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 ...

Adversarial machine learning studies the creation and defence against inputs—known as adversarial examples—that are intentionally perturbed to mislead trained models. Deep networks and other ...

Adversarial vulnerabilities pose a fundamental challenge to the deployment of deep neural networks in real-world settings. By introducing carefully crafted perturbations imperceptible to human ...

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 ...

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 ...

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 ...

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 ...

Artificial intelligence and machine learning projects require a lot of complex data, which presents a unique cybersecurity risk. Security experts are not always included in the algorithm development ...

Learn what machine learning is, how it works, its types, the algorithms it uses, and its real-world uses in this complete 2026 guide.

Machine learning is changing the front end of drug discovery, where researchers decide which targets to pursue and which molecules deserve costly laboratory work. Its deeper test lies further ...