Xiaowei Huang

Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm

2018 · arXiv (Cornell University)

Deployment of deep neural networks (DNNs) in safety- or security-critical systems requires provable guarantees on their correct behaviour. A common requirement is robustness to adversarial perturbations in a neighbourhood around an input. In this paper …

Safety and Trustworthiness of Deep Neural Networks: A Survey

2018 · arXiv (Cornell University)

In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level intelligence on several long-standing tasks. With broader deployment of DNNs on various applications, the concerns on its …

Safety Verification of Deep Neural Networks

2016 · arXiv (Cornell University)

Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify …