Overview on Adversarial Attacks and Defenses in Deep Learning Systems

Abstract

In "machine learning and deep neural networks", there is a significant surge in interest in adversarial assaults and answers due to the quickly growing Internet usage of deep learning and related scenarios. The use of deep neural networks in models for classification is highlighted in this article, which provides an extensive overview of the most recent advancements in "adversarial attack and defense tactics". People who manipulate the deep learning model are prone to making mistakes that lead to incorrect predictions and inappropriate actions. Learning the specified approach is crucial to keeping the machine in good working order and preventing it from being disrupted by hostile attack users. Through meticulous testing and data analysis, the study has produced verifiable evidence to support the ongoing discussion in the area of deep learning security.