This is an adversarial ML project where we create adversarial examples for tweets.

We have built a sentiment analyzer based on a DNN. The DNN has one hidden layer and two dropout layers. It performed with about 80% accuracy on the set-aside dataset.

The adversaries were generated using two methods based on references we read -

1. Synonyms for words which contribute most to the label
2. Insertion/Deletion/Modification of important words