skip to main content

Search for: All records

Creators/Authors contains: "Humayun, A.I"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Deep Neural Networks (DNNs) trained for classification tasks are vulnerable to adversarial attacks. But not all the classes are equally vulnerable. Adversarial training does not make all classes or groups equally robust as well. For example, in classification tasks with long-tailed distributions, classes are asymmetrically affected during adversarial training, with lower robust accuracy for less frequent classes. In this regard, we propose a provable robustness method by leveraging the continuous piecewise-affine (CPA) nature of DNNs. Our method can impose linearity constraints on the decision boundary, as well as the DNN CPA partition, without requiring any adversarial training. Using such constraints, we show that the margin between the decision boundary and minority classes can be increased in a provable manner. We also present qualitative and quantitative validation of our method for class-specific robustness. Our code is available at https: // 
    more » « less
    Free, publicly-accessible full text available July 28, 2024