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Reevaluating Adversarial Examples in Natural Language

This repository contains code and datasets for the experiments in "Reevaluating Adversarial Examples in Natural Language."

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Section 3

• section_3_case_study includes the datasets and survey results used for human studies of grammaticality, non-suspicion, and semantics of adversarial examples generated by Alzantot and TextFooler attacks. See section_3_case_study/<constraint>/*.ipynb for Jupyter notebooks containing analyses of results that correspond to Section 3 of the paper.
  • Semantics and non-suspicion studies are conducted using Amazon Mechanical Turk.
  • Grammaticality studies are conducted using the language_tool_python grammar checker.

Section 5

To generated adjusted exmaples using TFAdjusted's attack recipe, see section_6_adjusted_attacks/recipes/textfooler_2019_jin_adjusted.py (detailed under Section 6 header).

5.1 Adjusted Constraint Application

• section_5_tfadjusted/ contains the survey results for non-suspicion of adversarial examples generated using TFAdjusted.

5.2 Adversarial Training

• section_5_adv_training/ contains code for the adversarial training experiments in Section 5.2 of the paper.
  • ./Adversarial_Training contains code for training the models (train.py and datalaoder.py) and code for running a sweep over several random seeds for each augmentation recipe (no augmentation, TextFooler, and TfAdjusted). Hyperparameter sweeps are conducted using Weights and Biases.
  • ./Adversarial_Training_Robustness contains code for running textattack attacks on all models outputted during the adversarial training phase. The sweep.yaml file conducts a hyperparameter sweep using Weights and Biases that will run textattack attack on each of the trained models. Adversarial attacks are conducted using TextAttack.

Section 6

• section_6_adjusted_attacks/ contains code for running the adjusted Alzantot and TextFooler attacks as well as their survey results.
  • ./recipes contains the attack recipes for AlzantotAdjusted and TFAdjusted, which can be run using TextAttack,
  • ./surveys contains the results of Mechanical Turk semantics and non-suspicion surveys on the examples produced by the adjusted attack

Running the attacks with adjusted thresholds

Both attack recipes in section_6_adjusted_attacks/recipes/*.py are easily runnable by installing TextAttack and using its --attack-from-file parameter.

For example, to run AlzantotAdjusted on 5 samples from the MR datase against TextAttack's default bert-base-uncased model fine-tuned on MR, run the following command:

textattack attack --model bert-base-uncased-mr --attack-from-file section_6_adjusted_attacks/recipes/alzantot_2018_adjusted.py --num-examples 5

Or to run TFAdjusted on 5 examples on an LSTM fine-tuned for AGNews:

textattack attack --model lstm-ag-news --attack-from-file section_6_adjusted_attacks/recipes/textfooler_jin_2019_adjusted.py --num-examples 5

These attacks have been tested using TextAttack v0.2.10.

Appendix

• appx_1.2_finding_thresholds contain the results from the 'threshold-finding' experiments from the paper.
  • syn_thresh contains the synonyms used in the survey to find the threshold for word embedding cosine similarity in the counter-fitted embedding space. Human responses indicated that $0.9$ was the optimal threshold for counter-fitted embedding word cosine similarity.
  • se_thresh contains the code, generated data, and survey results for finding the USE Sentence Encoding similarity that best preserved semantics. For analysis of results, see se_thresh/results/se_threshold_analysis_results. Survey responses indicated that a USE cosine similarity threshold of $0.98$ preserved semantics without being overly restrictive.

Citation

• Title: Reevaluating Adversarial Examples in Natural Language
• Venue: 2020 EMNLP: 2020 Conference on Empirical Methods in Natural Language Processing Findings
• ArXiv Link: https://arxiv.org/abs/2004.14174
• Authors: John X. Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji, Yanjun Qi
• Abstract: State-of-the-art attacks on NLP models have different definitions of what constitutes a successful attack. These differences make the attacks difficult to compare. We propose to standardize definitions of natural language adversarial examples based on a set of linguistic constraints: semantics, grammaticality, edit distance, and non-suspicion. We categorize previous attacks based on these constraints. For each constraint, we suggest options for human and automatic evaluation methods. We use these methods to evaluate two state-of-the-art synonym substitution attacks. We find that perturbations often do not preserve semantics, and 45% introduce grammatical errors. Next, we conduct human studies to find a threshold for each evaluation method that aligns with human judgment. Human surveys reveal that to truly preserve semantics, we need to significantly increase the minimum cosine similarity between the embeddings of swapped words and sentence encodings of original and perturbed inputs. After tightening these constraints to agree with the judgment of our human annotators, the attacks produce valid, successful adversarial examples. But quality comes at a cost: attack success rate drops by over 70 percentage points. Finally, we introduce TextAttack, a library for adversarial attacks in NLP.

@article{morris2020reevaluating,
  title={Reevaluating Adversarial Examples in Natural Language},
  author={Morris, John X and Lifland, Eli and Lanchantin, Jack and Ji, Yangfeng and Qi, Yanjun},
  journal={arXiv preprint arXiv:2004.14174},
  year={2020}
}