Explainable AI for Inspecting Adversarial Attacks on Deep Neural Networks

Abstract

Deep Neural Networks (DNN) are state of the art algorithms for image classification. Although significant achievements and perspectives, deep neural networks and accompanying learning algorithms have some important challenges to tackle. However, it appears that it is relatively easy to attack and fool with well-designed input samples called adversarial examples. Adversarial perturbations are unnoticeable for humans. Such attacks are a severe threat to the development of these systems in critical applications, such as medical or military systems. Hence, it is necessary to develop methods of counteracting these attacks. These methods are called defense strategies and aim at increasing the neural model’s robustness against adversarial attacks. In this paper, we reviewed the recent findings in adversarial attacks and defense strategies. We also analyzed the effects of attacks and defense strategies applied, using the local and global analyzing methods from the family of explainable artificial intelligence.