Methods for Defending Deep Neural Networks Against Adversarial Attacks

Abstract

In this paper, Gradient-based adversarial attack methods on classifi cation neural networks for image processing are discussed. And also the archeitecture of neural network for defense against adversarial attacks is proposed. For the proposed neural network architecture, the dependence of the preprocessing quality of the attacked images on the training time and the number of trained parameters has been studied.