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Keynote Lectures

Keynote Lecture
Yaochu Jin, University of Surrey, United Kingdom

Secure Object Detection on the Edge
Ling Liu, Georgia Institute of Technology, United States

 

Keynote Lecture

Yaochu Jin
University of Surrey
United Kingdom
 

Brief Bio
Yaochu Jin is Professor of Computational Intelligence, Head of the Nature Inspired Computing and Engineering (NICE) group, Co-Coordinator of the Centre for Mathematical and Computational Biology (CMCB), Director of Research Management Committee, Department of Computing, University of Surrey. He is also a Finland Distinguished Professor (2015-17) funded by the Finnish Funding Agency for Innovation (Tekes), and a Changjiang Distinguished Visiting Professor, Ministry of Education, China.
He obtained the BSc, MSc and PhD degrees from Zhejiang University, Hangzhou, China and the Dr.-Ing. from Ruhr-University Bochum, Germany. Before joining Surrey in 2010, he was a Principal Scientist with Honda Research Institute Europe, Germany. He is particularly interested in nature-inspired, real-world driven problem-solving and interdisciplinary areas that bridge the gap between computational intelligence, machine learning and computational biology.
He is a Distinguished Lecturer of IEEE and Vice President for Technical Activities of the IEEE Computational Intelligence Society. He is an Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Nanobioscience, IEEE Computational Intelligence Magazine, Soft Computing Journal (Springer), BioSystems (Elsevier) and International Journal of Fuzzy Systems. He is also an Editorial Board Member of the Evolutionary Computation Journal (MIT Press) and Natural Computing (Springer).
He is the General Co-Chair of 2016 IEEE Symposium Series on Computational Intelligence. He is also Founding General Co-Chair of the IEEE Symposium on Computational Intelligence in Big Data, IEEE Symposium on Computational Intelligence in Multi-Criterion Decision-Making (IEEE MCDM), IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (IEEE CIDUE).


Abstract
Available soon.



 

 

Secure Object Detection on the Edge

Ling Liu
Georgia Institute of Technology
United States
 

Brief Bio
Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large scale big data-powered artificial intelligence (AI) systems, and machine learning (ML) algorithms and analytics, including performance, availability, privacy, security and trust. Prof. Liu is an elected IEEE Fellow, a recipient of IEEE Computer Society Technical Achievement Award (2012), and a recipient of the best paper award from numerous top venues, including IEEE ICDCS, WWW, ACM/IEEE CCGrid, IEEE Cloud, IEEE ICWS. Prof. Liu served on editorial board of over a dozen international journals, including the editor in chief of IEEE Transactions on Service Computing (2013-2016) and currently, the editor in chief of ACM Transactions on Internet Computing (TOIT). Prof. Liu is a frequent keynote speaker in top-tier venues in Big Data, AI and ML systems and applications, Cloud Computing, Services Computing, Privacy, Security and Trust. Her current research is primarily supported by USA National Science Foundation under CISE programs and IBM.


Abstract

Deep neural networks (DNNs) have fueled the wide deployment of object detection models in a number of mission-critical domains, such as traffic sign detection on autonomous vehicles, and intrusion detection on surveillance systems. Recent studies have revealed that deep object detectors can also be compromised under adversarial attacks, causing a victim detector to detect no object, fake objects, or wrong objects. However, very few studies how to guarantee the robustness of object detection against adversarial manipulations. This keynote presents an in-depth understanding of vulnerabilities of deep object detection systems by analyzing the adversarial robustness  under different DNN detector training algorithms, different attack strategies, different adverse effects and costs. Then I will describe a set of mitigation strategies and techniques for robust object detection by guaranteeing high adversarial robustness while maintaining high benign detection accuracy.



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