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University of Cambridge > Talks.cam > Computer Laboratory Security Seminar > Towards Meaningful Stochastic Defences in Machine Learning
Towards Meaningful Stochastic Defences in Machine LearningAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Kieron Ivy Turk. Machine learning (ML) has proven to be more fragile than previously thought, especially in adversarial settings. A capable adversary can cause ML systems to break at training, inference, and deployment stages. In this talk, I will cover the recent work on attacking and defending machine learning pipelines using stochastic defences; I will describe how, seemingly powerful defences fail to provide any security and end up being vulnerable to even standard attackers. I will then demonstrate a number of possible randomness-based defences that can provide theoretical and practical performance improvements. Bio: Ilia Shumailov holds a PhD in Computer Science from University of Cambridge, specialising in Machine Learning and Computer Security. During the PhD under the supervision of Prof Ross Anderson Ilia has worked on a number of projects spanning the fields of machine learning security, cybercrime analysis and signal processing. Following the PhD, Ilia joined Vector Institute in Canada as a Postdoctoral Fellow, where he worked under the supervision of Prof Nicolas Papernot and Prof Kassem Fawaz. Ilia is currently a Junior Research Fellow at Christ Church, University of Oxford. This talk is part of the Computer Laboratory Security Seminar series. This talk is included in these lists:
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