Understanding Catastrophic Overfitting in Single-step Adversarial Training

• URL: http://arxiv.org/abs/2010.01799v2
• Date: Tue, 15 Dec 2020 08:41:08 GMT
• Title: Understanding Catastrophic Overfitting in Single-step Adversarial Training
• Authors: Hoki Kim, Woojin Lee, Jaewook Lee
• Abstract summary: "catastrophic overfitting" is a phenomenon in which the robust accuracy against projected gradient descent suddenly decreases to 0% after a few epochs. We propose a simple method that not only prevents catastrophic overfitting, but also overrides the belief that it is difficult to prevent multi-step adversarial attacks with single-step adversarial training.
• Score: 9.560980936110234
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Although fast adversarial training has demonstrated both robustness and efficiency, the problem of "catastrophic overfitting" has been observed. This is a phenomenon in which, during single-step adversarial training, the robust accuracy against projected gradient descent (PGD) suddenly decreases to 0% after a few epochs, whereas the robust accuracy against fast gradient sign method (FGSM) increases to 100%. In this paper, we demonstrate that catastrophic overfitting is very closely related to the characteristic of single-step adversarial training which uses only adversarial examples with the maximum perturbation, and not all adversarial examples in the adversarial direction, which leads to decision boundary distortion and a highly curved loss surface. Based on this observation, we propose a simple method that not only prevents catastrophic overfitting, but also overrides the belief that it is difficult to prevent multi-step adversarial attacks with single-step adversarial training.

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