Title
Certified Adversarial Robustness with Additive Noise
Abstract
The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm. Although a significant body of work on developing defensive models has been considered, most such models are heuristic and are often vulnerable to adaptive attacks. Defensive methods that provide theoretical robustness guarantees have been studied intensively, yet most fail to obtain non-trivial robustness when a large-scale model and data are present. To address these limitations, we introduce a framework that is scalable and provides certified bounds on the norm of the input manipulation for constructing adversarial examples. We establish a connection between robustness against adversarial perturbation and additive random noise, and propose a training strategy that can significantly improve the certified bounds. Our evaluation on MNIST, CIFAR-10 and ImageNet suggests that the proposed method is scalable to complicated models and large data sets, while providing competitive robustness to state-of-the-art provable defense methods.
Year
Venue
Keywords
2019
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019)
learning community,scale model
Field
DocType
Volume
Computer science,Robustness (computer science),Artificial intelligence,Certification,Machine learning,Adversarial system
Conference
32
ISSN
Citations 
PageRank 
1049-5258
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Bai Li1102.82
Changyou Chen236536.95
Wenlin Wang3517.06
L. Carin44603339.36