no code implementations • 18 Jun 2021 • Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg, Christian Scharfenberger, Alexander Wong
Motivated by this, this study presents the concept of residual error, a new performance measure for not only assessing the adversarial robustness of a deep neural network at the individual sample level, but also can be used to differentiate between adversarial and non-adversarial examples to facilitate for adversarial example detection.
no code implementations • 1 Aug 2020 • Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg, Christian Scharfenberger, Alexander Wong
In this study, we investigate the effect of adversarial machine learning on the bias and variance of a trained deep neural network and analyze how adversarial perturbations can affect the generalization of a network.
1 code implementation • CVPR 2020 • Ahmadreza Jeddi, Mohammad Javad Shafiee, Michelle Karg, Christian Scharfenberger, Alexander Wong
In this study, we introduce Learn2Perturb, an end-to-end feature perturbation learning approach for improving the adversarial robustness of deep neural networks.
no code implementations • 16 Jan 2018 • Mohammad Javad Shafiee, Brendan Chwyl, Francis Li, Rongyan Chen, Michelle Karg, Christian Scharfenberger, Alexander Wong
The computational complexity of leveraging deep neural networks for extracting deep feature representations is a significant barrier to its widespread adoption, particularly for use in embedded devices.
no code implementations • 23 Mar 2015 • Robert Amelard, Christian Scharfenberger, Farnoud Kazemzadeh, Kaylen J. Pfisterer, Bill S. Lin, Alexander Wong, David A. Clausi
The results support the hypothesis that long-distance heart rate monitoring is feasible using transmittance PPGI, allowing for new possibilities of monitoring cardiovascular function in a non-contact manner.
no code implementations • CVPR 2013 • Christian Scharfenberger, Alexander Wong, Khalil Fergani, John S. Zelek, David A. Clausi
A novel statistical textural distinctiveness approach for robustly detecting salient regions in natural images is proposed.