The launch addresses a problem every security leader knows but few tools have solved: threat modeling is essential, never more so than in an AI-driven era, yet it has remained slow, manual, and ...

In the ongoing battle against adversarial attacks, adopting a suitable strategy to enhance model efficiency, bolster resistance to adversarial threats, and ensure practical deployment is crucial. To ...

We are witnessing a rapid advancement of AI and its impact across various industries. However, with great power comes great responsibility, and one of the emerging challenges in the AI landscape is ...

Despite the success of deep learning models for the state-of-health estimation of lithium-ion batteries in battery management systems, their susceptibility to adversarial attacks raises concerns about ...

Perhaps you've read about AI capable of producing humanlike speech or generating images of people that are difficult to distinguish from real-life photographs. More often than not, these systems build ...

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 attacks are an increasingly worrisome threat to the performance of artificial intelligence applications. If an attacker can introduce nearly invisible alterations to image, video, speech, ...

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 ...

Threat actors have several ways to fool or exploit artificial intelligence and machine learning systems and models, but you can defend against their tactics. As more companies roll out artificial ...