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Exploring Semantic Perturbations on Grover

Ziqing Ji, Pranav Kulkarni, Marko Neskovic, Kevin Nolan, Yan Xu

With news and information being as easy to access as they currently are, it is more important than ever to ensure that people are not mislead by what they read. Recently, the rise of neural fake news (AI-generated fake news) and its demonstrated effectiveness at fooling humans has prompted the development of models to detect it. One such model is the Grover model, which can both detect neural fake news to prevent it and generate it to demonstrate how a model could be misused to fool human readers. In this work we explore the Grover model's fake news detection capabilities by performing targeted attacks through perturbations on input news articles. Through this we expose some potential vulnerabilities with the Grover model, which should be addressed in further iterations to ensure it can detect all types of fake news accurately.

Setting Up Grover

We have included detailed setup instructions for Grover here.

We have also included steps to step up Grover on Google cloud TPUs here.

Changes to Grover

Using Grover directly from Zeller's repository will not yield accurate results. We've observed multiple inconsistencies and issues in their code on Github. As a result of this, we are providing two versions of Grover in our repository:

  • TensorFlow 1.13.1 Grover (Fixed)

    Located under ./tf1_grover, we have provided a fixed version of the run_discriminiation.py file. Some of the notable fixes include fixing the article vs text dicrepancy, which led to the discriminator not using article content for the discrimination process.

  • TensorFlow 2.5.1 Grover (Preliminary)

    Located under ./tf2_grover, we have provided a preliminary version of Grover updated to TensorFlow 2. The primary purpose of this is to enable access to gradients in the embedding table for informed perturbations. However, this opens up the opportunity for other researchers to continue working on updating Grover to TensorFlow 2.

Model Selection

We are using the medium generator, medium discriminator, p=0.96 model for testing purposes. It is the right balance between accuracy and computational requirements. Downloading from Google cloud storage is a bit finicky and inconsistent sometimes, so our purposes, we uploaded the model to Google drive for easy access.

Once the model is downloaded, place it under models/medium. We will also use the lm/config/large.json configuration so our code is consistent with the number of tensors in each hidden layer (768 in base vs 1024 in medium)

There's an inconsistency in naming convention since the repository was published. Since 2019, it appears large models were renamed to medium.

To make it clear: when we say large, it is equivalent to saying medium.

Generating Articles

We can prepare data such that it gives the generator context. The April 2019 sample included in Zeller's repository is a good starting point for generating articles.

We can generate fake articles using the command:

PYTHONPATH=$(pwd) python sample/contextual_generate.py -model_config_fn lm/configs/large.json -model_ckpt models/medium/model.ckpt-1562 -metadata_fn sample/april2019_set_mini.jsonl -out_fn april2019_set_mini_out.jsonl -top_p 0.96

This will use the chosen model with the correct configuration file for the network structure.

This does take a while to run because the medium model is pretty good. It creates weird articles but it's surprisingly coherent!

Training

Preparing Data

A few things to keep in mind:

  • It doesn't support multiple authors
  • Make sure all training articles have flag "split": "train" in the jsonl file.
  • Make sure all labels are set correctly "label": "human" or "label": "machine"

I've created two additional datasets from the realnews_tiny.jsonl dataset: one for training and one more testing!

Training the Model

This command will start the training process.

PYTHONPATH=$(pwd) python discrimination/run_discrimination.py -config_file lm/configs/large.json -input_data realnews/realnews_tiny-train.jsonl -output_dir models/test -do_train True -batch_size 5 -num_train_epochs 1.0

The trained model will be stored under models/test. Make sure to delete this directory if you are retraining the model for the same output directory. This will create a small scale generator and create checkpoints for the discriminator.

Testing

Preparing Data

A few things to keep in mind:

  • Apparently it doesn't support multiple authors TO-DO
  • Make sure all testing articles have flag "split": "test" in the jsonl file.
  • Make sure all labels are set correctly "label": "human" or "label": "machine"

Testing the Model

This command will start the testing process.

PYTHONPATH=$(pwd) python discrimination/run_discrimination.py -config_file lm/configs/large.json -input_data realnews/realnews_tiny-test.jsonl -output_dir models/medium -batch_size 5 -predict_test

Make sure to include the -predict_test flag! Otherwise, the model will not actually test anything. After running the discrimination, the model will generate a few files: test.tf_record and test-probs.npy.

We only care about test-probs.npy. This contains the internal probabilities of each of our two labels before clasification, stored as a numpy file. You can load the file in python using:

import numpy as np
probs = np.load('models/medium/test-probs.npy')

This will assign the variable probs an array of shape (n,2) where n is the number of testing articles.

We've also modifed the run_discrimination.py file to output the predicted and actual labels before calculating the accuracy for sanity's sake. Keep in mind, for the model, the labels are assigned numerical classes. The label machine is class 0 and label human is class 1.

Toolbox

Under ./toolbox, we have included all the scripts used in our research process to manipulate data, extract model weights, and graph results.

Results

All deliverables for our results are located under ./informed and ./uninformed. This includes all scripts used, data files generated, probabilities, and graphs.