# Using Intuition from Empirical Properties to Simplify Adversarial Training Defense

## TL;DR

This paper analyzes empirical properties of adversarial training methods and proposes modifications to improve single-step adversarial training, making it more effective and computationally efficient against iterative adversarial attacks.

## Contribution

The paper identifies empirical properties of iterative adversarial training and introduces modifications to single-step training to enhance its robustness and efficiency.

## Key findings

- Enhanced test accuracy against iterative adversarial examples by up to 16.93%.
- Reduced training cost of the defensive method by 28.75%.
- Proposed method performs competitively with state-of-the-art iterative adversarial training.

## 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%.