Devdiscourse

Adversarial attacks hit financial AI twice as hard during economic stress

The study, titled Conditional Adversarial Fragility in Financial Machine Learning under Macroeconomic Stress, published as a ...
ascopubs.org

Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology

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 ...
VentureBeat

When is ART useful? When it's IBM's Adversarial Robustness Toolbox for AI

IBM is hoping to advance the state of the art for artificial intelligence (AI) security with an open source project called the Adversarial Robustness Toolbox (ART). As AI usage is growing rapidly, ...
Semiconductor Engineering

Multiexpert Adversarial Regularization For Robust And Data-Efficient Deep Supervised Learning

Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a ...
AFCEA

Adversarial Machine Learning Poses a New Threat to National Security

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 ...
Forbes

This Harvard Professor And His Students Have Raised $14 Million To Make AI Too Smart To Be Fooled By Hackers

Yaron Singer climbed the tenure track ladder to a full professorship at Harvard in seven years, fueled by his work on adversarial machine learning, a way to fool artificial intelligence models using ...
technologynetworks

Cars Require Regular Inspection, Why Should AI Models Be Any Different?

This article includes research findings that are yet to be peer-reviewed. Results are therefore regarded as preliminary and should be interpreted as such. Find out about the role of the peer review ...