  Legal judgment prediction is the task of predicting the outcome of court
cases on a given text description of facts of cases. These tasks apply Natural
Language Processing (NLP) techniques to predict legal judgment results based on
facts. Recently, large-scale public datasets and NLP models have increased
research in areas related to legal judgment prediction systems. For such
systems to be practically helpful, they should be robust from adversarial
attacks. Previous works mainly focus on making a neural legal judgement system;
however, significantly less or no attention has been given to creating a robust
Legal Judgement Prediction(LJP) system. We implemented adversarial attacks on
early existing LJP systems and found that none of them could handle attacks. In
this work, we proposed an approach for making robust LJP systems. Extensive
experiments on three legal datasets show significant improvements in our
approach over the state-of-the-art LJP system in handling adversarial attacks.
To the best of our knowledge, we are the first to increase the robustness of
early-existing LJP systems.
