VentureBeat

Adversarial attacks in machine learning: What they are and how to stop them

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 ...
VentureBeat

Adversarial attacks on AI models are rising: what should you do now?

Adversarial attacks on machine learning (ML) models are growing in intensity, frequency and sophistication with more enterprises admitting they have experienced an AI-related security incident. AI's ...
University of Geneva

Defending against adversarial attacks by randomized diversification

The chain of the first 3 blocks can be organized in a parallel multi-channel structure that is followed by one or several aggregation blocks. The final decision about the class is made based on the ...
The National Law Review

NIST Finalizes Cyber Attack Guidance for Adversarial Machine Learning

We collaborate with the world's leading lawyers to deliver news tailored for you. Sign Up for any (or all) of our 25+ Newsletters. Some states have laws and ethical rules regarding solicitation and ...
Dark Reading

Machine Learning Models: A Dangerous New Attack Vector

Threat actors can hijack machine learning (ML) models that power artificial intelligence (AI) to deploy malware and move laterally across enterprise networks, researchers have found. These models, ...
Forbes

AI Ethics Tempted But Hesitant To Use AI Adversarial Attacks Against The Evils Of Machine Learning, Including For Self-Driving Cars

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. It is widely accepted sage wisdom to garner as much as you can ...
EurekAlert!

An innovative set-level adversarial learning method to improve domain adaptation

Domain adaptation remains a significant challenge in artificial intelligence, especially when models trained in one domain are required to perform well in another. Conventional adversarial domain ...