Abstract: This research evaluates a cognitive AI model for unmanned aerial vehicles (UAV) detection using adversarial machine learning (AML) techniques. We test the model using the VisDrone dataset ...

Abstract: Adversarial Machine Learning (AML) is a fascinating and fast-growing research direction and area of practical interest. Deployed Machine Learning (ML) models are known to be vulnerable to ...

ABSTRACT: To provide quantitative analysis of strategic confrontation game such as cross-border trades like tariff disputes and competitive scenarios like auction bidding, we propose an alternating ...

ABSTRACT: The application of artificial intelligence (AI) in healthcare has tremendous potential for improving diagnostic precision and optimizing treatment and patient care. However, increasing ...

Corresponding repo for "Busting the Ballot: Voting Meets Adversarial Machine Learning". We show the security risk associated with using machine learning classifiers in United States election ...

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 ...

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 ...

The National Institute of Standards and Technology has issued a document that identifies threats associated with adversarial machine learning. The Adversarial Machine Learning: A Taxonomy and ...