82 citations · 154 across the 14 of their papers we have counts for
5 papers · 2 filters
Towards Robust Deep Neural Networks with BANG
Andras Rozsa, Manuel Gunther, Terrance E. Boult
Machine learning models, including state-of-the-art deep neural networks, are vulnerable to small perturbations that cause unexpected classification errors. This unexpected lack of…
Are Accuracy and Robustness Correlated?
Andras Rozsa, Manuel Günther, Terrance E. Boult
Machine learning models are vulnerable to adversarial examples formed by applying small carefully chosen perturbations to inputs that cause unexpected classification errors. In thi…
Assessing Threat of Adversarial Examples on Deep Neural Networks
Abigail Graese, Andras Rozsa, Terrance E. Boult
Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network. For…
Adversarial Diversity and Hard Positive Generation
Andras Rozsa, Ethan M. Rudd, Terrance E. Boult
State-of-the-art deep neural networks suffer from a fundamental problem - they misclassify adversarial examples formed by applying small perturbations to inputs. In this paper, we…
PARAPH: Presentation Attack Rejection by Analyzing Polarization Hypotheses
Ethan M. Rudd, Manuel Gunther, Terrance E. Boult
For applications such as airport border control, biometric technologies that can process many capture subjects quickly, efficiently, with weak supervision, and with minimal discomf…