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 ...

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 ...

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. It is widely accepted sage wisdom to garner as much as you can ...

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 ...

The Artificial Intelligence and Machine Learning (“AI/ML”) risk environment is in flux. One reason is that regulators are shifting from AI safety to AI innovation approaches, as a recent DataPhiles ...

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 ...

RIT computer science professor Weijie Zhao has earned a National Science Foundation CAREER Award to defend machine learning from adversarial threats.

Researchers from machine learning lab OpenAI have discovered that their state-of-the-art computer vision system can be deceived by tools no more sophisticated than a pen and a pad. As illustrated in ...