Using intuition from empirical properties to simplify adversarial training defense

Document Type

Conference Proceeding

Publication Date

6-1-2019

Abstract

Due to the surprisingly good representation power of complex distributions, neural network (NN) classifiers are widely used in many tasks which include natural language processing, computer vision and cyber security. In recent works, people noticed the existence of adversarial examples. These adversarial examples break the NN classifiers' underlying assumption that the environment is attack free and can easily mislead fully trained NN classifier without noticeable changes. Among defensive methods, adversarial training is a popular choice. However, original adversarial training with single-step adversarial examples (Single-Adv) can not defend against iterative adversarial examples. Although adversarial training with iterative adversarial examples (Iter-Adv) can defend against iterative adversarial examples, it consumes too much computational power and hence is not scalable. In this paper, we analyze Iter-Adv techniques and identify two of their empirical properties. Based on these properties, we propose modifications which enhance Single-Adv to perform competitively as Iter-Adv. Through preliminary evaluation, we show that the proposed method enhances the test accuracy of state-of-the-art (SOTA) Single-Adv defensive method against iterative adversarial examples by up to 16.93% while reducing its training cost by 28.75%.

Identifier

85072024454 (Scopus)

ISBN

[9781728130309]

Publication Title

Proceedings 49th Annual IEEE IFIP International Conference on Dependable Systems and Networks Workshop Dsn W 2019

External Full Text Location

https://doi.org/10.1109/DSN-W.2019.00020

First Page

58

Last Page

61

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