Enhancing adversarial robustness of deep neural networks

• Main Author: Zhang, Jeffrey,M. Eng.Massachusetts Institute of Technology.
• Other Authors: Aleksander Madry.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2019
• Subjects: Electrical Engineering and Computer Science.
• Online Access: https://hdl.handle.net/1721.1/122994

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 57-58). === 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. === by Jeffry Zhang. === M. Eng. === M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science