AN ADVERSARIAL ROBUSTNESS ANALYSIS FRAMEWORK FOR CLASSIFICATION MODEL

Abstract

In the ML/DL algorithms developed by researchers/developers, the only constraint faced by them is the robustness of the model. The model may be experimented with certain data sets but for the model to get tested with all the permutation and combination of data sets under different conditions is a challenge. For the model to give efficient and accurate results under all circumstances, the users are demanding a more robust model that has been analyzed thoroughly. We aim to create an algorithm that is unique, efficient and solves a real-world problem and build an Adversarial Robustness analysis framework that works on reinforcement learning, applies utility metrics of attacks and defense to give a comprehensive robustness report