Mitigating Attacks on Fake News Detection Systems using Genetic-Based Adversarial Training

Abstract

The study of adversarial effects on AI systems is not a new concept, but much of the research has been devoted to deep learning. In this paper we explore the effects of adversarial examples on 4 machine learning classifiers and measure the effectiveness of adversarial training. Additionally, we present a novel method for selecting adversarial training examples that lead to a more robust machine learning system. Our results suggest that adversarial examples can significantly hinder the classification performance and that adversarial training is an effective defensive counter-measure.