A University of Maryland expert in machine learning is being funded by the National Institute of Standards and Technology (NIST) to develop metrics that will bridge the knowledge gap between empirical and certifiable defenses against adversarial attacks. Soheil Feizi, assistant professor of computer science with an appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS), is principal investigator of the $387K two-year project. An adversarial attack involves penetrating machine learning systems in order to make small changes to the input data to confuse the algorithm, resulting in flawed outputs. Some of these changes are so small they can fly under the radar undetected, posing a serious security risk for AI systems that are increasingly being applied to industrial settings, medicine, information analysis and more. Both empirical and certifiable defenses have recently gained attention in the machine learning community for showing success against adversarial attacks, says Feizi.