DAN: Distance-Based Anomaly Score for Textual Backdoor Defense

This is the official code of our EMNLP 2022 (findings) paper Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks.

Requirements

Python: 3.8.0

To install the dependencies, run

pip install -r requirements.txt

For the datasets used in our paper, please refer to the code of Embedding Poisoning.

For the posioned models, please obtain the poisoned weights following the intrsuction of the code of the attacking methods developed by previous researchers:

• RIPPLe (ACL 2020)
• Embedding Poisoning (also including data-free embedding poisoing and BadNet) (NAACL 2021)
• Layerwise Weight Poisoning (EMNLP 2021)
• NeuBA (ICML 2021 Workshop on Adversarial Machine Learning)
• BadPre (ICLR 2022)

Usage

For instance, there is a BERT model for SST-2 classification posioned by the embedding poisoing attack with a rare word trigger mb and the target class 1:

1. Feature Extraction

Run the following command:

python extract_embeddings.py --model_path ../Embedding-Poisoning/saved_models/sst-2/badnet_rw_mb_ls --test_data_path ./sentiment_data/sst-2/test.tsv --constructing_data_path ./sentiment_data/sst-2/dev.tsv   --output_dir ./log/embeddings/dan/sst-2/badnet_rw_mb_ls --batch_size 128 --backdoor_triggers mb --protect_label 1 --backdoor_trigger_type sentence

Notes: If you want to insert multiple trigger words, like mb and bb, concat them with a comma: --backdoor_triggers mb,bb; if you want to experiment on a posioned model embedded with a sentence trigger, just use --backdoor_trigger_type sentence and pass the trigger sentence string to --backdoor_triggers.

2. DAN Score Calculation and Evaluation

Run the following command:

python evaluate_dan.py --std --agg mean --score_ensemble  --input_dir ./log/embeddings/dan/sst-2/badnet_rw_mb_ls

Meaning of the arguments:

• score_ensemble: turn on the layer-wise score aggreation operation;
• std: turn on the normaliztion operation before aggreation;
• agg: the aggregation operator (mean or min)

Citation

If you find this repository to be useful for your research, please consider citing.

@inproceedings{chen-etal-2022-expose,
    title = "Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks",
    author = "Chen, Sishuo  and
      Yang, Wenkai  and
      Zhang, Zhiyuan  and
      Bi, Xiaohan  and
      Sun, Xu",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.findings-emnlp.47",
    pages = "668--683"
}

Acknowledgement

This repository relies on resources from Embedding-Poisoning, RAP, NeuBA, BadPre, and Huggingface Transformers. We thank the original authors for their open-sourcing.