Computer Science Lecture Series: Pooyan Jamshidi, USC, Adversarial Machine Learning

Friday, April 3, 2020
2:45 PM - 4:00 PM (ET)
Exley Science Center Tower ESC 121
Event Type
Presentation
Contact
Zimmeck, Sebastian
2398
Link
https://eaglet.wesleyan.edu/MasterCalendar/EventDetails.aspx?EventDetailId=92408

Ensembles of Many Diverse Weak Defenses can be Strong: Defending Deep Neural Networks Against Adversarial Attacks

Abstract: Despite achieving state-of-the-art performance across many domains, machine learning systems are highly vulnerable to subtle adversarial perturbations. Although defense approaches have been proposed in recent years, many have been bypassed by even weak adversarial attacks. Previous studies showed that ensembles created by combining multiple weak defenses (i.e., input data transformations) are still weak. In this talk, I will show that it is indeed possible to construct effective ensembles using weak defenses to block adversarial attacks. However, to do so requires a diverse set of such weak defenses. Based on this motivation, I will present Athena, an extensible framework for building effective defenses to adversarial attacks against machine learning systems. I will talk about the effectiveness of ensemble strategies with a diverse set of many weak defenses that comprise transforming the inputs (e.g., rotation, shifting, noising, denoising, and many more) before feeding them to target deep neural network classifiers. I will also discuss the effectiveness of the ensembles with adversarial examples generated by various adversaries in different threat models. In the second half of the talk, I will explain why building defenses based on the idea of many diverse weak defenses works, when it is most effective, and what its inherent limitations and overhead are. 

Bio: Pooyan Jamshidi is an Assistant Professor at the University of South Carolina. He directs the AISys Lab, where he investigates the development of novel algorithmic and theoretically principled methods for machine learning systems. Before his current position, he was a research associate at Carnegie Mellon University and Imperial College London, where he primarily worked on transfer learning for performance understanding of highly-configurable systems including robotics and big data systems. Pooyan’s general research interests are at the intersection of systems/software and machine learning. He received his Ph.D. in Computer Science at Dublin City University in 2014, and MS and BS degrees in Computer Science and Math from the Amirkabir University of Technology in 2003 and 2006 respectively. More info: http://pooyanjamshidi.github.io/ 

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