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 ...

"An AI system can be technically safe yet deeply untrustworthy. This distinction matters because satisfying benchmarks is necessary but insufficient for trust." ...

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 ...

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 ...

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 ...

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 ...

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 ...