David Wagner

Background Class Defense Against Adversarial Examples

2018

Adversarial examples allow crafted attacks against deep neural network classification of images. We propose a defense of expanding the training set with a single, large, and diverse class of background images, striving to `fill' around …

Demystifying the Adversarial Robustness of Random Transformation Defenses

2022 · arXiv (Cornell University)

Neural networks' lack of robustness against attacks raises concerns in security-sensitive settings such as autonomous vehicles. While many countermeasures may look promising, only a few withstand rigorous evaluation. Defenses using random transformations (RT) have shown …

DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection

2023 · arXiv (Cornell University)

We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes from the corresponding projects. Our new dataset contains 18,945 vulnerable …