SURVEY OF ADVERSARIAL ATTACKS AND DEFENSE AGAINST ADVERSARIAL ATTACKS

Abstract

In recent years, the fields of Artificial Intelligence (AI) and Deep learning (DL) techniques along with Neural Networks (NNs) have shown great progress and scope for future research. Along with all the developments comes the threats and security vulnerabilities to Neural Networks and AI models. A few fabricated inputs/samples can lead to deviations in the results of the models. Patch based Adversarial Attacks can change the output of a neural network to a completely different result just by making a few changes to the input of the neural network. These attacks employ a patch that is applied to the input image in order to cause the classifier to misclassify and make the incorrect prediction. The goal of this research is to develop effective defense strategies against these types of attacks and make the model/Neural Network more robust.