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Implementation code for the paper "Generating Natural Language Adversarial Examples"
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  1. Download the Imdb dataset
  1. Download the glove vector embeddings (used by the model)
  1. Download the counter-fitted vectors (used by our attack)
  1. Build the vocabulary and embeddings matrix.

That will take like a minute, and it will tokenize the dataset and save it to a pickle file. It will also compute some auxiliary files like the matrix of the vector embeddings for words in our dictionary. All files will be saved under aux_files directory created by this script.

  1. Train the sentiment analysis model.

6)Download the Google language model.

  1. Pre-compute the distances between embeddings of different words (required to do the attack) and save the distance matrix.

  1. Now, we are ready to try some attacks ! You can do so by running the IMDB_AttackDemo.ipynb jupyter notebook !
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