Improved methodology for evaluating adversarial robustness in deep neural networks

Deep neural networks are known to be vulnerable to adversarial perturbations, which are often imperceptible to humans but can alter predictions of machine learning systems. Since the exact value of adversarial robustness is difficult to obtain for complex deep neural networks, accuracy of the models against perturbed examples generated by attack methods is empirically used as a proxy to adversarial robustness. However, failure of attack methods to find adversarial perturbations cannot be equated with being robust.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Lee, Kyungmi,(Computer scientist)Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Anantha P. Chandrakasan.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127350

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127350