Webinar: Computer Vision for HW Security

Speaker: 

Dr. Damon Woodard, Director of the Florida Institute for National Security (FINS), Associate Professor within the Electrical and Computer Engineering Department at the University of Florida

Abstract:

This webinar will first provide an overview of computer vision and conventional methods that have been applied to hardware security problems. Also, a detailed discussion of two examples of computer vision-enabled hardware security applications, automated hardware trojan detection and reverse engineering, using SEM imagery will be provided.

Speaker Bio:

Dr. Woodard currently serves as the Director of the Florida Institute for National Security (FINS). He is an Associate Professor within the Electrical and Computer Engineering Department at the University of Florida and directs the Applied Artificial Intelligence Group. He is an IEEE Senior Member, an ACM Senior Member, a National Academy of Science Kavli Frontiers Fellow, and a member of the Association for the Advancement of Artificial Intelligence (AAAI). Dr. Woodard received his Ph.D. in Computer Science and Engineering from the University of Notre Dame, his M.E. in Computer Science and Engineering from Penn State University, and his B.S. in Computer Science and Computer Information Systems from Tulane University.

Before becoming a faculty member, Dr. Woodard was a Director of Central Intelligence postdoctoral fellow. His postdoctoral research focused on developing advanced iris recognition systems using high-resolution sensors. His research interests include biometrics/identity science and applied artificial intelligence (machine learning, deep learning, reinforcement learning, computer vision, and natural language processing). His current research projects include authorship attribution (stylometry) / computational behavioral analytics via text analytics and natural language processing, image analysis/machine learning-based hardware assurance (hardware trojan detection, counterfeit electronics detection), and adversarial machine learning (DeepFake detection).