Adversarial attacks and defenses for generative models

Adversarial Machine learning is a field of research lying at the intersection of Machine Learning and Security, which studies vulnerabilities of Machine learning models that make them susceptible to attacks. The attacks are inflicted by carefully designing a perturbed input which appears benign, but fools the models to perform in unexpected ways. To date, most work in adversarial attacks and defenses has been done for classification models. However, generative models are susceptible to attacks as well, and thus warrant attention. We study some attacks for generative models like Autoencoders and Variational Autoencoders. We discuss the relative effectiveness of the attack methods, and explore some simple defense methods against the attacks.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Agarwal, Rishika

Contributors dc:contributor

• Koyejo, Sanmi
• Li, Bo

• Adversarial Machine learning, generative models, attacks, defenses

Statement dc:rights

• Copyright 2019 Rishika Agarwal

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/104943

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/104943