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 ...

Machine learning (ML) and artificial intelligence (AI) are essential components in modern and effective cybersecurity solutions. However, as the use of ML and AI in cybersecurity is increasingly ...

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 ...

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 ...

The rapid evolution and enterprise adoption of AI has motivated bad actors to target these systems with greater frequency and sophistication. Many security leaders recognize the importance and urgency ...

A systematic study reveals that gradient-based adversarial attacks can significantly distort explainable AI feature rankings ...

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 ...

A new generative adversarial framework embeds Hopf bifurcation dynamics and Lyapunov-based regularization into its generator, reporting improved distributional similarity, temporal coherence, and ...