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 ...
JD Supra

Adversarial Machine Learning in Focus: Novel Risks, Straightforward Legal Approaches

The Artificial Intelligence and Machine Learning (“AI/ML”) risk environment is in flux. One reason is that regulators are shifting from AI safety to AI innovation approaches, as a recent DataPhiles ...
Rochester Institute of Technology

Professor earns NSF CAREER Award to defend AI models from attackers

RIT computer science professor Weijie Zhao has earned a National Science Foundation CAREER Award to defend machine learning ...
Homeland Security Today

NIST Seeks Comment on Draft Adversarial Machine Learning Report

NIST’s National Cybersecurity Center of Excellence (NCCoE) has released a draft report on machine learning (ML) for public comment. A Taxonomy and Terminology of Adversarial Machine Learning (Draft ...
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 ...
National Academies of Sciences%2c Engineering%2c and Medicine

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies: Proceedings of a Workshop

This activity was supported by Contract 2014-14041100003-019 with the Office of the Director of National Intelligence. Any opinions, findings, conclusions, or recommendations expressed in this ...
Homeland Security Today

NIST Releases Final Report on AI/ML Cybersecurity Threats and Mitigations

The National Institute of Standards and Technology (NIST) has published its final report on adversarial machine learning (AML), offering a comprehensive taxonomy and shared terminology to help ...