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 ...

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 ...

Can we make artificial intelligence impervious to adversaries who want to twist the technology to nefarious ends? Though AI ...

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 ...

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 ...

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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 ...

This paper proposes a novel machine learning paradigm called the generative adversarial tri-model (GAT) to incorporate analytical knowledge into neural networks through a unique positive-sum game ...

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 ...

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 ...