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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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02/15/2021
Expected Exploitability: Predicting the Development of Functional Vulnerability Exploits
Assessing the exploitability of software vulnerabilities at the time of ...
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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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02/28/2019
Poster: On the Feasibility of Training Neural Networks with Visibly Watermarked Dataset
As there are increasing needs of sharing data for machine learning, ther...
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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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04/03/2018
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Data poisoning is a type of adversarial attack on machine learning model...
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03/19/2018
When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks
Attacks against machine learning systems represent a growing threat as h...
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03/08/2018
