet law, and online culture for WIRED. Now, a leading group of researchers from MIT have found a different answer, in a paper that was presented earlier this ...

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 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, ...

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 ...

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 ...

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 ...

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 ...

The field of adversarial attacks in natural language processing (NLP) concerns the deliberate introduction of subtle perturbations into textual inputs with the aim of misleading deep learning models, ...