Enhancing adversarial robustness of deep neural networks
Author(s)Zhang, Jeffrey,M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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Logit-based regularization and pretrain-then-tune are two approaches that have recently been shown to enhance adversarial robustness of machine learning models. In the realm of regularization, Zhang et al. (2019) proposed TRADES, a logit-based regularization optimization function that has been shown to improve upon the robust optimization framework developed by Madry et al. (2018) [14, 9]. They were able to achieve state-of-the-art adversarial accuracy on CIFAR10. In the realm of pretrain- then-tune models, Hendrycks el al. (2019) demonstrated that adversarially pretraining a model on ImageNet then adversarially tuning on CIFAR10 greatly improves the adversarial robustness of machine learning models. In this work, we propose Adversarial Regularization, another logit-based regularization optimization framework that surpasses TRADES in adversarial generalization. Furthermore, we explore the impact of trying different types of adversarial training on the pretrain-then-tune paradigm.
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, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 57-58).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.