Certified Defenses for Adversarial Patches
OPEN ACCESS
Loading...
Author / Producer
Date
2020-04
Publication Type
Conference Paper
ETH Bibliography
no
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems. This paper studies certified and empirical defenses against patch attacks. We begin with a set of experiments showing that most existing defenses, which work by pre-processing input images to mitigate adversarial patches, are easily broken by simple white-box adversaries. Motivated by this finding, we propose the first certified defense against patch attacks, and propose faster methods for its training. Furthermore, we experiment with different patch shapes for testing, obtaining surprisingly good robustness transfer across shapes, and present preliminary results on certified defense against sparse attacks. Our complete implementation can be found on: https://github.com/Ping-C/certifiedpatchdefense.
Permanent link
Publication status
published
Editor
Book title
Journal / series
Volume
Pages / Article No.
Publisher
International Conference on Learning Representations
Event
8th International Conference on Learning Representations (ICLR 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Adversarial; Computer vision; Robustness
Organisational unit
09695 - Studer, Christoph / Studer, Christoph
Notes
Due to the Corona virus (COVID-19) the conference was conducted virtually.