Threats of Adversarial Attacks on Deep Learning

Abstract

AI, accompanied with Deep Learning, is a growing technological innovation which has an extensible reach to various applications. The complex computational models i.e. Deep Neural Networks (DNNs) excel in natural language processing, image recognition, computer vision, and autonomous systems. Despite the merits, these intricate networks are vulnerable to adversarial attacks (such as black-box and white-box) which can be the primary threats to the AI-DL integration. For their mitigation, adversarial training, defense distillations and other defenses are introduced. This paper deals with the impediments posed by adversarial attacks on Deep Learning, and lists out the possible solutions to reduce their impacts.