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 AI exploits model vulnerabilities by subtly altering inputs (like images or code) to trick AI systems into misclassifying or misbehaving. These attacks often evade detection because they ...

Adversarial attacks against the technique that powers game-playing AIs and could control self-driving cars shows it may be less robust than we thought. The soccer bot lines up to take a shot at the ...

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 ...

Accuracies obtained by the most effective configuration of each of the seven different attacks across the three datasets. The Jacobian-based Saliency Map Attack (JSMA) was the most effective in ...

Artificial intelligence and machine learning (AI/ML) systems trained using real-world data are increasingly being seen as open to certain attacks that fool the systems by using unexpected inputs. At ...

The context: One of the greatest unsolved flaws of deep learning is its vulnerability to so-called adversarial attacks. When added to the input of an AI system, these perturbations, seemingly random ...

A digital twin is an exact virtual copy of a real-world system. Built using real-time data, they provide a platform to test, simulate, and optimize the performance of their physical counterpart. In ...

By evaluating TTP coverage and stacking risk reductions across layers, organizations can decrease their odds of stopping ...