Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks

Thursday, July 12, 2018, 10:30 am - 11:30 am PDTiCal
Conference room: 1135
This event is open to the public.
Cyber Seminar Talk
Brendan Dolan-Gavitt

Abstract: Deep neural networks (DNNs) provide excellent performance
across a wide range of classification tasks, but their training
requires high computational resources and is often outsourced to third
parties. Recent work has shown that outsourced training introduces the
risk that a malicious trainer will return a backdoored DNN that
behaves normally on most inputs but causes targeted misclassifications
or degrades the accuracy of the network when a trigger known only to
the attacker is present. In this talk, we provide the first effective
defenses against backdoor attacks on DNNs. We implement three backdoor
attacks from prior work and use them to investigate two promising
defenses, pruning and fine-tuning. We show that neither, by itself, is
sufficient to defend against sophisticated attackers. We then evaluate
fine-pruning, a combination of pruning and fine-tuning, and show that
it successfully weakens or even eliminates the backdoors, i.e., in
some cases reducing the attack success rate to 0% with only a 0.4%
drop in accuracy for clean (non-triggering) inputs. Our work provides
the first step toward defenses against backdoor attacks in deep neural

Bio:  Brendan Dolan-Gavitt is an Assistant Professor in the Computer Science
and Engineering Department at the NYU Tandon School of Engineering. He
holds a Ph.D. in computer science from Georgia Tech (2014) and a BA in
Math and Computer Science from Wesleyan University (2006). His
research interests include software security, reverse engineering, and
the security of deep learning.

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