Effects of Adversarial Training on the Safety of Classification Models

Abstract

Artificial intelligence (AI) is one of the most important topics that implements symmetry in computer science. As like humans, most AI also learns by trial-and-error approach which requires appropriate adversarial examples. In this study, we prove that adversarial training can be useful to verify the safety of classification model in early stage of development. We experimented with various amount of adversarial data and found that the safety can be significantly improved by appropriate ratio of adversarial training.