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10/26/2021
Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes
Quantization is a popular technique that transforms the parameter repres...
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10/06/2020
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference
Recent increases in the computational demands of deep neural networks (D...
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06/09/2020
On the Effectiveness of Regularization Against Membership Inference Attacks
Deep learning models often raise privacy concerns as they leak informati...
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02/26/2020
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
Machine learning algorithms are vulnerable to data poisoning attacks. Pr...
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02/17/2020
How to 0wn NAS in Your Spare Time
New data processing pipelines and novel network architectures increasing...
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06/03/2019
Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks
Deep neural networks (DNNs) have been shown to tolerate "brain damage": ...
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10/16/2018
How to Stop Off-the-Shelf Deep Neural Networks from Overthinking
While deep neural networks (DNNs) can perform complex classification tas...
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10/08/2018
Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks
Recent work has introduced attacks that extract the architecture informa...
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03/19/2018