Local approximations of deep learning models for black-box adversarial attacks
Name
1102057729-MIT.pdf
Size
1.36 MB
Format
Adobe PDF
Checksum (MD5)
a61ef41543150e02128e1cd9513abbad
Author(s)
Sun, Michael(Michael Z.)
Advisor(s)
Aleksander Madry.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
We study the problem of generating adversarial examples for image classifiers in the black-box setting (when the model is available only as an oracle). We unify two seemingly orthogonal and concurrent lines of work in black-box adversarial generation: query-based attacks and substitute models. In particular, we reinterpret adversarial transferability as a strong gradient prior. Based on this unification, we develop a method for integrating model-based priors into the generation of black-box attacks. The resulting algorithms significantly improve upon the current state-of-the-art in black-box adversarial attacks across a wide range of threat models.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 45-47).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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