The vulnerabilities of machine learning models open the door for deceit, giving malicious operators the opportunity to interfere with the calculations or decision making of machine learning systems.

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 ...

The final guidance for defending against adversarial machine learning offers specific solutions for different attacks, but warns current mitigation is still developing. NIST Cyber Defense The final ...

The National Institute of Standards and Technology (NIST) has published its final report on adversarial machine learning (AML), offering a comprehensive taxonomy and shared terminology to help ...

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 ...

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 ...

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 ...

To human observers, the following two images are identical. But researchers at Google showed in 2015 that a popular object detection algorithm classified the left image as “panda” and the right one as ...

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 ...